U.S. patent application number 17/537865 was filed with the patent office on 2022-03-24 for moving data between tiers in a multi-tiered, cloud-based storage system.
The applicant listed for this patent is PURE STORAGE, INC.. Invention is credited to JOSHUA FREILICH, RONALD KARR, ASWIN KARUMBUNATHAN, NAVEEN NEELAKANTAM.
Application Number | 20220091771 17/537865 |
Document ID | / |
Family ID | |
Filed Date | 2022-03-24 |
United States Patent
Application |
20220091771 |
Kind Code |
A1 |
FREILICH; JOSHUA ; et
al. |
March 24, 2022 |
Moving Data Between Tiers In A Multi-Tiered, Cloud-Based Storage
System
Abstract
Staging data in a cloud-based storage system, including:
receiving, by a storage controller application executing on cloud
computing resources in a cloud-based storage system, a data storage
operation from a computer device, wherein the cloud-based storage
system includes a first tier of cloud storage and a second tier of
cloud storage; storing data corresponding to the data storage
operation within the first tier of cloud storage provided using a
first cloud storage service; and responsive to detecting a
condition for transferring data between the first tier of cloud
storage and the second tier of cloud storage, transferring the data
in the first tier of cloud storage to a second tier of cloud
storage provided using a second cloud storage service, wherein the
first cloud storage service is different than the second cloud
storage service.
Inventors: |
FREILICH; JOSHUA; (SAN
FRANCISCO, CA) ; KARUMBUNATHAN; ASWIN; (SAN
FRANCISCO, CA) ; NEELAKANTAM; NAVEEN; (MOUNTAIN VIEW,
CA) ; KARR; RONALD; (PALO ALTO, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
PURE STORAGE, INC. |
Mountain View |
CA |
US |
|
|
Appl. No.: |
17/537865 |
Filed: |
November 30, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16524861 |
Jul 29, 2019 |
11210009 |
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17537865 |
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16353775 |
Mar 14, 2019 |
10976962 |
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16524861 |
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62878877 |
Jul 26, 2019 |
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62875947 |
Jul 18, 2019 |
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62769277 |
Nov 19, 2018 |
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62768952 |
Nov 18, 2018 |
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62692602 |
Jun 29, 2018 |
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62643641 |
Mar 15, 2018 |
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International
Class: |
G06F 3/06 20060101
G06F003/06 |
Claims
1. A method comprising: receiving, by a storage controller
application executing on cloud computing resources in a cloud-based
storage system, a data storage operation from a computer device,
wherein the cloud-based storage system includes a first tier of
cloud storage and a second tier of cloud storage; storing data
corresponding to the data storage operation within the first tier
of cloud storage provided using a first cloud storage service; and
responsive to detecting a condition for transferring data between
the first tier of cloud storage and the second tier of cloud
storage, transferring the data in the first tier of cloud storage
to a second tier of cloud storage provided using a second cloud
storage service, wherein the first cloud storage service is
different than the second cloud storage service.
2. The method of claim 1 wherein the data stored using the first
cloud storage service is in a first format and the data stored
using the second cloud storage service is in a second format.
3. The method of claim 1 wherein the first tier of cloud storage
includes cloud-based block storage resources and the second tier of
cloud storage includes cloud-based object storage resources.
4. The method of claim 1 wherein the first tier of cloud storage
includes cloud-based block storage resources offered by a first
cloud block storage service and the second tier of cloud storage
includes cloud-based block storage resources offered by a second
cloud block storage service.
5. The method of claim 1 further comprising: determining a data
storage optimization that is applicable to one or more portions of
stored data within the first tier of cloud storage; and modifying
the one or more portions of stored data within the first tier of
cloud storage to generate modified data.
6. The method of claim 5 further comprising storing, after
modifying the one or more portions of data, the modified data
within the first tier of cloud storage.
7. The method of claim 5 further comprising storing, after
modifying the one or more portions of data, the modified data
within the second tier of cloud storage.
8. The method of claim 5 wherein the data storage optimization is
one or more of: data compression, data deduplication, or garbage
collection.
9. A cloud-based storage system including: a first tier of cloud
storage provided using a first cloud storage service; a second tier
of cloud storage provided using a second cloud storage service; and
one or more storage controller applications, each storage
controller application executing in a cloud computing instance,
wherein the one or more storage controllers are configured for:
receiving a data storage operation from a computer device; storing
data corresponding to the data storage operation within the first
tier of cloud storage provided using the first cloud storage
service; and responsive to detecting a condition for transferring
data between the first tier of cloud storage and the second tier of
cloud storage, transferring the data in the first tier of cloud
storage to a second tier of cloud storage provided using a second
cloud storage service, wherein the first cloud storage service is
different than the second cloud storage service.
10. The cloud-based storage system of claim 9 wherein the data
stored using the first cloud storage service is in a first format
and the data stored using the second cloud storage service is in a
second format.
11. The cloud-based storage system of claim 9 wherein the first
tier of cloud storage includes cloud-based block storage resources
and the second tier of cloud storage includes cloud-based object
storage resources.
12. The cloud-based storage system of claim 9 wherein the first
tier of cloud storage includes cloud-based block storage resources
offered by a first cloud block storage service and the second tier
of cloud storage includes cloud-based block storage resources
offered by a second cloud block storage service.
13. The method of claim 9 wherein the one or more storage
controllers are further configured for: determining a data storage
optimization that is applicable to one or more portions of stored
data within the first tier of cloud storage; and modifying the one
or more portions of stored data within the first tier of cloud
storage to generate modified data.
14. The method of claim 13 wherein the one or more storage
controllers are further configured for storing, after modifying the
one or more portions of data, the modified data within the first
tier of cloud storage.
15. The method of claim 13 wherein the one or more storage
controllers are further configured for storing, after modifying the
one or more portions of data, the modified data within the second
tier of cloud storage.
16. The method of claim 13 wherein the data storage optimization is
one or more of: data compression, data deduplication, or garbage
collection.
17. A computer program product disposed on a non-transitory
computer readable medium, the computer program product including
computer program instructions that, when executed, carry out the
steps of: receiving, by a storage controller application executing
on cloud computing resources in a cloud-based storage system, a
data storage operation from a computer device, wherein the
cloud-based storage system includes a first tier of cloud storage
and a second tier of cloud storage; storing data corresponding to
the data storage operation within the first tier of cloud storage
provided using a first cloud storage service; and responsive to
detecting a condition for transferring data between the first tier
of cloud storage and the second tier of cloud storage, transferring
the data in the first tier of cloud storage to a second tier of
cloud storage provided using a second cloud storage service,
wherein the first cloud storage service is different than the
second cloud storage service.
18. The computer program product of claim 17 further comprising
computer program instructions that, when executed, carry out the
steps of: determining a data storage optimization that is
applicable to one or more portions of stored data within the first
tier of cloud storage; and modifying the one or more portions of
stored data within the first tier of cloud storage to generate
modified data.
19. The computer program product of claim 17 further comprising
computer program instructions that, when executed, carry out the
step of storing, after modifying the one or more portions of data,
the modified data within the first tier of cloud storage.
20. The computer program product of claim 17 further comprising
computer program instructions that, when executed, carry out the
step of storing, after modifying the one or more portions of data,
the modified data within the second tier of cloud storage.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This is a continuation application for patent entitled to a
filing date and claiming the benefit of earlier-filed U.S. patent
application Ser. No. 16/524,861, filed Jul. 29, 2019, herein
incorporated by reference in its entirety.
BRIEF DESCRIPTION OF DRAWINGS
[0002] FIG. 1A illustrates a first example system for data storage
in accordance with some implementations.
[0003] FIG. 1B illustrates a second example system for data storage
in accordance with some implementations.
[0004] FIG. 1C illustrates a third example system for data storage
in accordance with some implementations.
[0005] FIG. 1D illustrates a fourth example system for data storage
in accordance with some implementations.
[0006] FIG. 2A is a perspective view of a storage cluster with
multiple storage nodes and internal storage coupled to each storage
node to provide network attached storage, in accordance with some
embodiments.
[0007] FIG. 2B is a block diagram showing an interconnect switch
coupling multiple storage nodes in accordance with some
embodiments.
[0008] FIG. 2C is a multiple level block diagram, showing contents
of a storage node and contents of one of the non-volatile solid
state storage units in accordance with some embodiments.
[0009] FIG. 2D shows a storage server environment, which uses
embodiments of the storage nodes and storage units of some previous
figures in accordance with some embodiments.
[0010] FIG. 2E is a blade hardware block diagram, showing a control
plane, compute and storage planes, and authorities interacting with
underlying physical resources, in accordance with some
embodiments.
[0011] FIG. 2F depicts elasticity software layers in blades of a
storage cluster, in accordance with some embodiments.
[0012] FIG. 2G depicts authorities and storage resources in blades
of a storage cluster, in accordance with some embodiments.
[0013] FIG. 3A sets forth a diagram of a storage system that is
coupled for data communications with a cloud services provider in
accordance with some embodiments of the present disclosure.
[0014] FIG. 3B sets forth a diagram of a storage system in
accordance with some embodiments of the present disclosure.
[0015] FIG. 3C sets forth a diagram of a storage system in
accordance with some embodiments of the present disclosure.
[0016] FIG. 3D sets forth a diagram of a storage system in
accordance with some embodiments of the present disclosure.
[0017] FIG. 4 sets forth a block diagram illustrating a plurality
of storage systems that support a pod according to some embodiments
of the present disclosure.
[0018] FIG. 5 sets forth a flow chart illustrating an example
method of establishing a synchronous replication relationship
between two or more storage systems according to some embodiments
of the present disclosure.
[0019] FIG. 6 sets forth a flow chart illustrating an example
method of establishing a synchronous replication relationship
between two or more storage systems according to some embodiments
of the present disclosure.
[0020] FIG. 7 sets forth a flow chart illustrating an example
method of establishing a synchronous replication relationship
between two or more storage systems according to some embodiments
of the present disclosure.
[0021] FIG. 8 sets forth a block diagram illustrating a plurality
of storage systems that support a pod according to some embodiments
of the present disclosure.
[0022] FIG. 9 sets forth an example of a cloud-based storage system
in accordance with some embodiments of the present disclosure.
[0023] FIG. 10 sets forth a flow chart illustrating an example
method of servicing I/O operations in a cloud-based storage
system.
[0024] FIG. 11 sets forth a flow chart illustrating an example
method of servicing I/O operations in a cloud-based storage
system.
[0025] FIG. 12 sets forth a flow chart illustrating an example
method of servicing I/O operations in a cloud-based storage
system.
[0026] FIG. 13 sets forth a flow chart illustrating an additional
example method of servicing I/O operations in a cloud-based storage
system.
[0027] FIG. 14 sets forth a flow chart illustrating an example
method for staging data in a cloud-based storage system according
to some embodiments of the present disclosure.
[0028] FIG. 15 sets forth a flow chart illustrating an example
method for staging data in a cloud-based storage system according
to some embodiments of the present disclosure.
[0029] FIG. 16 sets forth a flow chart illustrating an example
method for staging data in a cloud-based storage system according
to some embodiments of the present disclosure.
[0030] FIG. 17 sets forth a flow chart illustrating an example
method for staging data in a cloud-based storage system according
to some embodiments of the present disclosure.
DESCRIPTION OF EMBODIMENTS
[0031] Example methods, apparatus, and products for staging data in
a cloud-based storage system in accordance with embodiments of the
present disclosure are described with reference to the accompanying
drawings, beginning with FIG. 1A. FIG. 1A illustrates an example
system for data storage, in accordance with some implementations.
System 100 (also referred to as "storage system" herein) includes
numerous elements for purposes of illustration rather than
limitation. It may be noted that system 100 may include the same,
more, or fewer elements configured in the same or different manner
in other implementations.
[0032] System 100 includes a number of computing devices 164A-B.
Computing devices (also referred to as "client devices" herein) may
be embodied, for example, a server in a data center, a workstation,
a personal computer, a notebook, or the like. Computing devices
164A-B may be coupled for data communications to one or more
storage arrays 102A-B through a storage area network (`SAN`) 158 or
a local area network (`LAN`) 160.
[0033] The SAN 158 may be implemented with a variety of data
communications fabrics, devices, and protocols. For example, the
fabrics for SAN 158 may include Fibre Channel, Ethernet,
InfiniBand, Serial Attached Small Computer System Interface
(`SAS`), or the like. Data communications protocols for use with
SAN 158 may include Advanced Technology Attachment (`ATA`), Fibre
Channel Protocol, Small Computer System Interface (`SCSI`),
Internet Small Computer System Interface (`iSCSI`), HyperSCSI,
Non-Volatile Memory Express (`NVMe`) over Fabrics, or the like. It
may be noted that SAN 158 is provided for illustration, rather than
limitation. Other data communication couplings may be implemented
between computing devices 164A-B and storage arrays 102A-B.
[0034] The LAN 160 may also be implemented with a variety of
fabrics, devices, and protocols. For example, the fabrics for LAN
160 may include Ethernet (802.3), wireless (802.11), or the like.
Data communication protocols for use in LAN 160 may include
Transmission Control Protocol (`TCP`), User Datagram Protocol
(`UDP`), Internet Protocol (`IP`), HyperText Transfer Protocol
(`HTTP`), Wireless Access Protocol (`WAP`), Handheld Device
Transport Protocol (`HDTP`), Session Initiation Protocol (SIT),
Real Time Protocol (`RTP`), or the like.
[0035] Storage arrays 102A-B may provide persistent data storage
for the computing devices 164A-B. Storage array 102A may be
contained in a chassis (not shown), and storage array 102B may be
contained in another chassis (not shown), in implementations.
Storage array 102A and 102B may include one or more storage array
controllers 110A-D (also referred to as "controller" herein). A
storage array controller 110A-D may be embodied as a module of
automated computing machinery comprising computer hardware,
computer software, or a combination of computer hardware and
software. In some implementations, the storage array controllers
110A-D may be configured to carry out various storage tasks.
Storage tasks may include writing data received from the computing
devices 164A-B to storage array 102A-B, erasing data from storage
array 102A-B, retrieving data from storage array 102A-B and
providing data to computing devices 164A-B, monitoring and
reporting of disk utilization and performance, performing
redundancy operations, such as Redundant Array of Independent
Drives (`RAID`) or RAID-like data redundancy operations,
compressing data, encrypting data, and so forth.
[0036] Storage array controller 110A-D may be implemented in a
variety of ways, including as a Field Programmable Gate Array
(`FPGA`), a Programmable Logic Chip (`PLC`), an Application
Specific Integrated Circuit (`ASIC`), System-on-Chip (`SOC`), or
any computing device that includes discrete components such as a
processing device, central processing unit, computer memory, or
various adapters. Storage array controller 110A-D may include, for
example, a data communications adapter configured to support
communications via the SAN 158 or LAN 160. In some implementations,
storage array controller 110A-D may be independently coupled to the
LAN 160. In implementations, storage array controller 110A-D may
include an I/O controller or the like that couples the storage
array controller 110A-D for data communications, through a midplane
(not shown), to a persistent storage resource 170A-B (also referred
to as a "storage resource" herein). The persistent storage resource
170A-B main include any number of storage drives 171A-F (also
referred to as "storage devices" herein) and any number of
non-volatile Random Access Memory (`NVRAM`) devices (not
shown).
[0037] In some implementations, the NVRAM devices of a persistent
storage resource 170A-B may be configured to receive, from the
storage array controller 110A-D, data to be stored in the storage
drives 171A-F. In some examples, the data may originate from
computing devices 164A-B. In some examples, writing data to the
NVRAM device may be carried out more quickly than directly writing
data to the storage drive 171A-F. In implementations, the storage
array controller 110A-D may be configured to utilize the NVRAM
devices as a quickly accessible buffer for data destined to be
written to the storage drives 171A-F. Latency for write requests
using NVRAM devices as a buffer may be improved relative to a
system in which a storage array controller 110A-D writes data
directly to the storage drives 171A-F. In some implementations, the
NVRAM devices may be implemented with computer memory in the form
of high bandwidth, low latency RAM. The NVRAM device is referred to
as "non-volatile" because the NVRAM device may receive or include a
unique power source that maintains the state of the RAM after main
power loss to the NVRAM device. Such a power source may be a
battery, one or more capacitors, or the like. In response to a
power loss, the NVRAM device may be configured to write the
contents of the RAM to a persistent storage, such as the storage
drives 171A-F.
[0038] In implementations, storage drive 171A-F may refer to any
device configured to record data persistently, where "persistently"
or "persistent" refers as to a device's ability to maintain
recorded data after loss of power. In some implementations, storage
drive 171A-F may correspond to non-disk storage media. For example,
the storage drive 171A-F may be one or more solid-state drives
(`SSDs`), flash memory based storage, any type of solid-state
non-volatile memory, or any other type of non-mechanical storage
device. In other implementations, storage drive 171A-F may include
mechanical or spinning hard disk, such as hard-disk drives
(`HDD`).
[0039] In some implementations, the storage array controllers
110A-D may be configured for offloading device management
responsibilities from storage drive 171A-F in storage array 102A-B.
For example, storage array controllers 110A-D may manage control
information that may describe the state of one or more memory
blocks in the storage drives 171A-F. The control information may
indicate, for example, that a particular memory block has failed
and should no longer be written to, that a particular memory block
contains boot code for a storage array controller 110A-D, the
number of program-erase (`P/E`) cycles that have been performed on
a particular memory block, the age of data stored in a particular
memory block, the type of data that is stored in a particular
memory block, and so forth. In some implementations, the control
information may be stored with an associated memory block as
metadata. In other implementations, the control information for the
storage drives 171A-F may be stored in one or more particular
memory blocks of the storage drives 171A-F that are selected by the
storage array controller 110A-D. The selected memory blocks may be
tagged with an identifier indicating that the selected memory block
contains control information. The identifier may be utilized by the
storage array controllers 110A-D in conjunction with storage drives
171A-F to quickly identify the memory blocks that contain control
information. For example, the storage controllers 110A-D may issue
a command to locate memory blocks that contain control information.
It may be noted that control information may be so large that parts
of the control information may be stored in multiple locations,
that the control information may be stored in multiple locations
for purposes of redundancy, for example, or that the control
information may otherwise be distributed across multiple memory
blocks in the storage drive 171A-F.
[0040] In implementations, storage array controllers 110A-D may
offload device management responsibilities from storage drives
171A-F of storage array 102A-B by retrieving, from the storage
drives 171A-F, control information describing the state of one or
more memory blocks in the storage drives 171A-F. Retrieving the
control information from the storage drives 171A-F may be carried
out, for example, by the storage array controller 110A-D querying
the storage drives 171A-F for the location of control information
for a particular storage drive 171A-F. The storage drives 171A-F
may be configured to execute instructions that enable the storage
drive 171A-F to identify the location of the control information.
The instructions may be executed by a controller (not shown)
associated with or otherwise located on the storage drive 171A-F
and may cause the storage drive 171A-F to scan a portion of each
memory block to identify the memory blocks that store control
information for the storage drives 171A-F. The storage drives
171A-F may respond by sending a response message to the storage
array controller 110A-D that includes the location of control
information for the storage drive 171A-F. Responsive to receiving
the response message, storage array controllers 110A-D may issue a
request to read data stored at the address associated with the
location of control information for the storage drives 171A-F.
[0041] In other implementations, the storage array controllers
110A-D may further offload device management responsibilities from
storage drives 171A-F by performing, in response to receiving the
control information, a storage drive management operation. A
storage drive management operation may include, for example, an
operation that is typically performed by the storage drive 171A-F
(e.g., the controller (not shown) associated with a particular
storage drive 171A-F). A storage drive management operation may
include, for example, ensuring that data is not written to failed
memory blocks within the storage drive 171A-F, ensuring that data
is written to memory blocks within the storage drive 171A-F in such
a way that adequate wear leveling is achieved, and so forth.
[0042] In implementations, storage array 102A-B may implement two
or more storage array controllers 110A-D. For example, storage
array 102A may include storage array controllers 110A and storage
array controllers 110B. At a given instance, a single storage array
controller 110A-D (e.g., storage array controller 110A) of a
storage system 100 may be designated with primary status (also
referred to as "primary controller" herein), and other storage
array controllers 110A-D (e.g., storage array controller 110A) may
be designated with secondary status (also referred to as "secondary
controller" herein). The primary controller may have particular
rights, such as permission to alter data in persistent storage
resource 170A-B (e.g., writing data to persistent storage resource
170A-B). At least some of the rights of the primary controller may
supersede the rights of the secondary controller. For instance, the
secondary controller may not have permission to alter data in
persistent storage resource 170A-B when the primary controller has
the right. The status of storage array controllers 110A-D may
change. For example, storage array controller 110A may be
designated with secondary status, and storage array controller 110B
may be designated with primary status.
[0043] In some implementations, a primary controller, such as
storage array controller 110A, may serve as the primary controller
for one or more storage arrays 102A-B, and a second controller,
such as storage array controller 110B, may serve as the secondary
controller for the one or more storage arrays 102A-B. For example,
storage array controller 110A may be the primary controller for
storage array 102A and storage array 102B, and storage array
controller 110B may be the secondary controller for storage array
102A and 102B. In some implementations, storage array controllers
110C and 110D (also referred to as "storage processing modules")
may neither have primary or secondary status. Storage array
controllers 110C and 110D, implemented as storage processing
modules, may act as a communication interface between the primary
and secondary controllers (e.g., storage array controllers 110A and
110B, respectively) and storage array 102B. For example, storage
array controller 110A of storage array 102A may send a write
request, via SAN 158, to storage array 102B. The write request may
be received by both storage array controllers 110C and 110D of
storage array 102B. Storage array controllers 110C and 110D
facilitate the communication, e.g., send the write request to the
appropriate storage drive 171A-F. It may be noted that in some
implementations storage processing modules may be used to increase
the number of storage drives controlled by the primary and
secondary controllers.
[0044] In implementations, storage array controllers 110A-D are
communicatively coupled, via a midplane (not shown), to one or more
storage drives 171A-F and to one or more NVRAM devices (not shown)
that are included as part of a storage array 102A-B. The storage
array controllers 110A-D may be coupled to the midplane via one or
more data communication links and the midplane may be coupled to
the storage drives 171A-F and the NVRAM devices via one or more
data communications links. The data communications links described
herein are collectively illustrated by data communications links
108A-D and may include a Peripheral Component Interconnect Express
(`PCIe`) bus, for example.
[0045] FIG. 1B illustrates an example system for data storage, in
accordance with some implementations. Storage array controller 101
illustrated in FIG. 1B may be similar to the storage array
controllers 110A-D described with respect to FIG. 1A. In one
example, storage array controller 101 may be similar to storage
array controller 110A or storage array controller 110B. Storage
array controller 101 includes numerous elements for purposes of
illustration rather than limitation. It may be noted that storage
array controller 101 may include the same, more, or fewer elements
configured in the same or different manner in other
implementations. It may be noted that elements of FIG. 1A may be
included below to help illustrate features of storage array
controller 101.
[0046] Storage array controller 101 may include one or more
processing devices 104 and random access memory (`RAM`) 111.
Processing device 104 (or controller 101) represents one or more
general-purpose processing devices such as a microprocessor,
central processing unit, or the like. More particularly, the
processing device 104 (or controller 101) may be a complex
instruction set computing (`CISC`) microprocessor, reduced
instruction set computing (`RISC`) microprocessor, very long
instruction word (`VLIW`) microprocessor, or a processor
implementing other instruction sets or processors implementing a
combination of instruction sets. The processing device 104 (or
controller 101) may also be one or more special-purpose processing
devices such as an application specific integrated circuit
(`ASIC`), a field programmable gate array (`FPGA`), a digital
signal processor (`DSP`), network processor, or the like.
[0047] The processing device 104 may be connected to the RAM 111
via a data communications link 106, which may be embodied as a high
speed memory bus such as a Double-Data Rate 4 (`DDR4`) bus. Stored
in RAM 111 is an operating system 112. In some implementations,
instructions 113 are stored in RAM 111. Instructions 113 may
include computer program instructions for performing operations in
in a direct-mapped flash storage system. In one embodiment, a
direct-mapped flash storage system is one that that addresses data
blocks within flash drives directly and without an address
translation performed by the storage controllers of the flash
drives.
[0048] In implementations, storage array controller 101 includes
one or more host bus adapters 103A-C that are coupled to the
processing device 104 via a data communications link 105A-C. In
implementations, host bus adapters 103A-C may be computer hardware
that connects a host system (e.g., the storage array controller) to
other network and storage arrays. In some examples, host bus
adapters 103A-C may be a Fibre Channel adapter that enables the
storage array controller 101 to connect to a SAN, an Ethernet
adapter that enables the storage array controller 101 to connect to
a LAN, or the like. Host bus adapters 103A-C may be coupled to the
processing device 104 via a data communications link 105A-C such
as, for example, a PCIe bus.
[0049] In implementations, storage array controller 101 may include
a host bus adapter 114 that is coupled to an expander 115. The
expander 115 may be used to attach a host system to a larger number
of storage drives. The expander 115 may, for example, be a SAS
expander utilized to enable the host bus adapter 114 to attach to
storage drives in an implementation where the host bus adapter 114
is embodied as a SAS controller.
[0050] In implementations, storage array controller 101 may include
a switch 116 coupled to the processing device 104 via a data
communications link 109. The switch 116 may be a computer hardware
device that can create multiple endpoints out of a single endpoint,
thereby enabling multiple devices to share a single endpoint. The
switch 116 may, for example, be a PCIe switch that is coupled to a
PCIe bus (e.g., data communications link 109) and presents multiple
PCIe connection points to the midplane.
[0051] In implementations, storage array controller 101 includes a
data communications link 107 for coupling the storage array
controller 101 to other storage array controllers. In some
examples, data communications link 107 may be a QuickPath
Interconnect (QPI) interconnect.
[0052] A traditional storage system that uses traditional flash
drives may implement a process across the flash drives that are
part of the traditional storage system. For example, a higher level
process of the storage system may initiate and control a process
across the flash drives. However, a flash drive of the traditional
storage system may include its own storage controller that also
performs the process. Thus, for the traditional storage system, a
higher level process (e.g., initiated by the storage system) and a
lower level process (e.g., initiated by a storage controller of the
storage system) may both be performed.
[0053] To resolve various deficiencies of a traditional storage
system, operations may be performed by higher level processes and
not by the lower level processes. For example, the flash storage
system may include flash drives that do not include storage
controllers that provide the process. Thus, the operating system of
the flash storage system itself may initiate and control the
process. This may be accomplished by a direct-mapped flash storage
system that addresses data blocks within the flash drives directly
and without an address translation performed by the storage
controllers of the flash drives.
[0054] The operating system of the flash storage system may
identify and maintain a list of allocation units across multiple
flash drives of the flash storage system. The allocation units may
be entire erase blocks or multiple erase blocks. The operating
system may maintain a map or address range that directly maps
addresses to erase blocks of the flash drives of the flash storage
system.
[0055] Direct mapping to the erase blocks of the flash drives may
be used to rewrite data and erase data. For example, the operations
may be performed on one or more allocation units that include a
first data and a second data where the first data is to be retained
and the second data is no longer being used by the flash storage
system. The operating system may initiate the process to write the
first data to new locations within other allocation units and
erasing the second data and marking the allocation units as being
available for use for subsequent data. Thus, the process may only
be performed by the higher level operating system of the flash
storage system without an additional lower level process being
performed by controllers of the flash drives.
[0056] Advantages of the process being performed only by the
operating system of the flash storage system include increased
reliability of the flash drives of the flash storage system as
unnecessary or redundant write operations are not being performed
during the process. One possible point of novelty here is the
concept of initiating and controlling the process at the operating
system of the flash storage system. In addition, the process can be
controlled by the operating system across multiple flash drives.
This is contrast to the process being performed by a storage
controller of a flash drive.
[0057] A storage system can consist of two storage array
controllers that share a set of drives for failover purposes, or it
could consist of a single storage array controller that provides a
storage service that utilizes multiple drives, or it could consist
of a distributed network of storage array controllers each with
some number of drives or some amount of Flash storage where the
storage array controllers in the network collaborate to provide a
complete storage service and collaborate on various aspects of a
storage service including storage allocation and garbage
collection.
[0058] FIG. 1C illustrates a third example system 117 for data
storage in accordance with some implementations. System 117 (also
referred to as "storage system" herein) includes numerous elements
for purposes of illustration rather than limitation. It may be
noted that system 117 may include the same, more, or fewer elements
configured in the same or different manner in other
implementations.
[0059] In one embodiment, system 117 includes a dual Peripheral
Component Interconnect (PCP) flash storage device 118 with
separately addressable fast write storage. System 117 may include a
storage controller 119. In one embodiment, storage controller
119A-D may be a CPU, ASIC, FPGA, or any other circuitry that may
implement control structures necessary according to the present
disclosure. In one embodiment, system 117 includes flash memory
devices (e.g., including flash memory devices 120a-n), operatively
coupled to various channels of the storage device controller 119.
Flash memory devices 120a-n, may be presented to the controller
119A-D as an addressable collection of Flash pages, erase blocks,
and/or control elements sufficient to allow the storage device
controller 119A-D to program and retrieve various aspects of the
Flash. In one embodiment, storage device controller 119A-D may
perform operations on flash memory devices 120a-n including storing
and retrieving data content of pages, arranging and erasing any
blocks, tracking statistics related to the use and reuse of Flash
memory pages, erase blocks, and cells, tracking and predicting
error codes and faults within the Flash memory, controlling voltage
levels associated with programming and retrieving contents of Flash
cells, etc.
[0060] In one embodiment, system 117 may include RAM 121 to store
separately addressable fast-write data. In one embodiment, RAM 121
may be one or more separate discrete devices. In another
embodiment, RAM 121 may be integrated into storage device
controller 119A-D or multiple storage device controllers. The RAM
121 may be utilized for other purposes as well, such as temporary
program memory for a processing device (e.g., a CPU) in the storage
device controller 119.
[0061] In one embodiment, system 117 may include a stored energy
device 122, such as a rechargeable battery or a capacitor. Stored
energy device 122 may store energy sufficient to power the storage
device controller 119, some amount of the RAM (e.g., RAM 121), and
some amount of Flash memory (e.g., Flash memory 120a-120n) for
sufficient time to write the contents of RAM to Flash memory. In
one embodiment, storage device controller 119A-D may write the
contents of RAM to Flash Memory if the storage device controller
detects loss of external power.
[0062] In one embodiment, system 117 includes two data
communications links 123a, 123b. In one embodiment, data
communications links 123a, 123b may be PCI interfaces. In another
embodiment, data communications links 123a, 123b may be based on
other communications standards (e.g., HyperTransport, InfiniBand,
etc.). Data communications links 123a, 123b may be based on
non-volatile memory express (`NVMe`) or NVMe over fabrics (`NVMf`)
specifications that allow external connection to the storage device
controller 119A-D from other components in the storage system 117.
It should be noted that data communications links may be
interchangeably referred to herein as PCI buses for
convenience.
[0063] System 117 may also include an external power source (not
shown), which may be provided over one or both data communications
links 123a, 123b, or which may be provided separately. An
alternative embodiment includes a separate Flash memory (not shown)
dedicated for use in storing the content of RAM 121. The storage
device controller 119A-D may present a logical device over a PCI
bus which may include an addressable fast-write logical device, or
a distinct part of the logical address space of the storage device
118, which may be presented as PCI memory or as persistent storage.
In one embodiment, operations to store into the device are directed
into the RAM 121. On power failure, the storage device controller
119A-D may write stored content associated with the addressable
fast-write logical storage to Flash memory (e.g., Flash memory
120a-n) for long-term persistent storage.
[0064] In one embodiment, the logical device may include some
presentation of some or all of the content of the Flash memory
devices 120a-n, where that presentation allows a storage system
including a storage device 118 (e.g., storage system 117) to
directly address Flash memory pages and directly reprogram erase
blocks from storage system components that are external to the
storage device through the PCI bus. The presentation may also allow
one or more of the external components to control and retrieve
other aspects of the Flash memory including some or all of:
tracking statistics related to use and reuse of Flash memory pages,
erase blocks, and cells across all the Flash memory devices;
tracking and predicting error codes and faults within and across
the Flash memory devices; controlling voltage levels associated
with programming and retrieving contents of Flash cells; etc.
[0065] In one embodiment, the stored energy device 122 may be
sufficient to ensure completion of in-progress operations to the
Flash memory devices 120a-120n stored energy device 122 may power
storage device controller 119A-D and associated Flash memory
devices (e.g., 120a-n) for those operations, as well as for the
storing of fast-write RAM to Flash memory. Stored energy device 122
may be used to store accumulated statistics and other parameters
kept and tracked by the Flash memory devices 120a-n and/or the
storage device controller 119. Separate capacitors or stored energy
devices (such as smaller capacitors near or embedded within the
Flash memory devices themselves) may be used for some or all of the
operations described herein.
[0066] Various schemes may be used to track and optimize the life
span of the stored energy component, such as adjusting voltage
levels over time, partially discharging the storage energy device
122 to measure corresponding discharge characteristics, etc. If the
available energy decreases over time, the effective available
capacity of the addressable fast-write storage may be decreased to
ensure that it can be written safely based on the currently
available stored energy.
[0067] FIG. 1D illustrates a third example system 124 for data
storage in accordance with some implementations. In one embodiment,
system 124 includes storage controllers 125a, 125b. In one
embodiment, storage controllers 125a, 125b are operatively coupled
to Dual PCI storage devices 119a, 119b and 119c, 119d,
respectively. Storage controllers 125a, 125b may be operatively
coupled (e.g., via a storage network 130) to some number of host
computers 127a-n.
[0068] In one embodiment, two storage controllers (e.g., 125a and
125b) provide storage services, such as a SCS) block storage array,
a file server, an object server, a database or data analytics
service, etc. The storage controllers 125a, 125b may provide
services through some number of network interfaces (e.g., 126a-d)
to host computers 127a-n outside of the storage system 124. Storage
controllers 125a, 125b may provide integrated services or an
application entirely within the storage system 124, forming a
converged storage and compute system. The storage controllers 125a,
125b may utilize the fast write memory within or across storage
devices 119a-d to journal in progress operations to ensure the
operations are not lost on a power failure, storage controller
removal, storage controller or storage system shutdown, or some
fault of one or more software or hardware components within the
storage system 124.
[0069] In one embodiment, controllers 125a, 125b operate as PCI
masters to one or the other PCI buses 128a, 128b. In another
embodiment, 128a and 128b may be based on other communications
standards (e.g., HyperTransport, InfiniBand, etc.). Other storage
system embodiments may operate storage controllers 125a, 125b as
multi-masters for both PCI buses 128a, 128b. Alternately, a
PCI/NVMe/NVMf switching infrastructure or fabric may connect
multiple storage controllers. Some storage system embodiments may
allow storage devices to communicate with each other directly
rather than communicating only with storage controllers. In one
embodiment, a storage device controller 119a may be operable under
direction from a storage controller 125a to synthesize and transfer
data to be stored into Flash memory devices from data that has been
stored in RAM (e.g., RAM 121 of FIG. 1C). For example, a
recalculated version of RAM content may be transferred after a
storage controller has determined that an operation has fully
committed across the storage system, or when fast-write memory on
the device has reached a certain used capacity, or after a certain
amount of time, to ensure improve safety of the data or to release
addressable fast-write capacity for reuse. This mechanism may be
used, for example, to avoid a second transfer over a bus (e.g.,
128a, 128b) from the storage controllers 125a, 125b. In one
embodiment, a recalculation may include compressing data, attaching
indexing or other metadata, combining multiple data segments
together, performing erasure code calculations, etc.
[0070] In one embodiment, under direction from a storage controller
125a, 125b, a storage device controller 119a, 119b may be operable
to calculate and transfer data to other storage devices from data
stored in RAM (e.g., RAM 121 of FIG. 1C) without involvement of the
storage controllers 125a, 125b. This operation may be used to
mirror data stored in one controller 125a to another controller
125b, or it could be used to offload compression, data aggregation,
and/or erasure coding calculations and transfers to storage devices
to reduce load on storage controllers or the storage controller
interface 129a, 129b to the PCI bus 128a, 128b.
[0071] A storage device controller 119A-D may include mechanisms
for implementing high availability primitives for use by other
parts of a storage system external to the Dual PCI storage device
118. For example, reservation or exclusion primitives may be
provided so that, in a storage system with two storage controllers
providing a highly available storage service, one storage
controller may prevent the other storage controller from accessing
or continuing to access the storage device. This could be used, for
example, in cases where one controller detects that the other
controller is not functioning properly or where the interconnect
between the two storage controllers may itself not be functioning
properly.
[0072] In one embodiment, a storage system for use with Dual PCI
direct mapped storage devices with separately addressable fast
write storage includes systems that manage erase blocks or groups
of erase blocks as allocation units for storing data on behalf of
the storage service, or for storing metadata (e.g., indexes, logs,
etc.) associated with the storage service, or for proper management
of the storage system itself. Flash pages, which may be a few
kilobytes in size, may be written as data arrives or as the storage
system is to persist data for long intervals of time (e.g., above a
defined threshold of time). To commit data more quickly, or to
reduce the number of writes to the Flash memory devices, the
storage controllers may first write data into the separately
addressable fast write storage on one more storage devices.
[0073] In one embodiment, the storage controllers 125a, 125b may
initiate the use of erase blocks within and across storage devices
(e.g., 118) in accordance with an age and expected remaining
lifespan of the storage devices, or based on other statistics. The
storage controllers 125a, 125b may initiate garbage collection and
data migration data between storage devices in accordance with
pages that are no longer needed as well as to manage Flash page and
erase block lifespans and to manage overall system performance.
[0074] In one embodiment, the storage system 124 may utilize
mirroring and/or erasure coding schemes as part of storing data
into addressable fast write storage and/or as part of writing data
into allocation units associated with erase blocks. Erasure codes
may be used across storage devices, as well as within erase blocks
or allocation units, or within and across Flash memory devices on a
single storage device, to provide redundancy against single or
multiple storage device failures or to protect against internal
corruptions of Flash memory pages resulting from Flash memory
operations or from degradation of Flash memory cells. Mirroring and
erasure coding at various levels may be used to recover from
multiple types of failures that occur separately or in
combination.
[0075] The embodiments depicted with reference to FIGS. 2A-G
illustrate a storage cluster that stores user data, such as user
data originating from one or more user or client systems or other
sources external to the storage cluster. The storage cluster
distributes user data across storage nodes housed within a chassis,
or across multiple chassis, using erasure coding and redundant
copies of metadata. Erasure coding refers to a method of data
protection or reconstruction in which data is stored across a set
of different locations, such as disks, storage nodes or geographic
locations. Flash memory is one type of solid-state memory that may
be integrated with the embodiments, although the embodiments may be
extended to other types of solid-state memory or other storage
medium, including non-solid state memory. Control of storage
locations and workloads are distributed across the storage
locations in a clustered peer-to-peer system. Tasks such as
mediating communications between the various storage nodes,
detecting when a storage node has become unavailable, and balancing
I/Os (inputs and outputs) across the various storage nodes, are all
handled on a distributed basis. Data is laid out or distributed
across multiple storage nodes in data fragments or stripes that
support data recovery in some embodiments. Ownership of data can be
reassigned within a cluster, independent of input and output
patterns. This architecture described in more detail below allows a
storage node in the cluster to fail, with the system remaining
operational, since the data can be reconstructed from other storage
nodes and thus remain available for input and output operations. In
various embodiments, a storage node may be referred to as a cluster
node, a blade, or a server.
[0076] The storage cluster may be contained within a chassis, i.e.,
an enclosure housing one or more storage nodes. A mechanism to
provide power to each storage node, such as a power distribution
bus, and a communication mechanism, such as a communication bus
that enables communication between the storage nodes are included
within the chassis. The storage cluster can run as an independent
system in one location according to some embodiments. In one
embodiment, a chassis contains at least two instances of both the
power distribution and the communication bus which may be enabled
or disabled independently. The internal communication bus may be an
Ethernet bus, however, other technologies such as PCIe, InfiniBand,
and others, are equally suitable. The chassis provides a port for
an external communication bus for enabling communication between
multiple chassis, directly or through a switch, and with client
systems. The external communication may use a technology such as
Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments, the
external communication bus uses different communication bus
technologies for inter-chassis and client communication. If a
switch is deployed within or between chassis, the switch may act as
a translation between multiple protocols or technologies. When
multiple chassis are connected to define a storage cluster, the
storage cluster may be accessed by a client using either
proprietary interfaces or standard interfaces such as network file
system (`NFS`), common internet file system (`CIFS`), small
computer system interface (`SCSI`) or hypertext transfer protocol
(`HTTP`). Translation from the client protocol may occur at the
switch, chassis external communication bus or within each storage
node. In some embodiments, multiple chassis may be coupled or
connected to each other through an aggregator switch. A portion
and/or all of the coupled or connected chassis may be designated as
a storage cluster. As discussed above, each chassis can have
multiple blades, each blade has a media access control (`MAC`)
address, but the storage cluster is presented to an external
network as having a single cluster IP address and a single MAC
address in some embodiments.
[0077] Each storage node may be one or more storage servers and
each storage server is connected to one or more non-volatile solid
state memory units, which may be referred to as storage units or
storage devices. One embodiment includes a single storage server in
each storage node and between one to eight non-volatile solid state
memory units, however this one example is not meant to be limiting.
The storage server may include a processor, DRAM and interfaces for
the internal communication bus and power distribution for each of
the power buses. Inside the storage node, the interfaces and
storage unit share a communication bus, e.g., PCI Express, in some
embodiments. The non-volatile solid state memory units may directly
access the internal communication bus interface through a storage
node communication bus, or request the storage node to access the
bus interface. The non-volatile solid state memory unit contains an
embedded CPU, solid state storage controller, and a quantity of
solid state mass storage, e.g., between 2-32 terabytes (`TB`) in
some embodiments. An embedded volatile storage medium, such as
DRAM, and an energy reserve apparatus are included in the
non-volatile solid state memory unit. In some embodiments, the
energy reserve apparatus is a capacitor, super-capacitor, or
battery that enables transferring a subset of DRAM contents to a
stable storage medium in the case of power loss. In some
embodiments, the non-volatile solid state memory unit is
constructed with a storage class memory, such as phase change or
magnetoresistive random access memory (`MRAM`) that substitutes for
DRAM and enables a reduced power hold-up apparatus.
[0078] One of many features of the storage nodes and non-volatile
solid state storage is the ability to proactively rebuild data in a
storage cluster. The storage nodes and non-volatile solid state
storage can determine when a storage node or non-volatile solid
state storage in the storage cluster is unreachable, independent of
whether there is an attempt to read data involving that storage
node or non-volatile solid state storage. The storage nodes and
non-volatile solid state storage then cooperate to recover and
rebuild the data in at least partially new locations. This
constitutes a proactive rebuild, in that the system rebuilds data
without waiting until the data is needed for a read access
initiated from a client system employing the storage cluster. These
and further details of the storage memory and operation thereof are
discussed below.
[0079] FIG. 2A is a perspective view of a storage cluster 161, with
multiple storage nodes 150 and internal solid-state memory coupled
to each storage node to provide network attached storage or storage
area network, in accordance with some embodiments. A network
attached storage, storage area network, or a storage cluster, or
other storage memory, could include one or more storage clusters
161, each having one or more storage nodes 150, in a flexible and
reconfigurable arrangement of both the physical components and the
amount of storage memory provided thereby. The storage cluster 161
is designed to fit in a rack, and one or more racks can be set up
and populated as desired for the storage memory. The storage
cluster 161 has a chassis 138 having multiple slots 142. It should
be appreciated that chassis 138 may be referred to as a housing,
enclosure, or rack unit. In one embodiment, the chassis 138 has
fourteen slots 142, although other numbers of slots are readily
devised. For example, some embodiments have four slots, eight
slots, sixteen slots, thirty-two slots, or other suitable number of
slots. Each slot 142 can accommodate one storage node 150 in some
embodiments. Chassis 138 includes flaps 148 that can be utilized to
mount the chassis 138 on a rack. Fans 144 provide air circulation
for cooling of the storage nodes 150 and components thereof,
although other cooling components could be used, or an embodiment
could be devised without cooling components. A switch fabric 146
couples storage nodes 150 within chassis 138 together and to a
network for communication to the memory. In an embodiment depicted
in herein, the slots 142 to the left of the switch fabric 146 and
fans 144 are shown occupied by storage nodes 150, while the slots
142 to the right of the switch fabric 146 and fans 144 are empty
and available for insertion of storage node 150 for illustrative
purposes. This configuration is one example, and one or more
storage nodes 150 could occupy the slots 142 in various further
arrangements. The storage node arrangements need not be sequential
or adjacent in some embodiments. Storage nodes 150 are hot
pluggable, meaning that a storage node 150 can be inserted into a
slot 142 in the chassis 138, or removed from a slot 142, without
stopping or powering down the system. Upon insertion or removal of
storage node 150 from slot 142, the system automatically
reconfigures in order to recognize and adapt to the change.
Reconfiguration, in some embodiments, includes restoring redundancy
and/or rebalancing data or load.
[0080] Each storage node 150 can have multiple components. In the
embodiment shown here, the storage node 150 includes a printed
circuit board 159 populated by a CPU 156, i.e., processor, a memory
154 coupled to the CPU 156, and a non-volatile solid state storage
152 coupled to the CPU 156, although other mountings and/or
components could be used in further embodiments. The memory 154 has
instructions which are executed by the CPU 156 and/or data operated
on by the CPU 156. As further explained below, the non-volatile
solid state storage 152 includes flash or, in further embodiments,
other types of solid-state memory.
[0081] Referring to FIG. 2A, storage cluster 161 is scalable,
meaning that storage capacity with non-uniform storage sizes is
readily added, as described above. One or more storage nodes 150
can be plugged into or removed from each chassis and the storage
cluster self-configures in some embodiments. Plug-in storage nodes
150, whether installed in a chassis as delivered or later added,
can have different sizes. For example, in one embodiment a storage
node 150 can have any multiple of 4 TB, e.g., 8 TB, 12 TB, 16 TB,
32 TB, etc. In further embodiments, a storage node 150 could have
any multiple of other storage amounts or capacities. Storage
capacity of each storage node 150 is broadcast, and influences
decisions of how to stripe the data. For maximum storage
efficiency, an embodiment can self-configure as wide as possible in
the stripe, subject to a predetermined requirement of continued
operation with loss of up to one, or up to two, non-volatile solid
state storage units 152 or storage nodes 150 within the
chassis.
[0082] FIG. 2B is a block diagram showing a communications
interconnect 173 and power distribution bus 172 coupling multiple
storage nodes 150. Referring back to FIG. 2A, the communications
interconnect 173 can be included in or implemented with the switch
fabric 146 in some embodiments. Where multiple storage clusters 161
occupy a rack, the communications interconnect 173 can be included
in or implemented with a top of rack switch, in some embodiments.
As illustrated in FIG. 2B, storage cluster 161 is enclosed within a
single chassis 138. External port 176 is coupled to storage nodes
150 through communications interconnect 173, while external port
174 is coupled directly to a storage node. External power port 178
is coupled to power distribution bus 172. Storage nodes 150 may
include varying amounts and differing capacities of non-volatile
solid state storage 152 as described with reference to FIG. 2A. In
addition, one or more storage nodes 150 may be a compute only
storage node as illustrated in FIG. 2B. Authorities 168 are
implemented on the non-volatile solid state storages 152, for
example as lists or other data structures stored in memory. In some
embodiments the authorities are stored within the non-volatile
solid state storage 152 and supported by software executing on a
controller or other processor of the non-volatile solid state
storage 152. In a further embodiment, authorities 168 are
implemented on the storage nodes 150, for example as lists or other
data structures stored in the memory 154 and supported by software
executing on the CPU 156 of the storage node 150. Authorities 168
control how and where data is stored in the non-volatile solid
state storages 152 in some embodiments. This control assists in
determining which type of erasure coding scheme is applied to the
data, and which storage nodes 150 have which portions of the data.
Each authority 168 may be assigned to a non-volatile solid state
storage 152. Each authority may control a range of inode numbers,
segment numbers, or other data identifiers which are assigned to
data by a file system, by the storage nodes 150, or by the
non-volatile solid state storage 152, in various embodiments.
[0083] Every piece of data, and every piece of metadata, has
redundancy in the system in some embodiments. In addition, every
piece of data and every piece of metadata has an owner, which may
be referred to as an authority. If that authority is unreachable,
for example through failure of a storage node, there is a plan of
succession for how to find that data or that metadata. In various
embodiments, there are redundant copies of authorities 168.
Authorities 168 have a relationship to storage nodes 150 and
non-volatile solid state storage 152 in some embodiments. Each
authority 168, covering a range of data segment numbers or other
identifiers of the data, may be assigned to a specific non-volatile
solid state storage 152. In some embodiments the authorities 168
for all of such ranges are distributed over the non-volatile solid
state storages 152 of a storage cluster. Each storage node 150 has
a network port that provides access to the non-volatile solid state
storage(s) 152 of that storage node 150. Data can be stored in a
segment, which is associated with a segment number and that segment
number is an indirection for a configuration of a RAID (redundant
array of independent disks) stripe in some embodiments. The
assignment and use of the authorities 168 thus establishes an
indirection to data. Indirection may be referred to as the ability
to reference data indirectly, in this case via an authority 168, in
accordance with some embodiments. A segment identifies a set of
non-volatile solid state storage 152 and a local identifier into
the set of non-volatile solid state storage 152 that may contain
data. In some embodiments, the local identifier is an offset into
the device and may be reused sequentially by multiple segments. In
other embodiments the local identifier is unique for a specific
segment and never reused. The offsets in the non-volatile solid
state storage 152 are applied to locating data for writing to or
reading from the non-volatile solid state storage 152 (in the form
of a RAID stripe). Data is striped across multiple units of
non-volatile solid state storage 152, which may include or be
different from the non-volatile solid state storage 152 having the
authority 168 for a particular data segment.
[0084] If there is a change in where a particular segment of data
is located, e.g., during a data move or a data reconstruction, the
authority 168 for that data segment should be consulted, at that
non-volatile solid state storage 152 or storage node 150 having
that authority 168. In order to locate a particular piece of data,
embodiments calculate a hash value for a data segment or apply an
inode number or a data segment number. The output of this operation
points to a non-volatile solid state storage 152 having the
authority 168 for that particular piece of data. In some
embodiments there are two stages to this operation. The first stage
maps an entity identifier (ID), e.g., a segment number, inode
number, or directory number to an authority identifier. This
mapping may include a calculation such as a hash or a bit mask. The
second stage is mapping the authority identifier to a particular
non-volatile solid state storage 152, which may be done through an
explicit mapping. The operation is repeatable, so that when the
calculation is performed, the result of the calculation repeatably
and reliably points to a particular non-volatile solid state
storage 152 having that authority 168. The operation may include
the set of reachable storage nodes as input. If the set of
reachable non-volatile solid state storage units changes the
optimal set changes. In some embodiments, the persisted value is
the current assignment (which is always true) and the calculated
value is the target assignment the cluster will attempt to
reconfigure towards. This calculation may be used to determine the
optimal non-volatile solid state storage 152 for an authority in
the presence of a set of non-volatile solid state storage 152 that
are reachable and constitute the same cluster. The calculation also
determines an ordered set of peer non-volatile solid state storage
152 that will also record the authority to non-volatile solid state
storage mapping so that the authority may be determined even if the
assigned non-volatile solid state storage is unreachable. A
duplicate or substitute authority 168 may be consulted if a
specific authority 168 is unavailable in some embodiments.
[0085] With reference to FIGS. 2A and 2B, two of the many tasks of
the CPU 156 on a storage node 150 are to break up write data, and
reassemble read data. When the system has determined that data is
to be written, the authority 168 for that data is located as above.
When the segment ID for data is already determined the request to
write is forwarded to the non-volatile solid state storage 152
currently determined to be the host of the authority 168 determined
from the segment. The host CPU 156 of the storage node 150, on
which the non-volatile solid state storage 152 and corresponding
authority 168 reside, then breaks up or shards the data and
transmits the data out to various non-volatile solid state storage
152. The transmitted data is written as a data stripe in accordance
with an erasure coding scheme. In some embodiments, data is
requested to be pulled, and in other embodiments, data is pushed.
In reverse, when data is read, the authority 168 for the segment ID
containing the data is located as described above. The host CPU 156
of the storage node 150 on which the non-volatile solid state
storage 152 and corresponding authority 168 reside requests the
data from the non-volatile solid state storage and corresponding
storage nodes pointed to by the authority. In some embodiments the
data is read from flash storage as a data stripe. The host CPU 156
of storage node 150 then reassembles the read data, correcting any
errors (if present) according to the appropriate erasure coding
scheme, and forwards the reassembled data to the network. In
further embodiments, some or all of these tasks can be handled in
the non-volatile solid state storage 152. In some embodiments, the
segment host requests the data be sent to storage node 150 by
requesting pages from storage and then sending the data to the
storage node making the original request.
[0086] In some systems, for example in UNIX-style file systems,
data is handled with an index node or inode, which specifies a data
structure that represents an object in a file system. The object
could be a file or a directory, for example. Metadata may accompany
the object, as attributes such as permission data and a creation
timestamp, among other attributes. A segment number could be
assigned to all or a portion of such an object in a file system. In
other systems, data segments are handled with a segment number
assigned elsewhere. For purposes of discussion, the unit of
distribution is an entity, and an entity can be a file, a directory
or a segment. That is, entities are units of data or metadata
stored by a storage system. Entities are grouped into sets called
authorities. Each authority has an authority owner, which is a
storage node that has the exclusive right to update the entities in
the authority. In other words, a storage node contains the
authority, and that the authority, in turn, contains entities.
[0087] A segment is a logical container of data in accordance with
some embodiments. A segment is an address space between medium
address space and physical flash locations, i.e., the data segment
number, are in this address space. Segments may also contain
meta-data, which enable data redundancy to be restored (rewritten
to different flash locations or devices) without the involvement of
higher level software. In one embodiment, an internal format of a
segment contains client data and medium mappings to determine the
position of that data. Each data segment is protected, e.g., from
memory and other failures, by breaking the segment into a number of
data and parity shards, where applicable. The data and parity
shards are distributed, i.e., striped, across non-volatile solid
state storage 152 coupled to the host CPUs 156 (See FIGS. 2E and
2G) in accordance with an erasure coding scheme. Usage of the term
segments refers to the container and its place in the address space
of segments in some embodiments. Usage of the term stripe refers to
the same set of shards as a segment and includes how the shards are
distributed along with redundancy or parity information in
accordance with some embodiments.
[0088] A series of address-space transformations takes place across
an entire storage system. At the top are the directory entries
(file names) which link to an inode. Inodes point into medium
address space, where data is logically stored. Medium addresses may
be mapped through a series of indirect mediums to spread the load
of large files, or implement data services like deduplication or
snapshots. Medium addresses may be mapped through a series of
indirect mediums to spread the load of large files, or implement
data services like deduplication or snapshots. Segment addresses
are then translated into physical flash locations. Physical flash
locations have an address range bounded by the amount of flash in
the system in accordance with some embodiments. Medium addresses
and segment addresses are logical containers, and in some
embodiments use a 128 bit or larger identifier so as to be
practically infinite, with a likelihood of reuse calculated as
longer than the expected life of the system. Addresses from logical
containers are allocated in a hierarchical fashion in some
embodiments. Initially, each non-volatile solid state storage unit
152 may be assigned a range of address space. Within this assigned
range, the non-volatile solid state storage 152 is able to allocate
addresses without synchronization with other non-volatile solid
state storage 152.
[0089] Data and metadata is stored by a set of underlying storage
layouts that are optimized for varying workload patterns and
storage devices. These layouts incorporate multiple redundancy
schemes, compression formats and index algorithms. Some of these
layouts store information about authorities and authority masters,
while others store file metadata and file data. The redundancy
schemes include error correction codes that tolerate corrupted bits
within a single storage device (such as a NAND flash chip), erasure
codes that tolerate the failure of multiple storage nodes, and
replication schemes that tolerate data center or regional failures.
In some embodiments, low density parity check (`LDPC`) code is used
within a single storage unit. Reed-Solomon encoding is used within
a storage cluster, and mirroring is used within a storage grid in
some embodiments. Metadata may be stored using an ordered log
structured index (such as a Log Structured Merge Tree), and large
data may not be stored in a log structured layout.
[0090] In order to maintain consistency across multiple copies of
an entity, the storage nodes agree implicitly on two things through
calculations: (1) the authority that contains the entity, and (2)
the storage node that contains the authority. The assignment of
entities to authorities can be done by pseudo randomly assigning
entities to authorities, by splitting entities into ranges based
upon an externally produced key, or by placing a single entity into
each authority. Examples of pseudorandom schemes are linear hashing
and the Replication Under Scalable Hashing (`RUSH`) family of
hashes, including Controlled Replication Under Scalable Hashing
(`CRUSH`). In some embodiments, pseudo-random assignment is
utilized only for assigning authorities to nodes because the set of
nodes can change. The set of authorities cannot change so any
subjective function may be applied in these embodiments. Some
placement schemes automatically place authorities on storage nodes,
while other placement schemes rely on an explicit mapping of
authorities to storage nodes. In some embodiments, a pseudorandom
scheme is utilized to map from each authority to a set of candidate
authority owners. A pseudorandom data distribution function related
to CRUSH may assign authorities to storage nodes and create a list
of where the authorities are assigned. Each storage node has a copy
of the pseudorandom data distribution function, and can arrive at
the same calculation for distributing, and later finding or
locating an authority. Each of the pseudorandom schemes requires
the reachable set of storage nodes as input in some embodiments in
order to conclude the same target nodes. Once an entity has been
placed in an authority, the entity may be stored on physical
devices so that no expected failure will lead to unexpected data
loss. In some embodiments, rebalancing algorithms attempt to store
the copies of all entities within an authority in the same layout
and on the same set of machines.
[0091] Examples of expected failures include device failures,
stolen machines, datacenter fires, and regional disasters, such as
nuclear or geological events. Different failures lead to different
levels of acceptable data loss. In some embodiments, a stolen
storage node impacts neither the security nor the reliability of
the system, while depending on system configuration, a regional
event could lead to no loss of data, a few seconds or minutes of
lost updates, or even complete data loss.
[0092] In the embodiments, the placement of data for storage
redundancy is independent of the placement of authorities for data
consistency. In some embodiments, storage nodes that contain
authorities do not contain any persistent storage. Instead, the
storage nodes are connected to non-volatile solid state storage
units that do not contain authorities. The communications
interconnect between storage nodes and non-volatile solid state
storage units consists of multiple communication technologies and
has non-uniform performance and fault tolerance characteristics. In
some embodiments, as mentioned above, non-volatile solid state
storage units are connected to storage nodes via PCI express,
storage nodes are connected together within a single chassis using
Ethernet backplane, and chassis are connected together to form a
storage cluster. Storage clusters are connected to clients using
Ethernet or fiber channel in some embodiments. If multiple storage
clusters are configured into a storage grid, the multiple storage
clusters are connected using the Internet or other long-distance
networking links, such as a "metro scale" link or private link that
does not traverse the internet.
[0093] Authority owners have the exclusive right to modify
entities, to migrate entities from one non-volatile solid state
storage unit to another non-volatile solid state storage unit, and
to add and remove copies of entities. This allows for maintaining
the redundancy of the underlying data. When an authority owner
fails, is going to be decommissioned, or is overloaded, the
authority is transferred to a new storage node. Transient failures
make it non-trivial to ensure that all non-faulty machines agree
upon the new authority location. The ambiguity that arises due to
transient failures can be achieved automatically by a consensus
protocol such as Paxos, hot-warm failover schemes, via manual
intervention by a remote system administrator, or by a local
hardware administrator (such as by physically removing the failed
machine from the cluster, or pressing a button on the failed
machine). In some embodiments, a consensus protocol is used, and
failover is automatic. If too many failures or replication events
occur in too short a time period, the system goes into a
self-preservation mode and halts replication and data movement
activities until an administrator intervenes in accordance with
some embodiments.
[0094] As authorities are transferred between storage nodes and
authority owners update entities in their authorities, the system
transfers messages between the storage nodes and non-volatile solid
state storage units. With regard to persistent messages, messages
that have different purposes are of different types. Depending on
the type of the message, the system maintains different ordering
and durability guarantees. As the persistent messages are being
processed, the messages are temporarily stored in multiple durable
and non-durable storage hardware technologies. In some embodiments,
messages are stored in RAM, NVRAM and on NAND flash devices, and a
variety of protocols are used in order to make efficient use of
each storage medium. Latency-sensitive client requests may be
persisted in replicated NVRAM, and then later NAND, while
background rebalancing operations are persisted directly to
NAND.
[0095] Persistent messages are persistently stored prior to being
transmitted. This allows the system to continue to serve client
requests despite failures and component replacement. Although many
hardware components contain unique identifiers that are visible to
system administrators, manufacturer, hardware supply chain and
ongoing monitoring quality control infrastructure, applications
running on top of the infrastructure address virtualize addresses.
These virtualized addresses do not change over the lifetime of the
storage system, regardless of component failures and replacements.
This allows each component of the storage system to be replaced
over time without reconfiguration or disruptions of client request
processing, i.e., the system supports non-disruptive upgrades.
[0096] In some embodiments, the virtualized addresses are stored
with sufficient redundancy. A continuous monitoring system
correlates hardware and software status and the hardware
identifiers. This allows detection and prediction of failures due
to faulty components and manufacturing details. The monitoring
system also enables the proactive transfer of authorities and
entities away from impacted devices before failure occurs by
removing the component from the critical path in some
embodiments.
[0097] FIG. 2C is a multiple level block diagram, showing contents
of a storage node 150 and contents of a non-volatile solid state
storage 152 of the storage node 150. Data is communicated to and
from the storage node 150 by a network interface controller (`NIC`)
202 in some embodiments. Each storage node 150 has a CPU 156, and
one or more non-volatile solid state storage 152, as discussed
above. Moving down one level in FIG. 2C, each non-volatile solid
state storage 152 has a relatively fast non-volatile solid state
memory, such as nonvolatile random access memory (`NVRAM`) 204, and
flash memory 206. In some embodiments, NVRAM 204 may be a component
that does not require program/erase cycles (DRAM, MRAM, PCM), and
can be a memory that can support being written vastly more often
than the memory is read from. Moving down another level in FIG. 2C,
the NVRAM 204 is implemented in one embodiment as high speed
volatile memory, such as dynamic random access memory (DRAM) 216,
backed up by energy reserve 218. Energy reserve 218 provides
sufficient electrical power to keep the DRAM 216 powered long
enough for contents to be transferred to the flash memory 206 in
the event of power failure. In some embodiments, energy reserve 218
is a capacitor, super-capacitor, battery, or other device, that
supplies a suitable supply of energy sufficient to enable the
transfer of the contents of DRAM 216 to a stable storage medium in
the case of power loss. The flash memory 206 is implemented as
multiple flash dies 222, which may be referred to as packages of
flash dies 222 or an array of flash dies 222. It should be
appreciated that the flash dies 222 could be packaged in any number
of ways, with a single die per package, multiple dies per package
(i.e. multichip packages), in hybrid packages, as bare dies on a
printed circuit board or other substrate, as encapsulated dies,
etc. In the embodiment shown, the non-volatile solid state storage
152 has a controller 212 or other processor, and an input output
(I/O) port 210 coupled to the controller 212. I/O port 210 is
coupled to the CPU 156 and/or the network interface controller 202
of the flash storage node 150. Flash input output (I/O) port 220 is
coupled to the flash dies 222, and a direct memory access unit
(DMA) 214 is coupled to the controller 212, the DRAM 216 and the
flash dies 222. In the embodiment shown, the I/O port 210,
controller 212, DMA unit 214 and flash I/O port 220 are implemented
on a programmable logic device (`PLD`) 208, e.g., a field
programmable gate array (FPGA). In this embodiment, each flash die
222 has pages, organized as sixteen kB (kilobyte) pages 224, and a
register 226 through which data can be written to or read from the
flash die 222. In further embodiments, other types of solid-state
memory are used in place of, or in addition to flash memory
illustrated within flash die 222.
[0098] Storage clusters 161, in various embodiments as disclosed
herein, can be contrasted with storage arrays in general. The
storage nodes 150 are part of a collection that creates the storage
cluster 161. Each storage node 150 owns a slice of data and
computing required to provide the data. Multiple storage nodes 150
cooperate to store and retrieve the data. Storage memory or storage
devices, as used in storage arrays in general, are less involved
with processing and manipulating the data. Storage memory or
storage devices in a storage array receive commands to read, write,
or erase data. The storage memory or storage devices in a storage
array are not aware of a larger system in which they are embedded,
or what the data means. Storage memory or storage devices in
storage arrays can include various types of storage memory, such as
RAM, solid state drives, hard disk drives, etc. The storage units
152 described herein have multiple interfaces active simultaneously
and serving multiple purposes. In some embodiments, some of the
functionality of a storage node 150 is shifted into a storage unit
152, transforming the storage unit 152 into a combination of
storage unit 152 and storage node 150. Placing computing (relative
to storage data) into the storage unit 152 places this computing
closer to the data itself. The various system embodiments have a
hierarchy of storage node layers with different capabilities. By
contrast, in a storage array, a controller owns and knows
everything about all of the data that the controller manages in a
shelf or storage devices. In a storage cluster 161, as described
herein, multiple controllers in multiple storage units 152 and/or
storage nodes 150 cooperate in various ways (e.g., for erasure
coding, data sharding, metadata communication and redundancy,
storage capacity expansion or contraction, data recovery, and so
on).
[0099] FIG. 2D shows a storage server environment, which uses
embodiments of the storage nodes 150 and storage units 152 of FIGS.
2A-C. In this version, each storage unit 152 has a processor such
as controller 212 (see FIG. 2C), an FPGA (field programmable gate
array), flash memory 206, and NVRAM 204 (which is super-capacitor
backed DRAM 216, see FIGS. 2B and 2C) on a PCIe (peripheral
component interconnect express) board in a chassis 138 (see FIG.
2A). The storage unit 152 may be implemented as a single board
containing storage, and may be the largest tolerable failure domain
inside the chassis. In some embodiments, up to two storage units
152 may fail and the device will continue with no data loss.
[0100] The physical storage is divided into named regions based on
application usage in some embodiments. The NVRAM 204 is a
contiguous block of reserved memory in the storage unit 152 DRAM
216, and is backed by NAND flash. NVRAM 204 is logically divided
into multiple memory regions written for two as spool (e.g.,
spool_region). Space within the NVRAM 204 spools is managed by each
authority 168 independently. Each device provides an amount of
storage space to each authority 168. That authority 168 further
manages lifetimes and allocations within that space. Examples of a
spool include distributed transactions or notions. When the primary
power to a storage unit 152 fails, onboard super-capacitors provide
a short duration of power hold up. During this holdup interval, the
contents of the NVRAM 204 are flushed to flash memory 206. On the
next power-on, the contents of the NVRAM 204 are recovered from the
flash memory 206.
[0101] As for the storage unit controller, the responsibility of
the logical "controller" is distributed across each of the blades
containing authorities 168. This distribution of logical control is
shown in FIG. 2D as a host controller 242, mid-tier controller 244
and storage unit controller(s) 246. Management of the control plane
and the storage plane are treated independently, although parts may
be physically co-located on the same blade. Each authority 168
effectively serves as an independent controller. Each authority 168
provides its own data and metadata structures, its own background
workers, and maintains its own lifecycle.
[0102] FIG. 2E is a blade 252 hardware block diagram, showing a
control plane 254, compute and storage planes 256, 258, and
authorities 168 interacting with underlying physical resources,
using embodiments of the storage nodes 150 and storage units 152 of
FIGS. 2A-C in the storage server environment of FIG. 2D. The
control plane 254 is partitioned into a number of authorities 168
which can use the compute resources in the compute plane 256 to run
on any of the blades 252. The storage plane 258 is partitioned into
a set of devices, each of which provides access to flash 206 and
NVRAM 204 resources. In one embodiment, the compute plane 256 may
perform the operations of a storage array controller, as described
herein, on one or more devices of the storage plane 258 (e.g., a
storage array).
[0103] In the compute and storage planes 256, 258 of FIG. 2E, the
authorities 168 interact with the underlying physical resources
(i.e., devices). From the point of view of an authority 168, its
resources are striped over all of the physical devices. From the
point of view of a device, it provides resources to all authorities
168, irrespective of where the authorities happen to run. Each
authority 168 has allocated or has been allocated one or more
partitions 260 of storage memory in the storage units 152, e.g.
partitions 260 in flash memory 206 and NVRAM 204. Each authority
168 uses those allocated partitions 260 that belong to it, for
writing or reading user data. Authorities can be associated with
differing amounts of physical storage of the system. For example,
one authority 168 could have a larger number of partitions 260 or
larger sized partitions 260 in one or more storage units 152 than
one or more other authorities 168.
[0104] FIG. 2F depicts elasticity software layers in blades 252 of
a storage cluster, in accordance with some embodiments. In the
elasticity structure, elasticity software is symmetric, i.e., each
blade's compute module 270 runs the three identical layers of
processes depicted in FIG. 2F. Storage managers 274 execute read
and write requests from other blades 252 for data and metadata
stored in local storage unit 152 NVRAM 204 and flash 206.
Authorities 168 fulfill client requests by issuing the necessary
reads and writes to the blades 252 on whose storage units 152 the
corresponding data or metadata resides. Endpoints 272 parse client
connection requests received from switch fabric 146 supervisory
software, relay the client connection requests to the authorities
168 responsible for fulfillment, and relay the authorities' 168
responses to clients. The symmetric three-layer structure enables
the storage system's high degree of concurrency. Elasticity scales
out efficiently and reliably in these embodiments. In addition,
elasticity implements a unique scale-out technique that balances
work evenly across all resources regardless of client access
pattern, and maximizes concurrency by eliminating much of the need
for inter-blade coordination that typically occurs with
conventional distributed locking.
[0105] Still referring to FIG. 2F, authorities 168 running in the
compute modules 270 of a blade 252 perform the internal operations
required to fulfill client requests. One feature of elasticity is
that authorities 168 are stateless, i.e., they cache active data
and metadata in their own blades' 252 DRAMs for fast access, but
the authorities store every update in their NVRAM 204 partitions on
three separate blades 252 until the update has been written to
flash 206. All the storage system writes to NVRAM 204 are in
triplicate to partitions on three separate blades 252 in some
embodiments. With triple-mirrored NVRAM 204 and persistent storage
protected by parity and Reed-Solomon RAID checksums, the storage
system can survive concurrent failure of two blades 252 with no
loss of data, metadata, or access to either.
[0106] Because authorities 168 are stateless, they can migrate
between blades 252. Each authority 168 has a unique identifier.
NVRAM 204 and flash 206 partitions are associated with authorities'
168 identifiers, not with the blades 252 on which they are running
in some. Thus, when an authority 168 migrates, the authority 168
continues to manage the same storage partitions from its new
location. When a new blade 252 is installed in an embodiment of the
storage cluster, the system automatically rebalances load by:
partitioning the new blade's 252 storage for use by the system's
authorities 168, migrating selected authorities 168 to the new
blade 252, starting endpoints 272 on the new blade 252 and
including them in the switch fabric's 146 client connection
distribution algorithm.
[0107] From their new locations, migrated authorities 168 persist
the contents of their NVRAM 204 partitions on flash 206, process
read and write requests from other authorities 168, and fulfill the
client requests that endpoints 272 direct to them. Similarly, if a
blade 252 fails or is removed, the system redistributes its
authorities 168 among the system's remaining blades 252. The
redistributed authorities 168 continue to perform their original
functions from their new locations.
[0108] FIG. 2G depicts authorities 168 and storage resources in
blades 252 of a storage cluster, in accordance with some
embodiments. Each authority 168 is exclusively responsible for a
partition of the flash 206 and NVRAM 204 on each blade 252. The
authority 168 manages the content and integrity of its partitions
independently of other authorities 168. Authorities 168 compress
incoming data and preserve it temporarily in their NVRAM 204
partitions, and then consolidate, RAID-protect, and persist the
data in segments of the storage in their flash 206 partitions. As
the authorities 168 write data to flash 206, storage managers 274
perform the necessary flash translation to optimize write
performance and maximize media longevity. In the background,
authorities 168 "garbage collect," or reclaim space occupied by
data that clients have made obsolete by overwriting the data. It
should be appreciated that since authorities' 168 partitions are
disjoint, there is no need for distributed locking to execute
client and writes or to perform background functions.
[0109] The embodiments described herein may utilize various
software, communication and/or networking protocols. In addition,
the configuration of the hardware and/or software may be adjusted
to accommodate various protocols. For example, the embodiments may
utilize Active Directory, which is a database based system that
provides authentication, directory, policy, and other services in a
WINDOWS.TM. environment. In these embodiments, LDAP (Lightweight
Directory Access Protocol) is one example application protocol for
querying and modifying items in directory service providers such as
Active Directory. In some embodiments, a network lock manager
(`NLM`) is utilized as a facility that works in cooperation with
the Network File System (`NFS`) to provide a System V style of
advisory file and record locking over a network. The Server Message
Block (`SMB`) protocol, one version of which is also known as
Common Internet File System (`CIFS`), may be integrated with the
storage systems discussed herein. SMP operates as an
application-layer network protocol typically used for providing
shared access to files, printers, and serial ports and
miscellaneous communications between nodes on a network. SMB also
provides an authenticated inter-process communication mechanism.
AMAZON.TM. S3 (Simple Storage Service) is a web service offered by
Amazon Web Services, and the systems described herein may interface
with Amazon S3 through web services interfaces (REST
(representational state transfer), SOAP (simple object access
protocol), and BitTorrent). A RESTful API (application programming
interface) breaks down a transaction to create a series of small
modules. Each module addresses a particular underlying part of the
transaction. The control or permissions provided with these
embodiments, especially for object data, may include utilization of
an access control list (`ACL`). The ACL is a list of permissions
attached to an object and the ACL specifies which users or system
processes are granted access to objects, as well as what operations
are allowed on given objects. The systems may utilize Internet
Protocol version 6 (`IPv6`), as well as IPv4, for the
communications protocol that provides an identification and
location system for computers on networks and routes traffic across
the Internet. The routing of packets between networked systems may
include Equal-cost multi-path routing (`ECMP`), which is a routing
strategy where next-hop packet forwarding to a single destination
can occur over multiple "best paths" which tie for top place in
routing metric calculations. Multi-path routing can be used in
conjunction with most routing protocols, because it is a per-hop
decision limited to a single router. The software may support
Multi-tenancy, which is an architecture in which a single instance
of a software application serves multiple customers. Each customer
may be referred to as a tenant. Tenants may be given the ability to
customize some parts of the application, but may not customize the
application's code, in some embodiments. The embodiments may
maintain audit logs. An audit log is a document that records an
event in a computing system. In addition to documenting what
resources were accessed, audit log entries typically include
destination and source addresses, a timestamp, and user login
information for compliance with various regulations. The
embodiments may support various key management policies, such as
encryption key rotation. In addition, the system may support
dynamic root passwords or some variation dynamically changing
passwords.
[0110] FIG. 3A sets forth a diagram of a storage system 306 that is
coupled for data communications with a cloud services provider 302
in accordance with some embodiments of the present disclosure.
Although depicted in less detail, the storage system 306 depicted
in FIG. 3A may be similar to the storage systems described above
with reference to FIGS. 1A-1D and FIGS. 2A-2G. In some embodiments,
the storage system 306 depicted in FIG. 3A may be embodied as a
storage system that includes imbalanced active/active controllers,
as a storage system that includes balanced active/active
controllers, as a storage system that includes active/active
controllers where less than all of each controller's resources are
utilized such that each controller has reserve resources that may
be used to support failover, as a storage system that includes
fully active/active controllers, as a storage system that includes
dataset-segregated controllers, as a storage system that includes
dual-layer architectures with front-end controllers and back-end
integrated storage controllers, as a storage system that includes
scale-out clusters of dual-controller arrays, as well as
combinations of such embodiments.
[0111] In the example depicted in FIG. 3A, the storage system 306
is coupled to the cloud services provider 302 via a data
communications link 304. The data communications link 304 may be
embodied as a dedicated data communications link, as a data
communications pathway that is provided through the use of one or
data communications networks such as a wide area network (`WAN`) or
local area network (`LAN`), or as some other mechanism capable of
transporting digital information between the storage system 306 and
the cloud services provider 302. Such a data communications link
304 may be fully wired, fully wireless, or some aggregation of
wired and wireless data communications pathways. In such an
example, digital information may be exchanged between the storage
system 306 and the cloud services provider 302 via the data
communications link 304 using one or more data communications
protocols. For example, digital information may be exchanged
between the storage system 306 and the cloud services provider 302
via the data communications link 304 using the handheld device
transfer protocol (`HDTP`), hypertext transfer protocol (`HTTP`),
internet protocol (`IP`), real-time transfer protocol (`RTP`),
transmission control protocol (`TCP`), user datagram protocol
(`UDP`), wireless application protocol (`WAP`), or other
protocol.
[0112] The cloud services provider 302 depicted in FIG. 3A may be
embodied, for example, as a system and computing environment that
provides services to users of the cloud services provider 302
through the sharing of computing resources via the data
communications link 304. The cloud services provider 302 may
provide on-demand access to a shared pool of configurable computing
resources such as computer networks, servers, storage, applications
and services, and so on. The shared pool of configurable resources
may be rapidly provisioned and released to a user of the cloud
services provider 302 with minimal management effort. Generally,
the user of the cloud services provider 302 is unaware of the exact
computing resources utilized by the cloud services provider 302 to
provide the services. Although in many cases such a cloud services
provider 302 may be accessible via the Internet, readers of skill
in the art will recognize that any system that abstracts the use of
shared resources to provide services to a user through any data
communications link may be considered a cloud services provider
302.
[0113] In the example depicted in FIG. 3A, the cloud services
provider 302 may be configured to provide a variety of services to
the storage system 306 and users of the storage system 306 through
the implementation of various service models. For example, the
cloud services provider 302 may be configured to provide services
to the storage system 306 and users of the storage system 306
through the implementation of an infrastructure as a service
(`IaaS`) service model where the cloud services provider 302 offers
computing infrastructure such as virtual machines and other
resources as a service to subscribers. In addition, the cloud
services provider 302 may be configured to provide services to the
storage system 306 and users of the storage system 306 through the
implementation of a platform as a service (`PaaS`) service model
where the cloud services provider 302 offers a development
environment to application developers. Such a development
environment may include, for example, an operating system,
programming-language execution environment, database, web server,
or other components that may be utilized by application developers
to develop and run software solutions on a cloud platform.
Furthermore, the cloud services provider 302 may be configured to
provide services to the storage system 306 and users of the storage
system 306 through the implementation of a software as a service
(`SaaS`) service model where the cloud services provider 302 offers
application software, databases, as well as the platforms that are
used to run the applications to the storage system 306 and users of
the storage system 306, providing the storage system 306 and users
of the storage system 306 with on-demand software and eliminating
the need to install and run the application on local computers,
which may simplify maintenance and support of the application. The
cloud services provider 302 may be further configured to provide
services to the storage system 306 and users of the storage system
306 through the implementation of an authentication as a service
(`AaaS`) service model where the cloud services provider 302 offers
authentication services that can be used to secure access to
applications, data sources, or other resources. The cloud services
provider 302 may also be configured to provide services to the
storage system 306 and users of the storage system 306 through the
implementation of a storage as a service model where the cloud
services provider 302 offers access to its storage infrastructure
for use by the storage system 306 and users of the storage system
306. Readers will appreciate that the cloud services provider 302
may be configured to provide additional services to the storage
system 306 and users of the storage system 306 through the
implementation of additional service models, as the service models
described above are included only for explanatory purposes and in
no way represent a limitation of the services that may be offered
by the cloud services provider 302 or a limitation as to the
service models that may be implemented by the cloud services
provider 302.
[0114] In the example depicted in FIG. 3A, the cloud services
provider 302 may be embodied, for example, as a private cloud, as a
public cloud, or as a combination of a private cloud and public
cloud. In an embodiment in which the cloud services provider 302 is
embodied as a private cloud, the cloud services provider 302 may be
dedicated to providing services to a single organization rather
than providing services to multiple organizations. In an embodiment
where the cloud services provider 302 is embodied as a public
cloud, the cloud services provider 302 may provide services to
multiple organizations. Public cloud and private cloud deployment
models may differ and may come with various advantages and
disadvantages. For example, because a public cloud deployment
involves the sharing of a computing infrastructure across different
organization, such a deployment may not be ideal for organizations
with security concerns, mission-critical workloads, uptime
requirements demands, and so on. While a private cloud deployment
can address some of these issues, a private cloud deployment may
require on-premises staff to manage the private cloud. In still
alternative embodiments, the cloud services provider 302 may be
embodied as a mix of a private and public cloud services with a
hybrid cloud deployment.
[0115] The cloud services provider 302 may also be configured to
provide access to virtualized computing environments to the storage
system 306 and users of the storage system 306. Such virtualized
computing environments may be embodied, for example, as a virtual
machine or other virtualized computer hardware platforms, virtual
storage devices, virtualized computer network resources, and so on.
Examples of such virtualized environments can include virtual
machines that are created to emulate an actual computer,
virtualized desktop environments that separate a logical desktop
from a physical machine, virtualized file systems that allow
uniform access to different types of concrete file systems, and
many others.
[0116] For further explanation, FIG. 3B sets forth a diagram of a
storage system 306 in accordance with some embodiments of the
present disclosure. Although depicted in less detail, the storage
system 306 depicted in FIG. 3B may be similar to the storage
systems described above with reference to FIGS. 1A-1D and FIGS.
2A-2G as the storage system may include many of the components
described above.
[0117] The storage system 306 depicted in FIG. 3B may include
storage resources 308, which may be embodied in many forms. For
example, in some embodiments the storage resources 308 can include
nano-RAM or another form of nonvolatile random access memory that
utilizes carbon nanotubes deposited on a substrate. In some
embodiments, the storage resources 308 may include 3D crosspoint
non-volatile memory in which bit storage is based on a change of
bulk resistance, in conjunction with a stackable cross-gridded data
access array. In some embodiments, the storage resources 308 may
include flash memory, including single-level cell (`SLC`) NAND
flash, multi-level cell (`MLC`) NAND flash, triple-level cell
(`TLC`) NAND flash, quad-level cell (`QLC`) NAND flash, and others.
The storage resources 308 depicted in FIG. 3A may include various
forms of storage-class memory (`SCM`).
[0118] The storage system 306 depicted in FIG. 3B also includes
communications resources 310 that may be useful in facilitating
data communications between components within the storage system
306, as well as data communications between the storage system 306
and computing devices that are outside of the storage system 306.
The communications resources 310 may be configured to utilize a
variety of different protocols and data communication fabrics to
facilitate data communications between components within the
storage systems as well as computing devices that are outside of
the storage system. For example, the communications resources 310
can include fibre channel (`FC`) technologies such as FC fabrics
and FC protocols that can transport SCSI commands over FC networks.
The communications resources 310 can also include FC over ethernet
(`FCoE`) technologies through which FC frames are encapsulated and
transmitted over Ethernet networks. The communications resources
310 can also include InfiniBand (`IB`) technologies in which a
switched fabric topology is utilized to facilitate transmissions
between channel adapters. The communications resources 310 can also
include NVM Express (`NVMe`) technologies and NVMe over fabrics
(`NVMeoF`) technologies through which non-volatile storage media
attached via a PCI express (`PCIe`) bus may be accessed. The
communications resources 310 can also include mechanisms for
accessing storage resources 308 within the storage system 306
utilizing serial attached SCSI (`SAS`), serial ATA (`SATA`) bus
interfaces for connecting storage resources 308 within the storage
system 306 to host bus adapters within the storage system 306,
internet small computer systems interface (`iSCSI`) technologies to
provide block-level access to storage resources 308 within the
storage system 306, and other communications resources that that
may be useful in facilitating data communications between
components within the storage system 306, as well as data
communications between the storage system 306 and computing devices
that are outside of the storage system 306.
[0119] The storage system 306 depicted in FIG. 3B also includes
processing resources 312 that may be useful in useful in executing
computer program instructions and performing other computational
tasks within the storage system 306. The processing resources 312
may include one or more application-specific integrated circuits
(`ASICs`) that are customized for some particular purpose as well
as one or more central processing units (`CPUs`). The processing
resources 312 may also include one or more digital signal
processors (`DSPs`), one or more field-programmable gate arrays
(`FPGAs`), one or more systems on a chip (`SoCs`), or other form of
processing resources 312. The storage system 306 may utilize the
storage resources 312 to perform a variety of tasks including, but
not limited to, supporting the execution of software resources 314
that will be described in greater detail below.
[0120] The storage system 306 depicted in FIG. 3B also includes
software resources 314 that, when executed by processing resources
312 within the storage system 306, may perform various tasks. The
software resources 314 may include, for example, one or more
modules of computer program instructions that when executed by
processing resources 312 within the storage system 306 are useful
in carrying out various data protection techniques to preserve the
integrity of data that is stored within the storage systems.
[0121] For further explanation, FIG. 3C sets forth an example of a
cloud-based storage system 318 in accordance with some embodiments
of the present disclosure. In the example depicted in FIG. 3C, the
cloud-based storage system 318 is created entirely in a cloud
computing environment 316 such as, for example, Amazon Web Services
(`AWS`), Microsoft Azure, Google Cloud Platform, IBM Cloud, Oracle
Cloud, and others. The cloud-based storage system 318 may be used
to provide services similar to the services that may be provided by
the storage systems described above. For example, the cloud-based
storage system 318 may be used to provide block storage services to
users of the cloud-based storage system 318, the cloud-based
storage system 318 may be used to provide storage services to users
of the cloud-based storage system 318 through the use of
solid-state storage, and so on.
[0122] The cloud-based storage system 318 depicted in FIG. 3C
includes two cloud computing instances 320, 322 that each are used
to support the execution of a storage controller application 324,
326. The cloud computing instances 320, 322 may be embodied, for
example, as instances of cloud computing resources (e.g., virtual
machines) that may be provided by the cloud computing environment
316 to support the execution of software applications such as the
storage controller application 324, 326. In one embodiment, the
cloud computing instances 320, 322 may be embodied as Amazon
Elastic Compute Cloud (`EC2`) instances. In such an example, an
Amazon Machine Image (`AMI`) that includes the storage controller
application 324, 326 may be booted to create and configure a
virtual machine that may execute the storage controller application
324, 326.
[0123] In the example method depicted in FIG. 3C, the storage
controller application 324, 326 may be embodied as a module of
computer program instructions that, when executed, carries out
various storage tasks. For example, the storage controller
application 324, 326 may be embodied as a module of computer
program instructions that, when executed, carries out the same
tasks as the controllers 110A, 110B in FIG. 1A described above such
as writing data received from the users of the cloud-based storage
system 318 to the cloud-based storage system 318, erasing data from
the cloud-based storage system 318, retrieving data from the
cloud-based storage system 318 and providing such data to users of
the cloud-based storage system 318, monitoring and reporting of
disk utilization and performance, performing redundancy operations,
such as RAID or RAID-like data redundancy operations, compressing
data, encrypting data, deduplicating data, and so forth. Readers
will appreciate that because there are two cloud computing
instances 320, 322 that each include the storage controller
application 324, 326, in some embodiments one cloud computing
instance 320 may operate as the primary controller as described
above while the other cloud computing instance 322 may operate as
the secondary controller as described above. In such an example, in
order to save costs, the cloud computing instance 320 that operates
as the primary controller may be deployed on a relatively
high-performance and relatively expensive cloud computing instance
while the cloud computing instance 322 that operates as the
secondary controller may be deployed on a relatively
low-performance and relatively inexpensive cloud computing
instance. Readers will appreciate that the storage controller
application 324, 326 depicted in FIG. 3C may include identical
source code that is executed within different cloud computing
instances 320, 322.
[0124] Consider an example in which the cloud computing environment
316 is embodied as AWS and the cloud computing instances are
embodied as EC2 instances. In such an example, AWS offers many
types of EC2 instances. For example, AWS offers a suite of general
purpose EC2 instances that include varying levels of memory and
processing power. In such an example, the cloud computing instance
320 that operates as the primary controller may be deployed on one
of the instance types that has a relatively large amount of memory
and processing power while the cloud computing instance 322 that
operates as the secondary controller may be deployed on one of the
instance types that has a relatively small amount of memory and
processing power. In such an example, upon the occurrence of a
failover event where the roles of primary and secondary are
switched, a double failover may actually be carried out such that:
1) a first failover event where the cloud computing instance 322
that formerly operated as the secondary controller begins to
operate as the primary controller, and 2) a third cloud computing
instance (not shown) that is of an instance type that has a
relatively large amount of memory and processing power is spun up
with a copy of the storage controller application, where the third
cloud computing instance begins operating as the primary controller
while the cloud computing instance 322 that originally operated as
the secondary controller begins operating as the secondary
controller again. In such an example, the cloud computing instance
320 that formerly operated as the primary controller may be
terminated. Readers will appreciate that in alternative
embodiments, the cloud computing instance 320 that is operating as
the secondary controller after the failover event may continue to
operate as the secondary controller and the cloud computing
instance 322 that operated as the primary controller after the
occurrence of the failover event may be terminated once the primary
role has been assumed by the third cloud computing instance (not
shown).
[0125] Readers will appreciate that while the embodiments described
above relate to embodiments where one cloud computing instance 320
operates as the primary controller and the second cloud computing
instance 322 operates as the secondary controller, other
embodiments are within the scope of the present disclosure. For
example, each cloud computing instance 320, 322 may operate as a
primary controller for some portion of the address space supported
by the cloud-based storage system 318, each cloud computing
instance 320, 322 may operate as a primary controller where the
servicing of I/O operations directed to the cloud-based storage
system 318 are divided in some other way, and so on. In fact, in
other embodiments where costs savings may be prioritized over
performance demands, only a single cloud computing instance may
exist that contains the storage controller application. In such an
example, a controller failure may take more time to recover from as
a new cloud computing instance that includes the storage controller
application would need to be spun up rather than having an already
created cloud computing instance take on the role of servicing I/O
operations that would have otherwise been handled by the failed
cloud computing instance.
[0126] The cloud-based storage system 318 depicted in FIG. 3C
includes cloud computing instances 340a, 340b, 340n with local
storage 330, 334, 338. The cloud computing instances 340a, 340b,
340n depicted in FIG. 3C may be embodied, for example, as instances
of cloud computing resources that may be provided by the cloud
computing environment 316 to support the execution of software
applications. The cloud computing instances 340a, 340b, 340n of
FIG. 3C may differ from the cloud computing instances 320, 322
described above as the cloud computing instances 340a, 340b, 340n
of FIG. 3C have local storage 330, 334, 338 resources whereas the
cloud computing instances 320, 322 that support the execution of
the storage controller application 324, 326 need not have local
storage resources. The cloud computing instances 340a, 340b, 340n
with local storage 330, 334, 338 may be embodied, for example, as
EC2 M5 instances that include one or more SSDs, as EC2 R5 instances
that include one or more SSDs, as EC2 I3 instances that include one
or more SSDs, and so on. In some embodiments, the local storage
330, 334, 338 must be embodied as solid-state storage (e.g., SSDs)
rather than storage that makes use of hard disk drives.
[0127] In the example depicted in FIG. 3C, each of the cloud
computing instances 340a, 340b, 340n with local storage 330, 334,
338 can include a software daemon 328, 332, 336 that, when executed
by a cloud computing instance 340a, 340b, 340n can present itself
to the storage controller applications 324, 326 as if the cloud
computing instance 340a, 340b, 340n were a physical storage device
(e.g., one or more SSDs). In such an example, the software daemon
328, 332, 336 may include computer program instructions similar to
those that would normally be contained on a storage device such
that the storage controller applications 324, 326 can send and
receive the same commands that a storage controller would send to
storage devices. In such a way, the storage controller applications
324, 326 may include code that is identical to (or substantially
identical to) the code that would be executed by the controllers in
the storage systems described above. In these and similar
embodiments, communications between the storage controller
applications 324, 326 and the cloud computing instances 340a, 340b,
340n with local storage 330, 334, 338 may utilize iSCSI, NVMe over
TCP, messaging, a custom protocol, or in some other mechanism.
[0128] In the example depicted in FIG. 3C, each of the cloud
computing instances 340a, 340b, 340n with local storage 330, 334,
338 may also be coupled to block-storage 342, 344, 346 that is
offered by the cloud computing environment 316. The block-storage
342, 344, 346 that is offered by the cloud computing environment
316 may be embodied, for example, as Amazon Elastic Block Store
(`EBS`) volumes. For example, a first EBS volume may be coupled to
a first cloud computing instance 340a, a second EBS volume may be
coupled to a second cloud computing instance 340b, and a third EBS
volume may be coupled to a third cloud computing instance 340n. In
such an example, the block-storage 342, 344, 346 that is offered by
the cloud computing environment 316 may be utilized in a manner
that is similar to how the NVRAM devices described above are
utilized, as the software daemon 328, 332, 336 (or some other
module) that is executing within a particular cloud comping
instance 340a, 340b, 340n may, upon receiving a request to write
data, initiate a write of the data to its attached EBS volume as
well as a write of the data to its local storage 330, 334, 338
resources. In some alternative embodiments, data may only be
written to the local storage 330, 334, 338 resources within a
particular cloud comping instance 340a, 340b, 340n. In an
alternative embodiment, rather than using the block-storage 342,
344, 346 that is offered by the cloud computing environment 316 as
NVRAM, actual RAM on each of the cloud computing instances 340a,
340b, 340n with local storage 330, 334, 338 may be used as NVRAM,
thereby decreasing network utilization costs that would be
associated with using an EBS volume as the NVRAM.
[0129] In the example depicted in FIG. 3C, the cloud computing
instances 340a, 340b, 340n with local storage 330, 334, 338 may be
utilized, by cloud computing instances 320, 322 that support the
execution of the storage controller application 324, 326 to service
I/O operations that are directed to the cloud-based storage system
318. Consider an example in which a first cloud computing instance
320 that is executing the storage controller application 324 is
operating as the primary controller. In such an example, the first
cloud computing instance 320 that is executing the storage
controller application 324 may receive (directly or indirectly via
the secondary controller) requests to write data to the cloud-based
storage system 318 from users of the cloud-based storage system
318. In such an example, the first cloud computing instance 320
that is executing the storage controller application 324 may
perform various tasks such as, for example, deduplicating the data
contained in the request, compressing the data contained in the
request, determining where to the write the data contained in the
request, and so on, before ultimately sending a request to write a
deduplicated, encrypted, or otherwise possibly updated version of
the data to one or more of the cloud computing instances 340a,
340b, 340n with local storage 330, 334, 338. Either cloud computing
instance 320, 322, in some embodiments, may receive a request to
read data from the cloud-based storage system 318 and may
ultimately send a request to read data to one or more of the cloud
computing instances 340a, 340b, 340n with local storage 330, 334,
338.
[0130] Readers will appreciate that when a request to write data is
received by a particular cloud computing instance 340a, 340b, 340n
with local storage 330, 334, 338, the software daemon 328, 332, 336
or some other module of computer program instructions that is
executing on the particular cloud computing instance 340a, 340b,
340n may be configured to not only write the data to its own local
storage 330, 334, 338 resources and any appropriate block-storage
342, 344, 346 that are offered by the cloud computing environment
316, but the software daemon 328, 332, 336 or some other module of
computer program instructions that is executing on the particular
cloud computing instance 340a, 340b, 340n may also be configured to
write the data to cloud-based object storage 348 that is attached
to the particular cloud computing instance 340a, 340b, 340n. The
cloud-based object storage 348 that is attached to the particular
cloud computing instance 340a, 340b, 340n may be embodied, for
example, as Amazon Simple Storage Service (`S3`) storage that is
accessible by the particular cloud computing instance 340a, 340b,
340n. In other embodiments, the cloud computing instances 320, 322
that each include the storage controller application 324, 326 may
initiate the storage of the data in the local storage 330, 334, 338
of the cloud computing instances 340a, 340b, 340n and the
cloud-based object storage 348.
[0131] Readers will appreciate that, as described above, the
cloud-based storage system 318 may be used to provide block storage
services to users of the cloud-based storage system 318. While the
local storage 330, 334, 338 resources and the block-storage 342,
344, 346 resources that are utilized by the cloud computing
instances 340a, 340b, 340n may support block-level access, the
cloud-based object storage 348 that is attached to the particular
cloud computing instance 340a, 340b, 340n supports only
object-based access. In order to address this, the software daemon
328, 332, 336 or some other module of computer program instructions
that is executing on the particular cloud computing instance 340a,
340b, 340n may be configured to take blocks of data, package those
blocks into objects, and write the objects to the cloud-based
object storage 348 that is attached to the particular cloud
computing instance 340a, 340b, 340n.
[0132] Readers will appreciate that the cloud-based object storage
348 may be incorporated into the cloud-based storage system 318 to
increase the durability of the cloud-based storage system 318.
Continuing with the example described above where the cloud
computing instances 340a, 340b, 340n are EC2 instances, readers
will understand that EC2 instances are only guaranteed to have a
monthly uptime of 99.9% and data stored in the local instance store
only persists during the lifetime of the EC2 instance. As such,
relying on the cloud computing instances 340a, 340b, 340n with
local storage 330, 334, 338 as the only source of persistent data
storage in the cloud-based storage system 318 may result in a
relatively unreliable storage system. Likewise, EBS volumes are
designed for 99.999% availability. As such, even relying on EBS as
the persistent data store in the cloud-based storage system 318 may
result in a storage system that is not sufficiently durable. Amazon
S3, however, is designed to provide 99.999999999% durability,
meaning that a cloud-based storage system 318 that can incorporate
S3 into its pool of storage is substantially more durable than
various other options.
[0133] Readers will appreciate that while a cloud-based storage
system 318 that can incorporate S3 into its pool of storage is
substantially more durable than various other options, utilizing S3
as the primary pool of storage may result in storage system that
has relatively slow response times and relatively long I/O
latencies. As such, the cloud-based storage system 318 depicted in
FIG. 3C not only stores data in S3 but the cloud-based storage
system 318 also stores data in local storage 330, 334, 338
resources and block-storage 342, 344, 346 resources that are
utilized by the cloud computing instances 340a, 340b, 340n, such
that read operations can be serviced from local storage 330, 334,
338 resources and the block-storage 342, 344, 346 resources that
are utilized by the cloud computing instances 340a, 340b, 340n,
thereby reducing read latency when users of the cloud-based storage
system 318 attempt to read data from the cloud-based storage system
318.
[0134] As described above, when the cloud computing instances 340a,
340b, 340n with local storage 330, 334, 338 are embodied as EC2
instances, the cloud computing instances 340a, 340b, 340n with
local storage 330, 334, 338 are only guaranteed to have a monthly
uptime of 99.9% and data stored in the local instance store only
persists during the lifetime of each cloud computing instance 340a,
340b, 340n with local storage 330, 334, 338. As such, one or more
modules of computer program instructions that are executing within
the cloud-based storage system 318 (e.g., a monitoring module that
is executing on its own EC2 instance) may be designed to handle the
failure of one or more of the cloud computing instances 340a, 340b,
340n with local storage 330, 334, 338. In such an example, the
monitoring module may handle the failure of one or more of the
cloud computing instances 340a, 340b, 340n with local storage 330,
334, 338 by creating one or more new cloud computing instances with
local storage, retrieving data that was stored on the failed cloud
computing instances 340a, 340b, 340n from the cloud-based object
storage 348, and storing the data retrieved from the cloud-based
object storage 348 in local storage on the newly created cloud
computing instances. Readers will appreciate that many variants of
this process may be implemented.
[0135] Readers will appreciate that the storage system 306 depicted
in FIG. 3B may be useful for supporting various types of software
applications. For example, the storage system 306 may be useful in
supporting artificial intelligence (`AI`) applications, database
applications, DevOps projects, electronic design automation tools,
event-driven software applications, high performance computing
applications, simulation applications, high-speed data capture and
analysis applications, machine learning applications, media
production applications, media serving applications, picture
archiving and communication systems (`PACS`) applications, software
development applications, virtual reality applications, augmented
reality applications, and many other types of applications by
providing storage resources to such applications.
[0136] The storage systems described above may also be part of a
multi-cloud environment in which multiple cloud computing and
storage services are deployed in a single heterogeneous
architecture. In order to facilitate the operation of such a
multi-cloud environment, DevOps tools may be deployed to enable
orchestration across clouds. Likewise, continuous development and
continuous integration tools may be deployed to standardize
processes around continuous integration and delivery, new feature
rollout and provisioning cloud workloads. By standardizing these
processes, a multi-cloud strategy may be implemented that enables
the utilization of the best provider for each workload.
Furthermore, application monitoring and visibility tools may be
deployed to move application workloads around different clouds,
identify performance issues, and perform other tasks. In addition,
security and compliance tools may be deployed for to ensure
compliance with security requirements, government regulations, and
so on. Such a multi-cloud environment may also include tools for
application delivery and smart workload management to ensure
efficient application delivery and help direct workloads across the
distributed and heterogeneous infrastructure, as well as tools that
ease the deployment and maintenance of packaged and custom
applications in the cloud and enable portability amongst clouds.
The multi-cloud environment may similarly include tools for data
portability.
[0137] The systems described above can support the execution of a
wide array of software applications. Such software applications can
be deployed in a variety of ways, including container-based
deployment models. Containerized applications may be managed using
a variety of tools. For example, containerized applications may be
managed using Docker Swarm, a clustering and scheduling tool for
Docker containers that enables IT administrators and developers to
establish and manage a cluster of Docker nodes as a single virtual
system. Likewise, containerized applications may be managed through
the use of Kubernetes, a container-orchestration system for
automating deployment, scaling and management of containerized
applications. Kubernetes may execute on top of operating systems
such as, for example, Red Hat Enterprise Linux, Ubuntu Server, SUSE
Linux Enterprise Servers, and others. In such examples, a master
node may assign tasks to worker/minion nodes. Kubernetes can
include a set of components (e.g., kubelet, kube-proxy, cAdvisor)
that manage individual nodes as a well as a set of components
(e.g., etcd, API server, Scheduler, Control Manager) that form a
control plane. Various controllers (e.g., Replication Controller,
DaemonSet Controller) can drive the state of a Kubernetes cluster
by managing a set of pods that includes one or more containers that
are deployed on a single node. Containerized applications may be
used to facilitate a serverless, cloud native computing deployment
and management model for software applications. In support of a
serverless, cloud native computing deployment and management model
for software applications, containers may be used as part of an
event handling mechanisms (e.g., AWS Lambdas) such that various
events cause a containerized application to be spun up to operate
as an event handler.
[0138] For further explanation, FIG. 3D illustrates an exemplary
computing device 350 that may be specifically configured to perform
one or more of the processes described herein. As shown in FIG. 3D,
computing device 350 may include a communication interface 352, a
processor 354, a storage device 356, and an input/output ("I/O")
module 358 communicatively connected one to another via a
communication infrastructure 360. While an exemplary computing
device 350 is shown in FIG. 3D, the components illustrated in FIG.
3D are not intended to be limiting. Additional or alternative
components may be used in other embodiments. Components of
computing device 350 shown in FIG. 3D will now be described in
additional detail.
[0139] Communication interface 352 may be configured to communicate
with one or more computing devices. Examples of communication
interface 352 include, without limitation, a wired network
interface (such as a network interface card), a wireless network
interface (such as a wireless network interface card), a modem, an
audio/video connection, and any other suitable interface.
[0140] Processor 354 generally represents any type or form of
processing unit capable of processing data and/or interpreting,
executing, and/or directing execution of one or more of the
instructions, processes, and/or operations described herein.
Processor 354 may perform operations by executing
computer-executable instructions 362 (e.g., an application,
software, code, and/or other executable data instance) stored in
storage device 356.
[0141] Storage device 356 may include one or more data storage
media, devices, or configurations and may employ any type, form,
and combination of data storage media and/or device. For example,
storage device 356 may include, but is not limited to, any
combination of the non-volatile media and/or volatile media
described herein. Electronic data, including data described herein,
may be temporarily and/or permanently stored in storage device 356.
For example, data representative of computer-executable
instructions 362 configured to direct processor 354 to perform any
of the operations described herein may be stored within storage
device 356. In some examples, data may be arranged in one or more
databases residing within storage device 356.
[0142] I/O module 358 may include one or more I/O modules
configured to receive user input and provide user output. I/O
module 358 may include any hardware, firmware, software, or
combination thereof supportive of input and output capabilities.
For example, I/O module 358 may include hardware and/or software
for capturing user input, including, but not limited to, a keyboard
or keypad, a touchscreen component (e.g., touchscreen display), a
receiver (e.g., an RF or infrared receiver), motion sensors, and/or
one or more input buttons.
[0143] I/O module 358 may include one or more devices for
presenting output to a user, including, but not limited to, a
graphics engine, a display (e.g., a display screen), one or more
output drivers (e.g., display drivers), one or more audio speakers,
and one or more audio drivers. In certain embodiments, I/O module
358 is configured to provide graphical data to a display for
presentation to a user. The graphical data may be representative of
one or more graphical user interfaces and/or any other graphical
content as may serve a particular implementation. In some examples,
any of the systems, computing devices, and/or other components
described herein may be implemented by computing device 350.
[0144] For further explanation, FIG. 4 sets forth a block diagram
illustrating a plurality of storage systems (402, 404, 406) that
support a pod according to some embodiments of the present
disclosure. Although depicted in less detail, the storage systems
(402, 404, 406) depicted in FIG. 4 may be similar to the storage
systems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G,
FIGS. 3A-3B, or any combination thereof. In fact, the storage
systems (402, 404, 406) depicted in FIG. 4 may include the same,
fewer, or additional components as the storage systems described
above.
[0145] In the example depicted in FIG. 4, each of the storage
systems (402, 404, 406) is depicted as having at least one computer
processor (408, 410, 412), computer memory (414, 416, 418), and
computer storage (420, 422, 424). Although in some embodiments the
computer memory (414, 416, 418) and the computer storage (420, 422,
424) may be part of the same hardware devices, in other embodiments
the computer memory (414, 416, 418) and the computer storage (420,
422, 424) may be part of different hardware devices. The
distinction between the computer memory (414, 416, 418) and the
computer storage (420, 422, 424) in this particular example may be
that the computer memory (414, 416, 418) is physically proximate to
the computer processors (408, 410, 412) and may store computer
program instructions that are executed by the computer processors
(408, 410, 412), while the computer storage (420, 422, 424) is
embodied as non-volatile storage for storing user data, metadata
describing the user data, and so on. Referring to the example above
in FIG. 1A, for example, the computer processors (408, 410, 412)
and computer memory (414, 416, 418) for a particular storage system
(402, 404, 406) may reside within one of more of the controllers
(110A-110D) while the attached storage devices (171A-171F) may
serve as the computer storage (420, 422, 424) within a particular
storage system (402, 404, 406).
[0146] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may attach to one or more pods (430, 432)
according to some embodiments of the present disclosure. Each of
the pods (430, 432) depicted in FIG. 4 can include a dataset (426,
428). For example, a first pod (430) that three storage systems
(402, 404, 406) have attached to includes a first dataset (426)
while a second pod (432) that two storage systems (404, 406) have
attached to includes a second dataset (428). In such an example,
when a particular storage system attaches to a pod, the pod's
dataset is copied to the particular storage system and then kept up
to date as the dataset is modified. Storage systems can be removed
from a pod, resulting in the dataset being no longer kept up to
date on the removed storage system. In the example depicted in FIG.
4, any storage system which is active for a pod (it is an
up-to-date, operating, non-faulted member of a non-faulted pod) can
receive and process requests to modify or read the pod's
dataset.
[0147] In the example depicted in FIG. 4, each pod (430, 432) may
also include a set of managed objects and management operations, as
well as a set of access operations to modify or read the dataset
(426, 428) that is associated with the particular pod (430, 432).
In such an example, the management operations may modify or query
managed objects equivalently through any of the storage systems.
Likewise, access operations to read or modify the dataset may
operate equivalently through any of the storage systems. In such an
example, while each storage system stores a separate copy of the
dataset as a proper subset of the datasets stored and advertised
for use by the storage system, the operations to modify managed
objects or the dataset performed and completed through any one
storage system are reflected in subsequent management objects to
query the pod or subsequent access operations to read the
dataset.
[0148] Readers will appreciate that pods may implement more
capabilities than just a clustered synchronously replicated
dataset. For example, pods can be used to implement tenants,
whereby datasets are in some way securely isolated from each other.
Pods can also be used to implement virtual arrays or virtual
storage systems where each pod is presented as a unique storage
entity on a network (e.g., a Storage Area Network, or Internet
Protocol network) with separate addresses. In the case of a
multi-storage-system pod implementing a virtual storage system, all
physical storage systems associated with the pod may present
themselves as in some way the same storage system (e.g., as if the
multiple physical storage systems were no different than multiple
network ports into a single storage system).
[0149] Readers will appreciate that pods may also be units of
administration, representing a collection of volumes, file systems,
object/analytic stores, snapshots, and other administrative
entities, where making administrative changes (e.g., name changes,
property changes, managing exports or permissions for some part of
the pod's dataset), on any one storage system is automatically
reflected to all active storage systems associated with the pod. In
addition, pods could also be units of data collection and data
analysis, where performance and capacity metrics are presented in
ways that aggregate across all active storage systems for the pod,
or that call out data collection and analysis separately for each
pod, or perhaps presenting each attached storage system's
contribution to the incoming content and performance for each a
pod.
[0150] One model for pod membership may be defined as a list of
storage systems, and a subset of that list where storage systems
are considered to be in-sync for the pod. A storage system may be
considered to be in-sync for a pod if it is at least within a
recovery of having identical idle content for the last written copy
of the dataset associated with the pod. Idle content is the content
after any in-progress modifications have completed with no
processing of new modifications. Sometimes this is referred to as
"crash recoverable" consistency. Recovery of a pod carries out the
process of reconciling differences in applying concurrent updates
to in-sync storage systems in the pod. Recovery can resolve any
inconsistencies between storage systems in the completion of
concurrent modifications that had been requested to various members
of the pod but that were not signaled to any requestor as having
completed successfully. Storage systems that are listed as pod
members but that are not listed as in-sync for the pod can be
described as "detached" from the pod. Storage systems that are
listed as pod members, are in-sync for the pod, and are currently
available for actively serving data for the pod are "online" for
the pod.
[0151] Each storage system member of a pod may have its own copy of
the membership, including which storage systems it last knew were
in-sync, and which storage systems it last knew comprised the
entire set of pod members. To be online for a pod, a storage system
must consider itself to be in-sync for the pod and must be
communicating with all other storage systems it considers to be
in-sync for the pod. If a storage system can't be certain that it
is in-sync and communicating with all other storage systems that
are in-sync, then it must stop processing new incoming requests for
the pod (or must complete them with an error or exception) until it
can be certain that it is in-sync and communicating with all other
storage systems that are in-sync. A first storage system may
conclude that a second paired storage system should be detached,
which will allow the first storage system to continue since it is
now in-sync with all storage systems now in the list. But, the
second storage system must be prevented from concluding,
alternatively, that the first storage system should be detached and
with the second storage system continuing operation. This would
result in a "split brain" condition that can lead to irreconcilable
datasets, dataset corruption, or application corruption, among
other dangers.
[0152] The situation of needing to determine how to proceed when
not communicating with paired storage systems can arise while a
storage system is running normally and then notices lost
communications, while it is currently recovering from some previous
fault, while it is rebooting or resuming from a temporary power
loss or recovered communication outage, while it is switching
operations from one set of storage system controller to another set
for whatever reason, or during or after any combination of these or
other kinds of events. In fact, any time a storage system that is
associated with a pod can't communicate with all known non-detached
members, the storage system can either wait briefly until
communications can be established, go offline and continue waiting,
or it can determine through some means that it is safe to detach
the non-communicating storage system without risk of incurring a
split brain due to the non-communicating storage system concluding
the alternative view, and then continue. If a safe detach can
happen quickly enough, the storage system can remain online for the
pod with little more than a short delay and with no resulting
application outages for applications that can issue requests to the
remaining online storage systems.
[0153] One example of this situation is when a storage system may
know that it is out-of-date. That can happen, for example, when a
first storage system is first added to a pod that is already
associated with one or more storage systems, or when a first
storage system reconnects to another storage system and finds that
the other storage system had already marked the first storage
system as detached. In this case, this first storage system will
simply wait until it connects to some other set of storage systems
that are in-sync for the pod.
[0154] This model demands some degree of consideration for how
storage systems are added to or removed from pods or from the
in-sync pod members list. Since each storage system will have its
own copy of the list, and since two independent storage systems
can't update their local copy at exactly the same time, and since
the local copy is all that is available on a reboot or in various
fault scenarios, care must be taken to ensure that transient
inconsistencies don't cause problems. For example, if one storage
systems is in-sync for a pod and a second storage system is added,
then if the second storage system is updated to list both storage
systems as in-sync first, then if there is a fault and a restart of
both storage systems, the second might startup and wait to connect
to the first storage system while the first might be unaware that
it should or could wait for the second storage system. If the
second storage system then responds to an inability to connect with
the first storage system by going through a process to detach it,
then it might succeed in completing a process that the first
storage system is unaware of, resulting in a split brain. As such,
it may be necessary to ensure that storage systems won't disagree
inappropriately on whether they might opt to go through a detach
process if they aren't communicating.
[0155] One way to ensure that storage systems won't disagree
inappropriately on whether they might opt to go through a detach
process if they aren't communicating is to ensure that when adding
a new storage system to the in-sync member list for a pod, the new
storage system first stores that it is a detached member (and
perhaps that it is being added as an in-sync member). Then, the
existing in-sync storage systems can locally store that the new
storage system is an in-sync pod member before the new storage
system locally stores that same fact. If there is a set of reboots
or network outages prior to the new storage system storing its
in-sync status, then the original storage systems may detach the
new storage system due to non-communication, but the new storage
system will wait. A reverse version of this change might be needed
for removing a communicating storage system from a pod: first the
storage system being removed stores that it is no longer in-sync,
then the storage systems that will remain store that the storage
system being removed is no longer in-sync, then all storage systems
delete the storage system being removed from their pod membership
lists. Depending on the implementation, an intermediate persisted
detached state may not be necessary. Whether or not care is
required in local copies of membership lists may depend on the
model storage systems use for monitoring each other or for
validating their membership. If a consensus model is used for both,
or if an external system (or an external distributed or clustered
system) is used to store and validate pod membership, then
inconsistencies in locally stored membership lists may not
matter.
[0156] When communications fail or one or several storage systems
in a pod fail, or when a storage system starts up (or fails over to
a secondary controller) and can't communicate with paired storage
systems for a pod, and it is time for one or more storage systems
to decide to detach one or more paired storage systems, some
algorithm or mechanism must be employed to decide that it is safe
to do so and to follow through on the detach. One means of
resolving detaches is use a majority (or quorum) model for
membership. With three storage systems, as long as two are
communicating, they can agree to detach a third storage system that
isn't communicating, but that third storage system cannot by itself
choose to detach either of the other two. Confusion can arise when
storage system communication is inconsistent. For example, storage
system A might be communicating with storage system B but not C,
while storage system B might be communicating with both A and C.
So, A and B could detach C, or B and C could detach A, but more
communication between pod members may be needed to figure this
out.
[0157] Care needs to be taken in a quorum membership model when
adding and removing storage systems. For example, if a fourth
storage system is added, then a "majority" of storage systems is at
that point three. The transition from three storage systems (with
two required for majority) to a pod including a fourth storage
system (with three required for majority) may require something
similar to the model described previously for carefully adding a
storage system to the in-sync list. For example, the fourth storage
system might start in an attaching state but not yet attached where
it would never instigate a vote over quorum. Once in that state,
the original three pod members could each be updated to be aware of
the fourth member and the new requirement for a three storage
system majority to detach a fourth. Removing a storage system from
a pod might similarly move that storage system to a locally stored
"detaching" state before updating other pod members. A variant
scheme for this is to use a distributed consensus mechanism such as
PAXOS or RAFT to implement any membership changes or to process
detach requests.
[0158] Another means of managing membership transitions is to use
an external system that is outside of the storage systems
themselves to handle pod membership. In order to become online for
a pod, a storage system must first contact the external pod
membership system to verify that it is in-sync for the pod. Any
storage system that is online for a pod should then remain in
communication with the pod membership system and should wait or go
offline if it loses communication. An external pod membership
manager could be implemented as a highly available cluster using
various cluster tools, such as Oracle RAC, Linux HA, VERITAS
Cluster Server, IBM's HACMP, or others. An external pod membership
manager could also use distributed configuration tools such as Etcd
or Zookeeper, or a reliable distributed database such as Amazon's
DynamoDB.
[0159] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may receive a request to read a portion of
the dataset (426, 428) and process the request to read the portion
of the dataset locally according to some embodiments of the present
disclosure. Readers will appreciate that although requests to
modify (e.g., a write operation) the dataset (426, 428) require
coordination between the storage systems (402, 404, 406) in a pod,
as the dataset (426, 428) should be consistent across all storage
systems (402, 404, 406) in a pod, responding to a request to read a
portion of the dataset (426, 428) does not require similar
coordination between the storage systems (402, 404, 406). As such,
a particular storage system that receives a read request may
service the read request locally by reading a portion of the
dataset (426, 428) that is stored within the storage system's
storage devices, with no synchronous communication with other
storage systems in the pod. Read requests received by one storage
system for a replicated dataset in a replicated cluster are
expected to avoid any communication in the vast majority of cases,
at least when received by a storage system that is running within a
cluster that is also running nominally. Such reads should normally
be processed simply by reading from the local copy of a clustered
dataset with no further interaction required with other storage
systems in the cluster.
[0160] Readers will appreciate that the storage systems may take
steps to ensure read consistency such that a read request will
return the same result regardless of which storage system processes
the read request. For example, the resulting clustered dataset
content for any set of updates received by any set of storage
systems in the cluster should be consistent across the cluster, at
least at any time updates are idle (all previous modifying
operations have been indicated as complete and no new update
requests have been received and processed in any way). More
specifically, the instances of a clustered dataset across a set of
storage systems can differ only as a result of updates that have
not yet completed. This means, for example, that any two write
requests which overlap in their volume block range, or any
combination of a write request and an overlapping snapshot,
compare-and-write, or virtual block range copy, must yield a
consistent result on all copies of the dataset. Two operations
should not yield a result as if they happened in one order on one
storage system and a different order on another storage system in
the replicated cluster.
[0161] Furthermore, read requests can be made time order
consistent. For example, if one read request is received on a
replicated cluster and completed and that read is then followed by
another read request to an overlapping address range which is
received by the replicated cluster and where one or both reads in
any way overlap in time and volume address range with a
modification request received by the replicated cluster (whether
any of the reads or the modification are received by the same
storage system or a different storage system in the replicated
cluster), then if the first read reflects the result of the update
then the second read should also reflect the results of that
update, rather than possibly returning data that preceded the
update. If the first read does not reflect the update, then the
second read can either reflect the update or not. This ensures that
between two read requests "time" for a data segment cannot roll
backward.
[0162] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may also detect a disruption in data
communications with one or more of the other storage systems and
determine whether to the particular storage system should remain in
the pod. A disruption in data communications with one or more of
the other storage systems may occur for a variety of reasons. For
example, a disruption in data communications with one or more of
the other storage systems may occur because one of the storage
systems has failed, because a network interconnect has failed, or
for some other reason. An important aspect of synchronous
replicated clustering is ensuring that any fault handling doesn't
result in unrecoverable inconsistencies, or any inconsistency in
responses. For example, if a network fails between two storage
systems, at most one of the storage systems can continue processing
newly incoming I/O requests for a pod. And, if one storage system
continues processing, the other storage system can't process any
new requests to completion, including read requests.
[0163] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may also determine whether the particular
storage system should remain in the pod in response to detecting a
disruption in data communications with one or more of the other
storage systems. As mentioned above, to be `online` as part of a
pod, a storage system must consider itself to be in-sync for the
pod and must be communicating with all other storage systems it
considers to be in-sync for the pod. If a storage system can't be
certain that it is in-sync and communicating with all other storage
systems that are in-sync, then it may stop processing new incoming
requests to access the dataset (426, 428). As such, the storage
system may determine whether to the particular storage system
should remain online as part of the pod, for example, by
determining whether it can communicate with all other storage
systems it considers to be in-sync for the pod (e.g., via one or
more test messages), by determining whether the all other storage
systems it considers to be in-sync for the pod also consider the
storage system to be attached to the pod, through a combination of
both steps where the particular storage system must confirm that it
can communicate with all other storage systems it considers to be
in-sync for the pod and that all other storage systems it considers
to be in-sync for the pod also consider the storage system to be
attached to the pod, or through some other mechanism.
[0164] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may also keep the dataset on the particular
storage system accessible for management and dataset operations in
response to determining that the particular storage system should
remain in the pod. The storage system may keep the dataset (426,
428) on the particular storage system accessible for management and
dataset operations, for example, by accepting requests to access
the version of the dataset (426, 428) that is stored on the storage
system and processing such requests, by accepting and processing
management operations associated with the dataset (426, 428) that
are issued by a host or authorized administrator, by accepting and
processing management operations associated with the dataset (426,
428) that are issued by one of the other storage systems, or in
some other way.
[0165] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may, however, make the dataset on the
particular storage system inaccessible for management and dataset
operations in response to determining that the particular storage
system should not remain in the pod. The storage system may make
the dataset (426, 428) on the particular storage system
inaccessible for management and dataset operations, for example, by
rejecting requests to access the version of the dataset (426, 428)
that is stored on the storage system, by rejecting management
operations associated with the dataset (426, 428) that are issued
by a host or other authorized administrator, by rejecting
management operations associated with the dataset (426, 428) that
are issued by one of the other storage systems in the pod, or in
some other way.
[0166] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may also detect that the disruption in data
communications with one or more of the other storage systems has
been repaired and make the dataset on the particular storage system
accessible for management and dataset operations. The storage
system may detect that the disruption in data communications with
one or more of the other storage systems has been repaired, for
example, by receiving a message from the one or more of the other
storage systems. In response to detecting that the disruption in
data communications with one or more of the other storage systems
has been repaired, the storage system may make the dataset (426,
428) on the particular storage system accessible for management and
dataset operations once the previously detached storage system has
been resynchronized with the storage systems that remained attached
to the pod.
[0167] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may also go offline from the pod such that
the particular storage system no longer allows management and
dataset operations. The depicted storage systems (402, 404, 406)
may go offline from the pod such that the particular storage system
no longer allows management and dataset operations for a variety of
reasons. For example, the depicted storage systems (402, 404, 406)
may also go offline from the pod due to some fault with the storage
system itself, because an update or some other maintenance is
occurring on the storage system, due to communications faults, or
for many other reasons. In such an example, the depicted storage
systems (402, 404, 406) may subsequently update the dataset on the
particular storage system to include all updates to the dataset
since the particular storage system went offline and go back online
with the pod such that the particular storage system allows
management and dataset operations, as will be described in greater
detail in the resynchronization sections included below.
[0168] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may also identifying a target storage
system for asynchronously receiving the dataset, where the target
storage system is not one of the plurality of storage systems
across which the dataset is synchronously replicated. Such a target
storage system may represent, for example, a backup storage system,
as some storage system that makes use of the synchronously
replicated dataset, and so on. In fact, synchronous replication can
be leveraged to distribute copies of a dataset closer to some rack
of servers, for better local read performance. One such case is
smaller top-of-rack storage systems symmetrically replicated to
larger storage systems that are centrally located in the data
center or campus and where those larger storage systems are more
carefully managed for reliability or are connected to external
networks for asynchronous replication or backup services.
[0169] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may also identify a portion of the dataset
that is not being asynchronously replicated to the target storage
system by any of the other storages systems and asynchronously
replicate, to the target storage system, the portion of the dataset
that is not being asynchronously replicated to the target storage
system by any of the other storages systems, wherein the two or
more storage systems collectively replicate the entire dataset to
the target storage system. In such a way, the work associated with
asynchronously replicating a particular dataset may be split
amongst the members of a pod, such that each storage system in a
pod is only responsible for asynchronously replicating a subset of
a dataset to the target storage system.
[0170] In the example depicted in FIG. 4, the depicted storage
systems (402, 404, 406) may also detach from the pod, such that the
particular storage system that detaches from the pod is no longer
included in the set of storage systems across which the dataset is
synchronously replicated. For example, if storage system (404) in
FIG. 4 detached from the pod (430) illustrated in FIG. 4, the pod
(430) would only include storage systems (402, 406) as the storage
systems across which the dataset (426) that is included in the pod
(430) would be synchronously replicated across. In such an example,
detaching the storage system from the pod could also include
removing the dataset from the particular storage system that
detached from the pod. Continuing with the example where the
storage system (404) in FIG. 4 detached from the pod (430)
illustrated in FIG. 4, the dataset (426) that is included in the
pod (430) could be deleted or otherwise removed from the storage
system (404).
[0171] Readers will appreciate that there are a number of unique
administrative capabilities enabled by the pod model that can
further be supported. Also, the pod model itself introduces some
issues that can be addressed by an implementation. For example,
when a storage system is offline for a pod, but is otherwise
running, such as because an interconnect failed and another storage
system for the pod won out in mediation, there may still be a
desire or need to access the offline pod's dataset on the offline
storage system. One solution may be simply to enable the pod in
some detached mode and allow the dataset to be accessed. However,
that solution can be dangerous and that solution can cause the
pod's metadata and data to be much more difficult to reconcile when
the storage systems do regain communication. Furthermore, there
could still be a separate path for hosts to access the offline
storage system as well as the still online storage systems. In that
case, a host might issue I/O to both storage systems even though
they are no longer being kept in sync, because the host sees target
ports reporting volumes with the same identifiers and the host I/O
drivers presume it sees additional paths to the same volume. This
can result in fairly damaging data corruption as reads and writes
issued to both storage systems are no longer consistent even though
the host presumes they are. As a variant of this case, in a
clustered application, such as a shared storage clustered database,
the clustered application running on one host might be reading or
writing to one storage system and the same clustered application
running on another host might be reading or writing to the
"detached" storage system, yet the two instances of the clustered
application are communicating between each other on the presumption
that the dataset they each see is entirely consistent for completed
writes. Since they aren't consistent, that presumption is violated
and the application's dataset (e.g., the database) can quickly end
up being corrupted.
[0172] One way to solve both of these problems is to allow for an
offline pod, or perhaps a snapshot of an offline pod, to be copied
to a new pod with new volumes that have sufficiently new identities
that host I/O drivers and clustered applications won't confuse the
copied volumes as being the same as the still online volumes on
another storage system. Since each pod maintains a complete copy of
the dataset, which is crash consistent but perhaps slightly
different from the copy of the pod dataset on another storage
system, and since each pod has an independent copy of all data and
metadata needed to operate on the pod content, it is a
straightforward problem to make a virtual copy of some or all
volumes or snapshots in the pod to new volumes in a new pod. In a
logical extent graph implementation, for example, all that is
needed is to define new volumes in a new pod which reference
logical extent graphs from the copied pod associated with the pod's
volumes or snapshots, and with the logical extent graphs being
marked as copy on write. The new volumes should be treated as new
volumes, similarly to how volume snapshots copied to a new volume
might be implemented. Volumes may have the same administrative
name, though within a new pod namespace. But, they should have
different underlying identifiers, and differing logical unit
identifiers from the original volumes.
[0173] In some cases it may be possible to use virtual network
isolation techniques (for example, by creating a virtual LAN in the
case of IP networks or a virtual SAN in the case of fiber channel
networks) in such a way that isolation of volumes presented to some
interfaces can be assured to be inaccessible from host network
interfaces or host SCSI initiator ports that might also see the
original volumes. In such cases, it may be safe to provide the
copies of volumes with the same SCSI or other storage identifiers
as the original volumes. This could be used, for example, in cases
where the applications expect to see a particular set of storage
identifiers in order to function without an undue burden in
reconfiguration.
[0174] Some of the techniques described herein could also be used
outside of an active fault context to test readiness for handling
faults. Readiness testing (sometimes referred to as "fire drills")
is commonly required for disaster recovery configurations, where
frequent and repeated testing is considered a necessity to ensure
that most or all aspects of a disaster recovery plan are correct
and account for any recent changes to applications, datasets, or
changes in equipment. Readiness testing should be non-disruptive to
current production operations, including replication. In many cases
the real operations can't actually be invoked on the active
configuration, but a good way to get close is to use storage
operations to make copies of production datasets, and then perhaps
couple that with the use of virtual networking, to create an
isolated environment containing all data that is believed necessary
for the important applications that must be brought up successfully
in cases of disasters. Making such a copy of a synchronously
replicated (or even an asynchronously replicated) dataset available
within a site (or collection of sites) that is expected to perform
a disaster recovery readiness test procedure and then starting the
important applications on that dataset to ensure that it can
startup and function is a great tool, since it helps ensure that no
important parts of the application datasets were left out in the
disaster recovery plan. If necessary, and practical, this could be
coupled with virtual isolated networks coupled perhaps with
isolated collection of physical or virtual machines, to get as
close as possible to a real world disaster recovery takeover
scenario. Virtually copying a pod (or set of pods) to another pod
as a point-in-time image of the pod datasets immediately creates an
isolated dataset that contains all the copied elements and that can
then be operated on essentially identically to the originally pods,
as well as allowing isolation to a single site (or a few sites)
separately from the original pod. Further, these are fast
operations and they can be torn down and repeated easily allowing
testing to repeated as often as is desired.
[0175] Some enhancements could be made to get further toward
perfect disaster recovery testing. For example, in conjunction with
isolated networks, SCSI logical unit identities or other types of
identities could be copied into the target pod so that the test
servers, virtual machines, and applications see the same
identities. Further, the administrative environment of the servers
could be configured to respond to requests from a particular
virtual set of virtual networks to respond to requests and
operations on the original pod name so scripts don't require use of
test-variants with alternate "test" versions of object names. A
further enhancement can be used in cases where the host-side server
infrastructure that will take over in the case of a disaster
takeover can be used during a test. This includes cases where a
disaster recovery data center is completely stocked with
alternative server infrastructure that won't generally be used
until directed to do so by a disaster. It also includes cases where
that infrastructure might be used for non-critical operations (for
example, running analytics on production data, or simply supporting
application development or other functions which may be important
but can be halted if needed for more critical functions).
Specifically, host definitions and configurations and the server
infrastructure that will use them can be set up as they will be for
an actual disaster recovery takeover event and tested as part of
disaster recovery takeover testing, with the tested volumes being
connected to these host definitions from the virtual pod copy used
to provide a snapshot of the dataset. From the standpoint of the
storage systems involved, then, these host definitions and
configurations used for testing, and the volume-to-host connection
configurations used during testing, can be reused when an actual
disaster takeover event is triggered, greatly minimizing the
configuration differences between the test configuration and the
real configuration that will be used in case of a disaster recovery
takeover.
[0176] In some cases it may make sense to move volumes out of a
first pod and into a new second pod including just those volumes.
The pod membership and high availability and recovery
characteristics can then be adjusted separately, and administration
of the two resulting pod datasets can then be isolated from each
other. An operation that can be done in one direction should also
be possible in the other direction. At some point, it may make
sense to take two pods and merge them into one so that the volumes
in each of the original two pods will now track each other for
storage system membership and high availability and recovery
characteristics and events. Both operations can be accomplished
safely and with reasonably minimal or no disruption to running
applications by relying on the characteristics suggested for
changing mediation or quorum properties for a pod which were
discussed in an earlier section. With mediation, for example, a
mediator for a pod can be changed using a sequence consisting of a
step where each storage system in a pod is changed to depend on
both a first mediator and a second mediator and each is then
changed to depend only on the second mediator. If a fault occurs in
the middle of the sequence, some storage systems may depend on both
the first mediator and the second mediator, but in no case will
recovery and fault handling result in some storage systems
depending only on the first mediator and other storage systems only
depending on the second mediator. Quorum can be handled similarly
by temporarily depending on winning against both a first quorum
model and a second quorum model in order to proceed to recovery.
This may result in a very short time period where availability of
the pod in the face of faults depend on additional resources, thus
reducing potential availability, but this time period is very short
and the reduction in availability is often very little. With
mediation, if the change in mediator parameters is nothing more
than the change in the key used for mediation and the mediation
service used is the same, then the potential reduction in
availability is even less, since it now depends only on two calls
to the same service versus one call to that service, and rather
than separate calls to two separate services.
[0177] Readers will note that changing the quorum model may be
quite complex. An additional step may be necessary where storage
systems will participate in the second quorum model but won't
depend on winning in that second quorum model, which is then
followed by the step of also depending on the second quorum model.
This may be necessary to account for the fact that if only one
system has processed the change to depend on the quorum model, then
it will never win quorum since there will never be a majority. With
this model in place for changing the high availability parameters
(mediation relationship, quorum model, takeover preferences), we
can create a safe procedure for these operations to split a pod
into two or to join two pods into one. This may require adding one
other capability: linking a second pod to a first pod for high
availability such that if two pods include compatible high
availability parameters the second pod linked to the first pod can
depend on the first pod for determining and instigating
detach-related processing and operations, offline and in-sync
states, and recovery and resynchronization actions.
[0178] To split a pod into two, which is an operation to move some
volumes into a newly created pod, a distributed operation may be
formed that can be described as: form a second pod into which we
will move a set of volumes which were previously in a first pod,
copy the high availability parameters from the first pod into the
second pod to ensure they are compatible for linking, and link the
second pod to the first pod for high availability. This operation
may be encoded as messages and should be implemented by each
storage system in the pod in such a way that the storage system
ensures that the operation happens completely on that storage
system or does not happen at all if processing is interrupted by a
fault. Once all in-sync storage systems for the two pods have
processed this operation, the storage systems can then process a
subsequent operation which changes the second pod so that it is no
longer linked to the first pod. As with other changes to high
availability characteristics for a pod, this involves first having
each in-sync storage system change to rely on both the previous
model (that model being that high availability is linked to the
first pod) and the new model (that model being its own now
independent high availability). In the case of mediation or quorum,
this means that storage systems which processed this change will
first depend on mediation or quorum being achieved as appropriate
for the first pod and will additionally depend on a new separate
mediation (for example, a new mediation key) or quorum being
achieved for the second pod before the second pod can proceed
following a fault that required mediation or testing for quorum. As
with the previous description of changing quorum models, an
intermediate step may set storage systems to participate in quorum
for the second pod before the step where storage systems
participate in and depend on quorum for the second pod. Once all
in-sync storage systems have processed the change to depend on the
new parameters for mediation or quorum for both the first pod and
the second pod, the split is complete.
[0179] Joining a second pod into a first pod operates essentially
in reverse. First, the second pod must be adjusted to be compatible
with the first pod, by having an identical list of storage systems
and by having a compatible high availability model. This may
involve some set of steps such as those described elsewhere in this
paper to add or remove storage systems or to change mediator and
quorum models. Depending on implementation, it may be necessary
only to reach an identical list of storage systems. Joining
proceeds by processing an operation on each in-sync storage system
to link the second pod to the first pod for high availability. Each
storage system which processes that operation will then depend on
the first pod for high availability and then the second pod for
high availability. Once all in-sync storage systems for the second
pod have processed that operation, the storage systems will then
each process a subsequent operation to eliminate the link between
the second pod and the first pod, migrate the volumes from the
second pod into the first pod, and delete the second pod. Host or
application dataset access can be preserved throughout these
operations, as long as the implementation allows proper direction
of host or application dataset modification or read operations to
the volume by identity and as long as the identity is preserved as
appropriate to the storage protocol or storage model (for example,
as long as logical unit identifiers for volumes and use of target
ports for accessing volumes are preserved in the case of SCSI).
[0180] Migrating a volume between pods may present issues. If the
pods have an identical set of in-sync membership storage systems,
then it may be straightforward: temporarily suspend operations on
the volumes being migrated, switch control over operations on those
volumes to controlling software and structures for the new pod, and
then resume operations. This allows for a seamless migration with
continuous uptime for applications apart from the very brief
operation suspension, provided network and ports migrate properly
between pods. Depending on the implementation, suspending
operations may not even be necessary, or may be so internal to the
system that the suspension of operations has no impact. Copying
volumes between pods with different in-sync membership sets is more
of a problem. If the target pod for the copy has a subset of
in-sync members from the source pod, this isn't much of a problem:
a member storage system can be dropped safely enough without having
to do more work. But, if the target pod adds in-sync member storage
systems to the volume over the source pod, then the added storage
systems must be synchronized to include the volume's content before
they can be used. Until synchronized, this leaves the copied
volumes distinctly different from the already synchronized volumes,
in that fault handling differs and request handling from the not
yet synced member storage systems either won't work or must be
forwarded or won't be as fast because reads will have to traverse
an interconnect. Also, the internal implementation will have to
handle some volumes being in sync and ready for fault handling and
others not being in sync.
[0181] There are other problems relating to reliability of the
operation in the face of faults. Coordinating a migration of
volumes between multi-storage-system pods is a distributed
operation. If pods are the unit of fault handling and recovery, and
if mediation or quorum or whatever means are used to avoid
split-brain situations, then a switch in volumes from one pod with
a particular set of state and configurations and relationships for
fault handling, recovery, mediation and quorum to another then
storage systems in a pod have to be careful about coordinating
changes related to that handling for any volumes. Operations can't
be atomically distributed between storage systems, but must be
staged in some way. Mediation and quorum models essentially provide
pods with the tools for implementing distributed transactional
atomicity, but this may not extend to inter-pod operations without
adding to the implementation.
[0182] Consider even a simple migration of a volume from a first
pod to a second pod even for two pods that share the same first and
second storage systems. At some point the storage systems will
coordinate to define that the volume is now in the second pod and
is no longer in the first pod. If there is no inherent mechanism
for transactional atomicity across the storage systems for the two
pods, then a naive implementation could leave the volume in the
first pod on the first storage system and the second pod on the
second storage system at the time of a network fault that results
in fault handling to detach storage systems from the two pods. If
pods separately determine which storage system succeeds in
detaching the other, then the result could be that the same storage
system detaches the other storage system for both pods, in which
case the result of the volume migration recovery should be
consistent, or it could result in a different storage system
detaching the other for the two pods. If the first storage system
detaches the second storage system for the first pod and the second
storage system detaches the first storage system for the second
pod, then recovery might result in the volume being recovered to
the first pod on the first storage system and into the second pod
on the second storage system, with the volume then running and
exported to hosts and storage applications on both storage systems.
If instead the second storage system detaches the first storage
system for the first pod and first storage detaches the second
storage system for the second pod, then recovery might result in
the volume being discarded from the second pod by the first storage
system and the volume being discarded from the first pod by the
second storage system, resulting in the volume disappearing
entirely. If the pods a volume is being migrated between are on
differing sets of storage systems, then things can get even more
complicated.
[0183] A solution to these problems may be to use an intermediate
pod along with the techniques described previously for splitting
and joining pods. This intermediate pod may never be presented as
visible managed objects associated with the storage systems. In
this model, volumes to be moved from a first pod to a second pod
are first split from the first pod into a new intermediate pod
using the split operation described previously. The storage system
members for the intermediate pod can then be adjusted to match the
membership of storage systems by adding or removing storage systems
from the pod as necessary. Subsequently, the intermediate pod can
be joined with the second pod.
[0184] For further explanation, FIG. 5 sets forth a flow chart
illustrating steps that may be performed by storage systems (402,
404, 406) that support a pod according to some embodiments of the
present disclosure. Although depicted in less detail, the storage
systems (402, 404, 406) depicted in FIG. 5 may be similar to the
storage systems described above with reference to FIGS. 1A-1D,
FIGS. 2A-2G, FIGS. 3A-3B, FIG. 4, or any combination thereof. In
fact, the storage systems (402, 404, 406) depicted in FIG. 5 may
include the same, fewer, additional components as the storage
systems described above.
[0185] In the example method depicted in FIG. 5, a storage system
(402) may attach (508) to a pod. The model for pod membership may
include a list of storage systems and a subset of that list where
storage systems are presumed to be in-sync for the pod. A storage
system is in-sync for a pod if it is at least within a recovery of
having identical idle content for the last written copy of the
dataset associated with the pod. Idle content is the content after
any in-progress modifications have completed with no processing of
new modifications. Sometimes this is referred to as "crash
recoverable" consistency. Storage systems that are listed as pod
members but that are not listed as in-sync for the pod can be
described as "detached" from the pod. Storage systems that are
listed as pod members, are in-sync for the pod, and are currently
available for actively serving data for the pod are "online" for
the pod.
[0186] In the example method depicted in FIG. 5, the storage system
(402) may attach (508) to a pod, for example, by synchronizing its
locally stored version of the dataset (426) along with an
up-to-date version of the dataset (426) that is stored on other
storage systems (404, 406) in the pod that are online, as the term
is described above. In such an example, in order for the storage
system (402) to attach (508) to the pod, a pod definition stored
locally within each of the storage systems (402, 404, 406) in the
pod may need to be updated in order for the storage system (402) to
attach (508) to the pod. In such an example, each storage system
member of a pod may have its own copy of the membership, including
which storage systems it last knew were in-sync, and which storage
systems it last knew comprised the entire set of pod members.
[0187] In the example method depicted in FIG. 5, the storage system
(402) may also receive (510) a request to read a portion of the
dataset (426) and the storage system (402) may process (512) the
request to read the portion of the dataset (426) locally. Readers
will appreciate that although requests to modify (e.g., a write
operation) the dataset (426) require coordination between the
storage systems (402, 404, 406) in a pod, as the dataset (426)
should be consistent across all storage systems (402, 404, 406) in
a pod, responding to a request to read a portion of the dataset
(426) does not require similar coordination between the storage
systems (402, 404, 406). As such, a particular storage system (402)
that receives a read request may service the read request locally
by reading a portion of the dataset (426) that is stored within the
storage system's (402) storage devices, with no synchronous
communication with other storage systems (404, 406) in the pod.
Read requests received by one storage system for a replicated
dataset in a replicated cluster are expected to avoid any
communication in the vast majority of cases, at least when received
by a storage system that is running within a cluster that is also
running nominally. Such reads should normally be processed simply
by reading from the local copy of a clustered dataset with no
further interaction required with other storage systems in the
cluster
[0188] Readers will appreciate that the storage systems may take
steps to ensure read consistency such that a read request will
return the same result regardless of which storage system processes
the read request. For example, the resulting clustered dataset
content for any set of updates received by any set of storage
systems in the cluster should be consistent across the cluster, at
least at any time updates are idle (all previous modifying
operations have been indicated as complete and no new update
requests have been received and processed in any way). More
specifically, the instances of a clustered dataset across a set of
storage systems can differ only as a result of updates that have
not yet completed. This means, for example, that any two write
requests which overlap in their volume block range, or any
combination of a write request and an overlapping snapshot,
compare-and-write, or virtual block range copy, must yield a
consistent result on all copies of the dataset. Two operations
cannot yield a result as if they happened in one order on one
storage system and a different order on another storage system in
the replicated cluster.
[0189] Furthermore, read requests may be time order consistent. For
example, if one read request is received on a replicated cluster
and completed and that read is then followed by another read
request to an overlapping address range which is received by the
replicated cluster and where one or both reads in any way overlap
in time and volume address range with a modification request
received by the replicated cluster (whether any of the reads or the
modification are received by the same storage system or a different
storage system in the replicated cluster), then if the first read
reflects the result of the update then the second read should also
reflect the results of that update, rather than possibly returning
data that preceded the update. If the first read does not reflect
the update, then the second read can either reflect the update or
not. This ensures that between two read requests "time" for a data
segment cannot roll backward.
[0190] In the example method depicted in FIG. 5, the storage system
(402) may also detect (514) a disruption in data communications
with one or more of the other storage systems (404, 406). A
disruption in data communications with one or more of the other
storage systems (404, 406) may occur for a variety of reasons. For
example, a disruption in data communications with one or more of
the other storage systems (404, 406) may occur because one of the
storage systems (402, 404, 406) has failed, because a network
interconnect has failed, or for some other reason. An important
aspect of synchronous replicated clustering is ensuring that any
fault handling doesn't result in unrecoverable inconsistencies, or
any inconsistency in responses. For example, if a network fails
between two storage systems, at most one of the storage systems can
continue processing newly incoming I/O requests for a pod. And, if
one storage system continues processing, the other storage system
can't process any new requests to completion, including read
requests.
[0191] In the example method depicted in FIG. 5, the storage system
(402) may also determine (516) whether to the particular storage
system (402) should remain online as part of the pod. As mentioned
above, to be `online` as part of a pod, a storage system must
consider itself to be in-sync for the pod and must be communicating
with all other storage systems it considers to be in-sync for the
pod. If a storage system can't be certain that it is in-sync and
communicating with all other storage systems that are in-sync, then
it may stop processing new incoming requests to access the dataset
(426). As such, the storage system (402) may determine (516)
whether to the particular storage system (402) should remain online
as part of the pod, for example, by determining whether it can
communicate with all other storage systems (404, 406) it considers
to be in-sync for the pod (e.g., via one or more test messages), by
determining whether the all other storage systems (404, 406) it
considers to be in-sync for the pod also consider the storage
system (402) to be attached to the pod, through a combination of
both steps where the particular storage system (402) must confirm
that it can communicate with all other storage systems (404, 406)
it considers to be in-sync for the pod and that all other storage
systems (404, 406) it considers to be in-sync for the pod also
consider the storage system (402) to be attached to the pod, or
through some other mechanism.
[0192] In the example method depicted in FIG. 5, the storage system
(402) may also, responsive to affirmatively (518) determining that
the particular storage system (402) should remain online as part of
the pod, keep (522) the dataset (426) on the particular storage
system (402) accessible for management and dataset operations. The
storage system (402) may keep (522) the dataset (426) on the
particular storage system (402) accessible for management and
dataset operations, for example, by accepting requests to access
the version of the dataset (426) that is stored on the storage
system (402) and processing such requests, by accepting and
processing management operations associated with the dataset (426)
that are issued by a host or authorized administrator, by accepting
and processing management operations associated with the dataset
(426) that are issued by one of the other storage systems (404,
406) in the pod, or in some other way.
[0193] In the example method depicted in FIG. 5, the storage system
(402) may also, responsive to determining that the particular
storage system should not (520) remain online as part of the pod,
make (524) the dataset (426) on the particular storage system (402)
inaccessible for management and dataset operations. The storage
system (402) may make (524) the dataset (426) on the particular
storage system (402) inaccessible for management and dataset
operations, for example, by rejecting requests to access the
version of the dataset (426) that is stored on the storage system
(402), by rejecting management operations associated with the
dataset (426) that are issued by a host or other authorized
administrator, by rejecting management operations associated with
the dataset (426) that are issued by one of the other storage
systems (404, 406) in the pod, or in some other way.
[0194] In the example method depicted in FIG. 5, the storage system
(402) may also detect (526) that the disruption in data
communications with one or more of the other storage systems (404,
406) has been repaired. The storage system (402) may detect (526)
that the disruption in data communications with one or more of the
other storage systems (404, 406) has been repaired, for example, by
receiving a message from the one or more of the other storage
systems (404, 406). In response to detecting (526) that the
disruption in data communications with one or more of the other
storage systems (404, 406) has been repaired, the storage system
(402) may make (528) the dataset (426) on the particular storage
system (402) accessible for management and dataset operations.
[0195] Readers will appreciate that the example depicted in FIG. 5
describes an embodiment in which various actions are depicted as
occurring within some order, although no ordering is required.
Furthermore, other embodiments may exist where the storage system
(402) only carries out a subset of the described actions. For
example, the storage system (402) may perform the steps of
detecting (514) a disruption in data communications with one or
more of the other storage systems (404, 406), determining (516)
whether to the particular storage system (402) should remain in the
pod, keeping (522) the dataset (426) on the particular storage
system (402) accessible for management and dataset operations or
making (524) the dataset (426) on the particular storage system
(402) inaccessible for management and dataset operations without
first receiving (510) a request to read a portion of the dataset
(426) and processing (512) the request to read the portion of the
dataset (426) locally. Furthermore, the storage system (402) may
detect (526) that the disruption in data communications with one or
more of the other storage systems (404, 406) has been repaired and
make (528) the dataset (426) on the particular storage system (402)
accessible for management and dataset operations without first
receiving (510) a request to read a portion of the dataset (426)
and processing (512) the request to read the portion of the dataset
(426) locally. In fact, none of the steps described herein are
explicitly required in all embodiments as prerequisites for
performing other steps described herein.
[0196] For further explanation, FIG. 6 sets forth a flow chart
illustrating steps that may be performed by storage systems (402,
404, 406) that support a pod according to some embodiments of the
present disclosure. Although depicted in less detail, the storage
systems (402, 404, 406) depicted in FIG. 6 may be similar to the
storage systems described above with reference to FIGS. 1A-1D,
FIGS. 2A-2G, FIGS. 3A-3B, FIG. 4, or any combination thereof. In
fact, the storage systems (402, 404, 406) depicted in FIG. 6 may
include the same, fewer, additional components as the storage
systems described above.
[0197] In the example method depicted in FIG. 6, two or more of the
storage systems (402, 404) may each identify (608) a target storage
system (618) for asynchronously receiving the dataset (426). The
target storage system (618) for asynchronously receiving the
dataset (426) may be embodied, for example, as a backup storage
system that is located in a different data center than either of
the storage systems (402, 404) that are members of a particular
pod, as cloud storage that is provided by a cloud services
provider, or in many other ways. Readers will appreciate that the
target storage system (618) is not one of the plurality of storage
systems (402, 404) across which the dataset (426) is synchronously
replicated, and as such, the target storage system (618) initially
does not include an up-to-date local copy of the dataset (426).
[0198] In the example method depicted in FIG. 6, two or more of the
storage systems (402, 404) may each also identify (610) a portion
of the dataset (426) that is not being asynchronously replicated to
the target storage (618) system by any of the other storages
systems that are members of a pod that includes the dataset (426).
In such an example, the storage systems (402, 404) may each
asynchronously replicate (612), to the target storage system (618),
the portion of the dataset (426) that is not being asynchronously
replicated to the target storage system by any of the other
storages systems. Consider an example in which a first storage
system (402) is responsible for asynchronously replicating a first
portion (e.g., a first half of an address space) of the dataset
(426) to the target storage system (618). In such an example, the
second storage system (404) would be responsible for asynchronously
replicating a second portion (e.g., a second half of an address
space) of the dataset (426) to the target storage system (618),
such that the two or more storage systems (402, 404) collectively
replicate the entire dataset (426) to the target storage system
(618).
[0199] Readers will appreciate that through the use of pods, as
described above, the replication relationship between two storage
systems may be switched from a relationship where data is
asynchronously replicated to a relationship where data is
synchronously replicated. For example, if storage system A is
configured to asynchronously replicate a dataset to storage system
B, creating a pod that includes the dataset, storage system A as a
member, and storage system B as a member can switch the
relationship where data is asynchronously replicated to a
relationship where data is synchronously replicated. Likewise,
through the use of pods, the replication relationship between two
storage systems may be switched from a relationship where data is
synchronously replicated to a relationship where data is
asynchronously replicated. For example, if a pod is created that
includes the dataset, storage system A as a member, and storage
system B as a member, by merely unstretching the pod (to remove
storage system A as a member or to remove storage system B as a
member), a relationship where data is synchronously replicated
between the storage systems can immediately be switched to a
relationship where data is asynchronously replicated. In such a
way, storage systems may switch back-and-forth as needed between
asynchronous replication and synchronous replication.
[0200] This switching can be facilitated by the implementation
relying on similar techniques for both synchronous and asynchronous
replication. For example, if resynchronization for a synchronously
replicated dataset relies on the same or a compatible mechanism as
is used for asynchronous replication, then switching to
asynchronous replication is conceptually identical to dropping the
in-sync state and leaving a relationship in a state similar to a
"perpetual recovery" mode. Likewise, switching from asynchronous
replication to synchronous replication can operate conceptually by
"catching up" and becoming in-sync just as is done when completing
a resynchronization with the switching system becoming an in-sync
pod member.
[0201] Alternatively, or additionally, if both synchronous and
asynchronous replication rely on similar or identical common
metadata, or a common model for representing and identifying
logical extents or stored block identities, or a common model for
representing content-addressable stored blocks, then these aspects
of commonality can be leveraged to dramatically reduce the content
that may need to be transferred when switching to and from
synchronous and asynchronous replication. Further, if a dataset is
asynchronously replicated from a storage system A to a storage
system B, and system B further asynchronously replicates that data
set to a storage system C, then a common metadata model, common
logical extent or block identities, or common representation of
content-addressable stored blocks, can dramatically reduce the data
transfers needed to enable synchronous replication between storage
system A and storage system C.
[0202] Readers will further appreciate that that through the use of
pods, as described above, replication techniques may be used to
perform tasks other than replicating data. In fact, because a pod
may include a set of managed objects, tasks like migrating a
virtual machine may be carried out using pods and the replication
techniques described herein. For example, if virtual machine A is
executing on storage system A, by creating a pod that includes
virtual machine A as a managed object, storage system A as a
member, and storage system B as a member, virtual machine A and any
associated images and definitions may be migrated to storage system
B, at which time the pod could simply be destroyed, membership
could be updated, or other actions may be taken as necessary.
[0203] For further explanation, FIG. 7 sets forth a flow chart
illustrating an example method of establishing a synchronous
replication relationship between two or more storage systems (714,
724, 728) according to some embodiments of the present disclosure.
Although depicted in less detail, the storage systems (714, 724,
728) depicted in FIG. 7 may be similar to the storage systems
described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS.
3A-3B, or any combination thereof. In fact, the storage systems
(714, 724, 728) depicted in FIG. 7 may include the same, fewer,
additional components as the storage systems described above.
[0204] The example method depicted in FIG. 7 includes identifying
(702), for a dataset (712), a plurality of storage systems (714,
724, 728) across which the dataset (712) will be synchronously
replicated. The dataset (712) depicted in FIG. 7 may be embodied,
for example, as the contents of a particular volume, as the
contents of a particular shard of a volume, or as any other
collection of one or more data elements. The dataset (712) may be
synchronized across a plurality of storage systems (714, 724, 728)
such that each storage system (714, 724, 728) retains a local copy
of the dataset (712). In the examples described herein, such a
dataset (712) is synchronously replicated across the storage
systems (714, 724, 728) in such a way that the dataset (712) can be
accessed through any of the storage systems (714, 724, 728) with
performance characteristics such that any one storage system in the
cluster doesn't operate substantially more optimally than any other
storage system in the cluster, at least as long as the cluster and
the particular storage system being accessed are running nominally.
In such systems, modifications to the dataset (712) should be made
to the copy of the dataset that resides on each storage system
(714, 724, 728) in such a way that accessing the dataset (712) on
any of the storage systems (714, 724, 728) will yield consistent
results. For example, a write request issued to the dataset must be
serviced on all storage systems (714, 724, 728) or serviced on none
of the storage systems (714, 724, 728). Likewise, some groups of
operations (e.g., two write operations that are directed to same
location within the dataset) must be executed in the same order on
all storage systems (714, 724, 728) such that the copy of the
dataset that resides on each storage system (714, 724, 728) is
ultimately identical. Modifications to the dataset (712) need not
be made at the exact same time, but some actions (e.g., issuing an
acknowledgement that a write request directed to the dataset,
enabling read access to a location within the dataset that is
targeted by a write request that has not yet been completed on all
storage systems) may be delayed until the copy of the dataset (712)
on each storage system (714, 724, 728) has been modified.
[0205] In the example method depicted in FIG. 7, identifying (702),
for a dataset (712), a plurality of storage systems (714, 724, 728)
across which the dataset (712) will be synchronously replicated may
be carried out, for example, by examining a pod definition or
similar data structure that associates a dataset (712) with one or
more storage systems (714, 724, 728) which nominally store that
dataset (712). A `pod`, as the term is used here and throughout the
remainder of the present application, may be embodied as a
management entity that represents a dataset, a set of managed
objects and management operations, a set of access operations to
modify or read the dataset, and a plurality of storage systems.
Such management operations may modify or query managed objects
equivalently through any of the storage systems, where access
operations to read or modify the dataset operate equivalently
through any of the storage systems. Each storage system may store a
separate copy of the dataset as a proper subset of the datasets
stored and advertised for use by the storage system, where
operations to modify managed objects or the dataset performed and
completed through any one storage system are reflected in
subsequent management objects to query the pod or subsequent access
operations to read the dataset. Additional details regarding a
`pod` may be found in previously filed provisional patent
application No. 62/518,071, which is incorporated herein by
reference. In such an example, the pod definition may include at
least an identification of a dataset (712) and a set of storage
systems (714, 724, 728) across which the dataset (712) is
synchronously replicated. Such a pod may encapsulate some of number
of (perhaps optional) properties including symmetric access,
flexible addition/removal of replicas, high availability data
consistency, uniform user administration across storage systems in
relationship to the dataset, managed host access, application
clustering, and so on. Storage systems can be added to a pod,
resulting in the pod's dataset (712) being copied to that storage
system and then kept up to date as the dataset (712) is modified.
Storage systems can also be removed from a pod, resulting in the
dataset (712) being no longer kept up to date on the removed
storage system. In such examples, a pod definition or similar data
structure may be updated as storage systems are added to and
removed from a particular pod.
[0206] The example method depicted in FIG. 7 also includes
configuring (704) one or more data communications links (716, 718,
720) between each of the plurality of storage systems (714, 724,
728) to be used for synchronously replicating the dataset (712). In
the example method depicted in FIG. 6, the storage systems (714,
724, 728) in a pod must communicate with each other both for high
bandwidth data transfer, and for cluster, status, and
administrative communication. These distinct types of communication
could be over the same data communications links (716, 718, 720)
or, in an alternative embodiment, these distinct types of
communication could be over separate data communications links
(716, 718, 720). In a cluster of dual controller storage systems,
both controllers in each storage system should have the nominal
ability to communicate with both controllers for any paired storage
systems (i.e., any other storage system in a pod).
[0207] In a primary/secondary controller design, all cluster
communication for active replication may run between primary
controllers until a fault occurs. In such systems, some
communication may occur between a primary controller and a
secondary controller, or between secondary controllers on distinct
storage systems, in order to verify that the data communications
links between such entities are operational. In other cases,
virtual network addresses might be used to limit the configuration
needed for of inter-datacenter network links, or to simplify design
of the clustered aspect of the storage system. In an active/active
controller design, cluster communications might run from all active
controllers of one storage system to some or all active controllers
in any paired storage systems, or they might be filtered through a
common switch, or they might use a virtual network address to
simplify configuration, or they might use some combination. In a
scale-out design, two or more common network switches may be used
such that all scale-out storage controllers within the storage
system connect to the network switches in order to handle data
traffic. The switches might or might not use techniques to limit
the number of exposed network addresses, so that paired storage
systems don't need to be configured with the network addresses of
all storage controllers.
[0208] In the example method depicted in FIG. 7, configuring (704)
one or more data communications links (716, 718, 720) between each
of the plurality of storage systems (714, 724, 728) to be used for
synchronously replicating the dataset (712) may be carried out, for
example, by configuring the storage systems (716, 718, 720) to
communicate via defined ports over a data communications network,
by configuring the storage systems (716, 718, 720) to communicate
over a point-to-point data communications link between two of the
storage systems (716, 724, 728), or in a variety of ways. If secure
communication is required, some form of key exchange may be needed,
or communication could be done or bootstrapped through some service
such as SSH (Secure SHell), SSL, or some other service or protocol
built around public keys or Diffie-Hellman key exchange or
reasonable alternatives. Secure communications could also be
mediated through some vendor-provided cloud service tied in some
way to customer identities. Alternately, a service configured to
run on customer facilities, such as running in a virtual machine or
container, could be used to mediate key exchanges necessary for
secure communications between replicating storage systems (716,
718, 720). Readers will appreciate that a pod including more than
two storage systems may need communication links between most or
all of the individual storage systems. In the example depicted in
FIG. 6, three data communications links (716, 718, 720) are
illustrated, although additional data communications links may
exist in other embodiments.
[0209] Readers will appreciate that communication between the
storage systems (714, 724, 728) across which the dataset (712) will
be synchronously replicated serves some number of purposes. One
purpose, for example, is to deliver data from one storage system
(714, 724, 728) to another storage system (714, 724, 728) as part
of I/O processing. For example, processing a write commonly
requires delivering the write content and some description of the
write to any paired storage systems for a pod. Another purpose
served by data communications between the storage systems (714,
724, 728) may be to communicate configuration changes and analytics
data in order to handle creating, extending, deleting or renaming
volumes, files, object buckets, and so on. Another purpose served
by data communications between the storage systems (714, 724, 728)
may be to carry out communication involved in detecting and
handling storage system and interconnect faults. This type of
communication may be time critical and may need to be prioritized
to ensure it doesn't get stuck behind a long network queue delay
when a large burst of write traffic is suddenly dumped on the
datacenter interconnect.
[0210] Readers will further appreciate that different types of
communication may use the same connections, or different
connections, and may use the same networks, or different networks,
in various combinations. Further, some communications may be
encrypted and secured while other communications might not be
encrypted. In some cases, the data communications links could be
used to forward I/O requests (either directly as the requests
themselves or as logical descriptions of the operations the I/O
requests represent) from one storage system to another. This could
be used, for example, in cases where one storage system has
up-to-date and in-sync content for a pod, and another storage
system does not currently have up-to-date and in-sync content for
the pod. In such cases, as long as the data communications links
are running, requests can be forwarded from the storage system that
is not up-to-date and in-sync to the storage system that is
up-to-date and in-sync.
[0211] The example method depicted in FIG. 7 also includes
exchanging (706), between the plurality of storage systems (714,
724, 728), timing information (710, 722, 726) for at least one of
the plurality of storage systems (714, 724, 728). In the example
method depicted in FIG. 6, timing information (710, 722, 726) for a
particular storage system (714, 724, 728) may be embodied, for
example, as the value of a clock within the storage system (714,
724, 728). In an alternative embodiment, the timing information
(710, 722, 726) for a particular storage system (714, 724, 728) may
be embodied as a value which serves as a proxy for a clock value.
The value which serves as a proxy for a clock value may be included
in a token that is exchanged between the storage systems. Such a
value which serves as a proxy for a clock value may be embodied,
for example, a sequence number that a particular storage system
(714, 724, 728) or storage system controller can internally record
as having been sent at a particular time. In such an example, if
the token (e.g., the sequence number) is received back, the
associated clock value can be found and utilized as the basis for
determining whether a valid lease is still in place. In the example
method depicted in FIG. 6, exchanging (706) timing information
(710, 722, 726) for at least one of the plurality of storage
systems (714, 724, 728) between the plurality of storage systems
(714, 724, 728) may be carried out, for example, by each storage
system (714, 724, 728) sending timing information to each other
storage system (714, 724, 728) in a pod on a periodic basis, on
demand, within a predetermined amount of time after a lease is
established, within a predetermined amount of time before a lease
is set to expire, as part of an attempt to initiate or re-establish
a synchronous replication relationship, or in some other way.
[0212] The example method depicted in FIG. 7 also includes
establishing (708), in dependence upon the timing information (710,
722, 726) for at least one of the plurality of storage systems
(714, 724, 728), a synchronous replication lease, the synchronous
replication lease identifying a period of time during which the
synchronous replication relationship is valid. In the example
method depicted in FIG. 7, a synchronous replication relationship
is formed as a set of storage systems (714, 724, 728) that
replicate some dataset (712) between these largely independent
stores, where each storage systems (714, 724, 728) has its own copy
and its own separate internal management of relevant data
structures for defining storage objects, for mapping objects to
physical storage, for deduplication, for defining the mapping of
content to snapshots, and so on. A synchronous replication
relationship can be specific to a particular dataset, such that a
particular storage system (714, 724, 728) may be associated with
more than one synchronous replication relationship, where each
synchronous replication relationship is differentiated by the
dataset being described and may further consist of a different set
of additional member storage systems.
[0213] In the example method depicted in FIG. 7, a synchronous
replication lease may be established (708) in dependence upon the
timing information (710, 722, 726) for at least one of the
plurality of storage systems (714, 724, 728) in a variety of
different ways. In one embodiment, the storage systems may
establish (708) a synchronous replication lease by utilizing the
timing information (710, 722, 726) for each of the plurality of
storage systems (714, 724, 728) to coordinate clocks. In such an
example, once the clocks are coordinated for each of the storage
systems (714, 724, 728), the storage system may establish (708) a
synchronous replication lease that extends for a predetermined
period of time beyond the coordinated clock values. For example, if
the clocks for each storage system (714, 724, 728) are coordinated
to be at a value of X, the storage systems (714, 724, 728) may each
be configured to establish a synchronous replication lease that is
valid until X+2 seconds.
[0214] In an alternative embodiment, the need to coordinate clocks
between the storage systems (714, 724, 728) may be avoided while
still achieving a timing guarantee. In such an embodiment, a
storage controller within each storage system (714, 724, 728) may
have a local monotonically increasing clock. A synchronous
replication lease may be established (708) between storage
controllers (such as a primary controller in one storage system
communicating with a primary controller in a paired storage system)
by each controller sending its clock value to the other storage
controllers along with the last clock value it received from the
other storage controller. When a particular controller receives
back its clock value from another controller, it adds some agreed
upon lease interval to that received clock value and uses that to
establish (708) its local synchronous replication lease. In such a
way, the synchronous replication lease may be calculated in
dependence upon a value of a local clock that was received from
another storage system.
[0215] Consider an example in which a storage controller in a first
storage system (714) is communicating with a storage controller in
a second storage system (724). In such an example, assume that the
value of the monotonically increasing clock for the storage
controller in the first storage system (714) is 1000 milliseconds.
Further assume that the storage controller in the first storage
system (714) sends a message to the storage controller in the
second storage system (724) indicating that its clock value at the
time that the message was generated was 1000 milliseconds. In such
an example, assume that 500 milliseconds after the storage
controller in the first storage system (714) sent a message to the
storage controller in the second storage system (724) indicating
that its clock value at the time that the message was generated was
1000 milliseconds, the storage controller in the first storage
system (714) receives a message from the storage controller in a
second storage system (724) indicating that: 1) the value of the
monotonically increasing clock in the storage controller in the
second storage system (724) was at a value of 5000 milliseconds
when the message was generated, and 2) the last value of the
monotonically increasing clock in the storage controller in the
first storage system (714) that was received by the second storage
system (724) was 1000 milliseconds. In such an example, if the
agreed upon lease interval is 2000 milliseconds, the first storage
system (714) will establish (708) a synchronous replication lease
that is valid until the monotonically increasing clock for the
storage controller in the first storage system (714) is at a value
of 3000 milliseconds. If the storage controller in the first
storage system (714) does not receive a message from the storage
controller in the second storage system (724) that includes an
updated value of the monotonically increasing clock for the storage
controller in the first storage system (714) by the time that the
monotonically increasing clock for the storage controller in the
first storage system (714) reaches a value of 3000 milliseconds,
the first storage system (714) will treat the synchronous
replication lease to have expired and may take various actions as
described in greater detail below. Readers will appreciate that
storage controllers within the remaining storage systems (724, 728)
in a pod may react similarly and perform a similar tracking and
updating of the synchronous replication lease. Essentially, the
receiving controller can be assured that the network and the paired
controllers were running somewhere during that time interval, and
it can be assured that the paired controller received a message
that it sent somewhere during that time interval. Without any
coordination in clocks, the receiving controller can't know exactly
where in that time interval the network and the paired controller
were running, and can't really know if there were queue delays in
sending its clock value or in receiving back its clock value.
[0216] In a pod consisting of two storage systems, each with a
simple primary controller, where the primary controllers are
exchanging clocks as part of their cluster communication, each
primary controller can use the activity lease to put a bound on
when it won't know for certain that the paired controller was
running. At the point it becomes uncertain (when the controller's
connection's activity lease has expired), it can start sending
messages indicating that it is uncertain and that a properly
synchronized connection must be reestablished before activity
leases can again be resumed. These messages may be received and
responses may not be received, if the network is working in one
direction but is not working properly in the other direction. This
may be the first indication by a running paired controller that the
connection isn't running normally, because its own activity lease
may not yet have expired, due to a different combination of lost
messages and queue delays. As a result, if such a message is
received, it should also consider its own activity lease to be
expired, and it should start sending messages of its own attempting
to coordinate synchronizing the connection and resuming of activity
leases. Until that happens and a new set of clock exchanges can
succeed, neither controller can consider its activity lease to be
valid.
[0217] In this model, a controller can wait for lease interval
seconds after it started sending reestablish messages, and if it
hasn't received a response, it can be assured that either the
paired controller is down or the paired controller's own lease for
the connection will have expired. To handle minor amounts of clock
drift, it may wait slightly longer than the lease interval (i.e., a
reestablishment lease). When a controller receives a reestablish
message, it could consider the reestablishment lease to be expired
immediately, rather than waiting (since it knows that the sending
controller's activity lease has expired), but it will often make
sense to attempt further messaging before giving up, in case
message loss was a temporary condition caused, for example, by a
congested network switch.
[0218] In an alternative embodiment, in addition to establishing a
synchronous replication lease, a cluster membership lease may also
be established upon receipt of a clock value from a paired storage
system or upon receipt back of a clock exchanged with a paired
storage system. In such an example, each storage system may have
its own synchronous replication lease and its own cluster
membership lease with every paired storage system. The expiration
of a synchronous replication lease with any pair may result in
paused processing. Cluster membership, however, cannot be
recalculated until the cluster membership lease has expired with
all pairs. As such, the duration of the cluster membership lease
should be set, based on the message and clock value interactions,
to ensure that the cluster membership lease with a pair will not
expire until after a pair's synchronous replication link for that
link has expired. Readers will appreciate that a cluster membership
lease can be established by each storage system in a pod and may be
associated with a communication link between any two storage
systems that are members of the pod. Furthermore, the cluster
membership lease may extend after the expiration of the synchronous
replication lease for a duration of time that is at least as long
as the time period for expiration of the synchronous replication
lease. The cluster membership lease may be extended on receipt of a
clock value received from a paired storage system as part of a
clock exchange, where the cluster membership lease period from the
current clock value may be at least as long as the period
established for the last synchronous replication lease extension
based on exchanged clock values. In additional embodiments,
additional cluster membership information can be exchanged over a
connection, including when a session is first negotiated. Readers
will appreciate that in embodiments that utilize a cluster
membership lease, each storage system (or storage controller) may
have its own value for the cluster membership lease. Such a lease
should not expire until it can be assured that all synchronous
replication leases across all pod members will have expired given
that the cluster lease expiration allows establishing new
membership such as through a mediator race and the synchronous
replication lease expiration forces processing of new requests to
pause. In such an example, the pause must be assured to be in place
everywhere before cluster membership actions can be taken.
[0219] Readers will appreciate that although only one of the
storage systems (714) is depicted as identifying (702), for a
dataset (712), a plurality of storage systems (714, 724, 728)
across which the dataset (712) will be synchronously replicated,
configuring (704) one or more data communications links (716, 718,
720) between each of the plurality of storage systems (714, 724,
728) to be used for synchronously replicating the dataset (712),
exchanging (706), between the plurality of storage systems (714,
724, 728), timing information (710, 722, 726) for at least one of
the plurality of storage systems (714, 724, 728), and establishing
(708), in dependence upon the timing information (710, 722, 726)
for at least one of the plurality of storage systems (714, 724,
728), a synchronous replication lease, the remaining storage
systems (724, 728) may also carry out such steps. In fact, all
three storage systems (714, 724, 728) may carry out one or more of
the steps described above at the same time, as establishing a
synchronous replication relationship between two or more storage
systems (714, 724, 728) may require collaboration and interaction
between two or more storage systems (714, 724, 728).
[0220] For further explanation, FIG. 8 sets forth an example of a
hybrid storage system for synchronously replicating datasets across
a cloud-based storage system (803) and multiple, physical storage
systems (402, 404 . . . 406) implemented similarly to the storage
systems described in FIGS. 4-7, and in accordance with some
embodiments of the present disclosure.
[0221] In the example depicted in FIG. 8, the cloud-based storage
system (803) is created entirely in a cloud computing environment
(852) such as, for example, Amazon Web Services (`AWS`), Microsoft
Azure, Google Cloud Platform, IBM Cloud, Oracle Cloud, and others.
The cloud-based storage system (803) may be used to provide
services similar to the services that may be provided by the
storage systems described above.
[0222] Further, in this example, the physical storage systems (402,
404 . . . 406) may include some or all of the components described
above with reference to FIGS. 1A-7.
[0223] As depicted in the hybrid configuration of FIG. 8, one or
more datasets (426, 428) may be synchronously replicated among the
physical storage systems (402, 404 . . . 406) and also among the
cloud-based storage system (803) implemented within a remote cloud
computing environment (852) that is connected across a network
(850), such as the Internet. In this example, there are two
datasets (426, 428) that correspond to two respective pods (853,
854), where the pods (853, 854) include all features and
functionality described above with reference to FIGS. 1A-7.
[0224] However, in other examples, not depicted, a hybrid
configuration may synchronously replicate datasets across one or
more physical storage systems (402, 404 . . . 406) and also across
a virtual machine based implementation of a storage system, such as
a virtual machine based implementation of storage systems (306)
described above with reference to FIGS. 1A-7. Further, in this
example of a hybrid configuration, the virtual machine based
implementation of a storage system may be on a same network as the
physical storage systems (402, 404 . . . 406), or the virtual
machine based storage system may be at a remote location and
connected across a wide area network.
[0225] In the depiction of FIG. 8, one or more datasets (426, 428)
are synchronously replicated between the cloud-based storage system
(803) and the physical storage systems (402, 404 . . . 406)
similarly to the synchronously replicated datasets among only the
physical storage systems (402, 404 . . . 406).
[0226] However, in other examples, one or more datasets (426, 428)
may be synchronously replicated among the physical storage systems
(402, 404 . . . 406), but where the one or more datasets (426, 428)
are asynchronously stored at the cloud-based storage system
(803).
[0227] In this way, in the event of a failure or in the event of a
request to reconstruct or clone one or more pods (853, 854), the
recovery, reconstruction, or cloning may be performed using only
data stored on the cloud-based storage system (803) as a source
storage system such that a target storage system, whether virtual
of physical, may be used to resume operation of the original
storage systems within the context of any prior replication or
other relationship for the dataset.
[0228] Further, in this example, a target storage system may be one
or more physical storage systems, such as storage system (306), or
where a target storage system may be another cloud-based storage
system, such as cloud-based storage system (803).
[0229] For further explanation, FIG. 9 sets forth an example of an
additional cloud-based storage system (902) in accordance with some
embodiments of the present disclosure. In the example depicted in
FIG. 50, the cloud-based storage system (902) is created entirely
in a cloud computing environment (900) such as, for example, Amazon
Web Services (`AWS`), Microsoft Azure, Google Cloud Platform, IBM
Cloud, Oracle Cloud, and others. The cloud-based storage system
(902) may be used to provide services similar to the services that
may be provided by the storage systems described above. For
example, the cloud-based storage system (902) may be used to
provide block storage services to users of the cloud-based storage
system (902), the cloud-based storage system (902) may be used to
provide storage services to users of the cloud-based storage system
(902) through the use of solid-state storage, and so on.
[0230] The cloud-based storage system (902) depicted in FIG. 50 may
operate in a manner that is somewhat similar to the cloud-based
storage system (902) depicted in FIG. 49, as the cloud-based
storage system (902) depicted in FIG. 50 includes a storage
controller application (906) that is being executed in a cloud
computing instance (904). In the example depicted in FIG. 50,
however, the cloud computing instance (904) that executes the
storage controller application (906) is a cloud computing instance
(904) with local storage (908). In such an example, data written to
the cloud-based storage system (902) may be stored in both the
local storage (908) of the cloud computing instance (904) and also
in cloud-based object storage (910) in the same manner that the
cloud-based object storage (910) was used above. In some
embodiments, for example, the storage controller application (906)
may be responsible for writing data to the local storage (908) of
the cloud computing instance (904) while a software daemon (912)
may be responsible for ensuring that the data is written to the
cloud-based object storage (910) in the same manner that the
cloud-based object storage (910) was used above.
[0231] Readers will appreciate that a cloud-based storage system
(902) depicted in FIG. 50 may represent a less expensive, less
robust version of a cloud-based storage system than was depicted in
FIG. 49. In yet alternative embodiments, the cloud-based storage
system (902) depicted in FIG. 50 could include additional cloud
computing instances with local storage that supported the execution
of the storage controller application (906), such that failover can
occur if the cloud computing instance (904) that executes the
storage controller application (906) fails. Likewise, in other
embodiments, the cloud-based storage system (902) depicted in FIG.
50 can include additional cloud computing instances with local
storage to expand the amount local storage that is offered by the
cloud computing instances in the cloud-based storage system
(902).
[0232] Readers will appreciate that many of the failure scenarios
described above with reference to FIG. 49 would also apply to
cloud-based storage system (902) depicted in FIG. 50. Likewise, the
cloud-based storage system (902) depicted in FIG. 50 may be
dynamically scaled up and down in a similar manner as described
above. The performance of various system-level tasks may also be
executed by the cloud-based storage system (902) depicted in FIG.
50 in an intelligent way, as described above.
[0233] Readers will appreciate that, in an effort to increase the
resiliency of the cloud-based storage systems described above,
various components may be located within different availability
zones. For example, a first cloud computing instance that supports
the execution of the storage controller application may be located
within a first availability zone while a second cloud computing
instance that also supports the execution of the storage controller
application may be located within a second availability zone.
Likewise, the cloud computing instances with local storage may be
distributed across multiple availability zones. In fact, in some
embodiments, an entire second cloud-based storage system could be
created in a different availability zone, where data in the
original cloud-based storage system is replicated (synchronously or
asynchronously) to the second cloud-based storage system so that if
the entire original cloud-based storage system went down, a
replacement cloud-based storage system (the second cloud-based
storage system) could be brought up in a trivial amount of
time.
[0234] Readers will appreciate that the cloud-based storage systems
described herein may be used as part of a fleet of storage systems.
In fact, the cloud-based storage systems described herein may be
paired with on-premises storage systems. In such an example, data
stored in the on-premises storage may be replicated (synchronously
or asynchronously) to the cloud-based storage system, and vice
versa.
[0235] For further explanation, FIG. 10 sets forth a flow chart
illustrating an example method of servicing I/O operations in a
cloud-based storage system (1004). Although depicted in less
detail, the cloud-based storage system (1004) depicted in FIG. 6
may be similar to the cloud-based storage systems described above
and may be supported by a cloud computing environment (1002).
[0236] The example method depicted in FIG. 10 includes receiving
(1006), by the cloud-based storage system (1004), a request to
write data to the cloud-based storage system (1004). The request to
write data may be received, for example, from an application
executing in the cloud computing environment, by a user of the
storage system that is communicatively coupled to the cloud
computing environment, and in other ways. In such an example, the
request can include the data that is to be written to the
cloud-based storage system (1004). In other embodiments, the
request to write data to the cloud-based storage system (1004) may
occur at boot-time when the cloud-based storage system (1004) is
being brought up.
[0237] The example method depicted in FIG. 10 also includes
deduplicating (1008) the data. Data deduplication is a data
reduction technique for eliminating duplicate copies of repeating
data. The cloud-based storage system (1004) may deduplicate (1008)
the data, for example, by comparing one or more portions of the
data to data that is already stored in the cloud-based storage
system (1004), by comparing a fingerprint for one or more portions
of the data to fingerprints for data that is already stored in the
cloud-based storage system (1004), or in other ways. In such an
example, duplicate data may be removed and replaced by a reference
to an already existing copy of the data that is already stored in
the cloud-based storage system (1004).
[0238] The example method depicted in FIG. 10 also includes
compressing (1010) the data. Data compression is a data reduction
technique whereby information is encoded using fewer bits than the
original representation. The cloud-based storage system (1004) may
compress (1010) the data by applying one or more data compression
algorithms to the data, which at this point may not include data
that data that is already stored in the cloud-based storage system
(1004).
[0239] The example method depicted in FIG. 10 also includes
encrypting (1012) the data. Data encryption is a technique that
involves the conversion of data from a readable format into an
encoded format that can only be read or processed after the data
has been decrypted. The cloud-based storage system (1004) may
encrypt (1012) the data, which at this point may have already been
deduplicated and compressed, using an encryption key. Readers will
appreciate that although the embodiment depicted in FIG. 10
involves deduplicating (1008) the data, compressing (1010) the
data, and encrypting (1012) the data, other embodiments exist in
which fewer of these steps are performed and embodiment exist in
which the same number of steps or fewer are performed in a
different order.
[0240] The example method depicted in FIG. 10 also includes storing
(1014), in solid-state storage of the cloud-based storage system
(1004), the data. Storing (1014) the data in solid-state storage of
the cloud-based storage system (1004) may be carried out, for
example, by storing (1016) the data in local storage (e.g., SSDs)
of one or more cloud computing instances, as described in more
detail above. In such an example, the data may be spread across the
local storage of many cloud computing instances, along with parity
data, to implement RAID or RAID-like data redundancy.
[0241] The example method depicted in FIG. 10 also includes storing
(1018), in object-storage of the cloud-based storage system (1004),
the data. Storing (1018) the data in object-storage of the
cloud-based storage system can include creating (1020) one or more
equal sized objects, wherein each equal sized object includes a
distinct chunk of the data, as described in greater detail
above.
[0242] The example method depicted in FIG. 10 also includes
receiving (1022), by the cloud-based storage system, a request to
read data from the cloud-based storage system (1004). The request
to read data from the cloud-based storage system (1004) may be
received, for example, from an application executing in the cloud
computing environment, by a user of the storage system that is
communicatively coupled to the cloud computing environment, and in
other ways. The request can include, for example, a logical address
the data that is to be read from the cloud-based storage system
(1004).
[0243] The example method depicted in FIG. 10 also includes
retrieving (1024), from solid-state storage of the cloud-based
storage system (1004), the data. Readers will appreciate that the
cloud-based storage system (1004) may retrieve (1024) the data from
solid-state storage of the cloud-based storage system (1004), for
example, by the storage controller application forwarding the read
request to the cloud computing instance that includes the requested
data in its local storage. Readers will appreciate that by
retrieving (1024) the data from solid-state storage of the
cloud-based storage system (1004), the data may be retrieved more
rapidly than if the data were read from cloud-based object storage,
even though the cloud-based object storage does include a copy of
the data.
[0244] For further explanation, FIG. 11 sets forth a flow chart
illustrating an additional example method of servicing I/O
operations in a cloud-based storage system (1004). The example
method depicted in FIG. 11 is similar to the example method
depicted in FIG. 10, as the example method depicted in FIG. 11 also
includes receiving (1006) a request to write data to the
cloud-based storage system (1004), storing (1014) the data in
solid-state storage of the cloud-based storage system (1004), and
storing (1018) the data in object-storage of the cloud-based
storage system (1004).
[0245] The example method depicted in FIG. 11 also includes
detecting (1102) that at least some portion of the solid-state
storage of the cloud-based storage system has become unavailable.
Detecting (1102) that at least some portion of the solid-state
storage of the cloud-based storage system has become unavailable
may be carried out, for example, by detecting that one or more of
the cloud computing instances that includes local storage has
become unavailable, as described in greater detail below.
[0246] The example method depicted in FIG. 11 also includes
identifying (1104) data that was stored in the portion of the
solid-state storage of the cloud-based storage system that has
become unavailable. Identifying (1104) data that was stored in the
portion of the solid-state storage of the cloud-based storage
system that has become unavailable may be carried out, for example,
through the use of metadata that maps some identifier of a piece of
data (e.g., a sequence number, an address) to the location where
the data is stored. Such metadata, or separate metadata, may also
map the piece of data to one or more object identifiers that
identify objects stored in the object-storage of the cloud-based
storage system that contain the piece of data.
[0247] The example method depicted in FIG. 11 also includes
retrieving (1106), from object-storage of the cloud-based storage
system, the data that was stored in the portion of the solid-state
storage of the cloud-based storage system that has become
unavailable. Retrieving (1106) the data that was stored in the
portion of the solid-state storage of the cloud-based storage
system that has become unavailable from object-storage of the
cloud-based storage system may be carried out, for example, through
the use of metadata described above that maps the data that was
stored in the portion of the solid-state storage of the cloud-based
storage system that has become unavailable to one or more objects
stored in the object-storage of the cloud-based storage system that
contain the piece of data. In such an example, retrieving (1106)
the data may be carried out by reading the objects that map to the
data from the object-storage of the cloud-based storage system.
[0248] The example method depicted in FIG. 11 also includes storing
(1108), in solid-state storage of the cloud-based storage system,
the retrieved data. Storing (1108) the retrieved data in
solid-state storage of the cloud-based storage system may be
carried out, for example, by creating replacement cloud computing
instances with local storage and storing the data in the local
storage of one or more of the replacement cloud computing
instances, as described in greater detail above.
[0249] Readers will appreciate that although the embodiments
described above relate to embodiments in which data that was stored
in the portion of the solid-state storage of the cloud-based
storage system that has become unavailable is essentially brought
back into the solid-state storage layer of the cloud-based storage
system by retrieving the data from the object-storage layer of the
cloud-based storage system, other embodiments are within the scope
of the present disclosure. For example, because data may be
distributed across the local storage of multiple cloud computing
instances using data redundancy techniques such as RAID, in some
embodiments the lost data may be brought back into the solid-state
storage layer of the cloud-based storage system through a RAID
rebuild.
[0250] For further explanation, FIG. 12 sets forth a flow chart
illustrating an example method of servicing I/O operations in a
cloud-based storage system (1204). Although depicted in less
detail, the cloud-based storage system (1204) depicted in FIG. 12
may be similar to the cloud-based storage systems described above
and may be supported by a cloud computing environment (1202).
[0251] The example method depicted in FIG. 12 includes receiving
(1206), by the cloud-based storage system (1204), a request to
write data to the cloud-based storage system (1204). The request to
write data may be received, for example, from an application
executing in the cloud computing environment, by a user of the
storage system that is communicatively coupled to the cloud
computing environment, and in other ways. In such an example, the
request can include the data that is to be written to the
cloud-based storage system (1204). In other embodiments, the
request to write data to the cloud-based storage system (1204) may
occur at boot-time when the cloud-based storage system (1204) is
being brought up.
[0252] The example method depicted in FIG. 12 also includes
deduplicating (1208) the data. Data deduplication is a data
reduction technique for eliminating duplicate copies of repeating
data. The cloud-based storage system (1204) may deduplicate (1208)
the data, for example, by comparing one or more portions of the
data to data that is already stored in the cloud-based storage
system (1204), by comparing a fingerprint for one or more portions
of the data to fingerprints for data that is already stored in the
cloud-based storage system (1204), or in other ways. In such an
example, duplicate data may be removed and replaced by a reference
to an already existing copy of the data that is already stored in
the cloud-based storage system (1204).
[0253] The example method depicted in FIG. 12 also includes
compressing (1210) the data. Data compression is a data reduction
technique whereby information is encoded using fewer bits than the
original representation. The cloud-based storage system (1204) may
compress (1210) the data by applying one or more data compression
algorithms to the data, which at this point may not include data
that data that is already stored in the cloud-based storage system
(1204).
[0254] The example method depicted in FIG. 12 also includes
encrypting (1212) the data. Data encryption is a technique that
involves the conversion of data from a readable format into an
encoded format that can only be read or processed after the data
has been decrypted. The cloud-based storage system (1204) may
encrypt (1212) the data, which at this point may have already been
deduplicated and compressed, using an encryption key. Readers will
appreciate that although the embodiment depicted in FIG. 12
involves deduplicating (1208) the data, compressing (1210) the
data, and encrypting (1212) the data, other embodiments exist in
which fewer of these steps are performed and embodiment exist in
which the same number of steps or fewer are performed in a
different order.
[0255] The example method depicted in FIG. 12 also includes storing
(1214), in block-storage of the cloud-based storage system (1204),
the data. Storing (1214) the data in block-storage of the
cloud-based storage system (1204) may be carried out, for example,
by storing (1216) the data in local storage (e.g., SSDs) of one or
more cloud computing instances, as described in more detail above.
In such an example, the data spread across local storage of
multiple cloud computing instances, along with parity data, to
implement RAID or RAID-like data redundancy.
[0256] The example method depicted in FIG. 12 also includes storing
(1218), in object-storage of the cloud-based storage system (1204),
the data. Storing (1218) the data in object-storage of the
cloud-based storage system can include creating (1220) one or more
equal sized objects, wherein each equal sized object includes a
distinct chunk of the data, as described in greater detail
above.
[0257] The example method depicted in FIG. 12 also includes
receiving (1222), by the cloud-based storage system, a request to
read data from the cloud-based storage system (1204). The request
to read data from the cloud-based storage system (1204) may be
received, for example, from an application executing in the cloud
computing environment, by a user of the storage system that is
communicatively coupled to the cloud computing environment, and in
other ways. The request can include, for example, a logical address
the data that is to be read from the cloud-based storage system
(1204).
[0258] The example method depicted in FIG. 12 also includes
retrieving (1224), from block-storage of the cloud-based storage
system (1204), the data. Readers will appreciate that the
cloud-based storage system (1204) may retrieve (1224) the data from
block-storage of the cloud-based storage system (1204), for
example, by the storage controller application forwarding the read
request to the cloud computing instance that includes the requested
data in its local storage. Readers will appreciate that by
retrieving (1224) the data from block-storage of the cloud-based
storage system (1204), the data may be retrieved more rapidly than
if the data were read from cloud-based object storage, even though
the cloud-based object storage does include a copy of the data.
[0259] For further explanation, FIG. 13 sets forth a flow chart
illustrating an additional example method of servicing I/O
operations in a cloud-based storage system (1204). The example
method depicted in FIG. 13 is similar to the example method
depicted in FIG. 12, as the example method depicted in FIG. 13 also
includes receiving (1206) a request to write data to the
cloud-based storage system (1204), storing (1214) the data in
block-storage of the cloud-based storage system (1204), and storing
(1218) the data in object-storage of the cloud-based storage system
(1204).
[0260] The example method depicted in FIG. 13 also includes
detecting (1302) that at least some portion of the block-storage of
the cloud-based storage system has become unavailable. Detecting
(1302) that at least some portion of the block-storage of the
cloud-based storage system has become unavailable may be carried
out, for example, by detecting that one or more of the cloud
computing instances that includes local storage has become
unavailable, as described in greater detail below.
[0261] The example method depicted in FIG. 13 also includes
identifying (1304) data that was stored in the portion of the
block-storage of the cloud-based storage system that has become
unavailable. Identifying (1304) data that was stored in the portion
of the block-storage of the cloud-based storage system that has
become unavailable may be carried out, for example, through the use
of metadata that maps some identifier of a piece of data (e.g., a
sequence number, an address) to the location where the data is
stored. Such metadata, or separate metadata, may also map the piece
of data to one or more object identifiers that identify objects
stored in the object-storage of the cloud-based storage system that
contain the piece of data.
[0262] The example method depicted in FIG. 13 also includes
retrieving (1306), from object-storage of the cloud-based storage
system, the data that was stored in the portion of the
block-storage of the cloud-based storage system that has become
unavailable. Retrieving (1306) the data that was stored in the
portion of the block-storage of the cloud-based storage system that
has become unavailable from object-storage of the cloud-based
storage system may be carried out, for example, through the use of
metadata described above that maps the data that was stored in the
portion of the block-storage of the cloud-based storage system that
has become unavailable to one or more objects stored in the
object-storage of the cloud-based storage system that contain the
piece of data. In such an example, retrieving (1306) the data may
be carried out by reading the objects that map to the data from the
object-storage of the cloud-based storage system.
[0263] The example method depicted in FIG. 13 also includes storing
(1308), in block-storage of the cloud-based storage system, the
retrieved data. Storing (1308) the retrieved data in block-storage
of the cloud-based storage system may be carried out, for example,
by creating replacement cloud computing instances with local
storage and storing the data in the local storage of one or more of
the replacement cloud computing instances, as described in greater
detail above.
[0264] Readers will appreciate that although the embodiments
described above relate to embodiments in which data that was stored
in the portion of the block-storage of the cloud-based storage
system that has become unavailable is essentially brought back into
the block-storage layer of the cloud-based storage system by
retrieving the data from the object-storage layer of the
cloud-based storage system, other embodiments are within the scope
of the present disclosure. For example, because data may be
distributed across the local storage of multiple cloud computing
instances using data redundancy techniques such as RAID, in some
embodiments the lost data may be brought back into the
block-storage layer of the cloud-based storage system through a
RAID rebuild.
[0265] For further explanation, FIG. 14 sets forth a flow chart
illustrating an example method for staging data in a cloud-based
storage system (1400A).
[0266] In some examples, a single cloud-based storage system
(1400A) may implement staging data as a stand-alone system, without
synchronizing data with other storage systems. However, in other
examples, a cloud-based storage system (1400A) may implement
staging data as part of synchronizing a dataset (1458) across one
or more other storage systems (1400A-1400N), where the one or more
other storage systems may include hardware-based storage systems or
cloud-based storage systems, and where the dataset (1458) may
correspond to a pod (1454) as described above with reference to
FIGS. 4-8.
[0267] In this example, and as illustrated in FIG. 14, the storage
systems (1400A-1400N) synchronously replicating a dataset (1458)
include both hardware-based storage systems (1400B-1400N) and a
cloud-based storage system (1400A) implemented within a cloud
computing environment (1401). For clarity, only one cloud-based
storage system (1400A) is depicted, however, in other examples,
there may be multiple cloud-based storage systems, or multiple
cloud-based storage systems without any hardware-based storage
systems.
[0268] In some implementations, the cloud-based storage system
(1400A) may provide similar services as those described for the
cloud-based storage systems above, with reference to FIGS. 8-13.
For example, the cloud-based storage system (1400A) may be used to
provide block storage services to users based on use of services of
a cloud-computing environment, such as cloud computing environments
described in FIGS. 8-13, including storage provided by virtual
computing instances or storage provided by solid-state storage, and
so on. Further, the cloud computing environment (1401) may be
implemented similarly to the cloud computing environments described
above with reference to FIGS. 8-13.
[0269] In some implementations, a cloud-based storage system
(1400A) may store data in multiple tiers of cloud storage,
including a first tier of block storage within a cloud computing
instance layer that includes respective local storage and including
a second tier of object storage within a cloud-based object
storage. For example, with reference to FIG. 3D, a first tier of
cloud storage may include cloud computing instances (340a-340n),
where respective cloud computing instances may include respective
storage (330, 342, 334, 344, 338, 346), and where the types of
storage are described above. Further, in this example, a second
tier of cloud storage may be a cloud-based object store (348), as
also described above with reference to FIG. 3D.
[0270] In some implementations, a cloud-based storage system
(1400A) may provide data storage using multiple types of
computational resources. For example, a cloud-based storage system
(1400A) may provide data storage using storage elements implemented
by cloud computing instances and/or storage provided by provisioned
solid state devices within a cloud services environment. Further,
the storage elements may be provisioned or configured in accordance
with different performance specifications. For example, a cloud
services environment may provide provisioned solid state devices of
varying storage capacity and/or of varying performance
specifications.
[0271] In some examples, a first tier of cloud storage may serve as
a cache of a second tier of cloud storage, and in such a case, the
entire content of the first tier of cloud storage may be
reconstructed from the second tier of cloud storage. In other
examples, a first tier of cloud storage may include some updates
not in the second tier of cloud storage or the first tier of cloud
storage may include some updates that are not yet in the second
tier of cloud storage, and in response, most, but not necessarily
all, of the content in the first tier of cloud storage may be
reconstructed from the second tier of cloud storage. In some cases,
suitable interaction in the data between the first tier of cloud
storage and the second tier of cloud storage may ensure that the
content of the second tier of cloud storage is appropriately
consistent.
[0272] Further, in some implementations, cloud computing instances
for a cloud-based storage system may supply partial front-end
datasets for a complete, or "bulk", dataset stored using cloud
storage infrastructure, such as object storage. In this case, a
cloud computing instance may be considered to be, more generically,
a virtual machine that executes parts of the cloud-based storage
system (1400A) implementation.
[0273] Continuing with this example, it may be necessary for these
parts of the cloud-based storage system (1400A) to reach a
consensus that the underlying virtual machine(s) are operating to
implement a single virtual storage system in accordance with the
rest of this model. In some examples, the cloud computing
environment (316) or cloud infrastructure may supply alternate
models for ensuring that the virtual machines, high performance
storage, and bulk storage used to create a single cloud-based
storage system (1400A) is operationally consistent and can interact
coherently as a storage system, such as the storage systems
described within FIGS. 1A-8.
[0274] In this example, the storage systems (1400A-1400N) depicted
in FIG. 14 may be similar to the storage systems described above
with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3D, FIG. 8-13,
or any combination thereof. In fact, the storage systems
(1400A-1400N) depicted in FIG. 14 may include the same, fewer, or
additional components as the storage systems described above.
[0275] In some implementations, a cloud-based storage system may
stage speculative writes in fast memory--where in the cloud-based
storage system (1400A) described above, fast memory may be the
first tier of cloud storage. However, in some implementations, fast
memory may be provided by cloud computing instances that include an
application configured as a storage controller. In this
implementation, "fast" storage may be a relative measure with
respect to one or more other storage elements within a cloud-based
storage system (1400A); for example, a first tier of cloud storage
may be implemented with storage elements, such as SSDs, that have
higher performance response times than storage provided by a second
tier of cloud storage, such as an object store.
[0276] In these examples, for a cloud-based storage system (1400A),
there may be a write into fast storage to record an operation, or
record operation results, in a way that makes the operation or the
results persistent more quickly--thus enabling one or more of the
following benefits, including: (a) enabling operations to be
signaled as completed faster; (b) enabling internal storage system
processes to be unblocked more quickly while allowing a larger
queue of changes to be built up for writing to slower bulk storage;
or to reduce the likelihood of temporary data, which may be
overwritten quickly, from being written to bulk storage in the
first place, thereby improving flash lifespans; or (c) enabling
multiple operations to be merged together for more efficient or
better organized storage operations, thereby potentially increasing
throughput.
[0277] One example for reducing bandwidth overhead is for the
cloud-based storage system (1400A) to use the fast storage as
internal staging space, acknowledging the write more quickly to a
storage controller, and then writing to bulk storage at a later
point in time--where the later write to bulk storage from fast
storage may be invisible to the storage controllers because to the
storage controller, the write was persisted, or durably stored at
the moment that the write was acknowledge. In some cases, such a
write protocol--where a storage controller is unaware of internal
transfers of data--may eliminate the need for separately
addressable fast durable storage.
[0278] However, there is significant flexibility to be gained by
allowing the storage controllers to manage the process of
transferring data between fast storage and bulk storage, rather
than having the cloud-based storage system (1400A) do these
transfers implicitly behind the scenes and hiding the fast durable
storage from the storage controller. In other words, a cloud-based
storage system implementation may gain flexibility for optimizing
overall operations by allowing higher level aspects of an
implementation to record to fast storage early in a processing
pipeline.
[0279] In some examples, while storage elements of a first tier of
cloud storage may be less durable than storage elements of a second
tier of cloud storage, data stored within the first tier of cloud
storage of a cloud-based storage system may be made durable, or
more durable, based on using one or more techniques for adding data
recovery options, such as through the generating and storing data
parity information, use of erasure codes, use of RAID
configurations, among other techniques described in greater detail
within a related application that includes some of the same
inventors, which is incorporated herein in its entirety for all
purposes.
[0280] In some embodiments, a solution to reduce bandwidth for
backend data transfers--or data transfers from a first tier of
cloud storage to a second tier of cloud storage within a
cloud-based storage system--is to utilize staging a write through
fast storage. In this example, if the write is first written as
three copies to fast storage on the respective fast storage of
three separate storage elements, where the contents of that
multi-copy write are aggregated with the contents of other writes
to form an N+2 protected stripe in bulk storage, then if one of the
storage elements selected serving as fast storage is the same
storage element to be selected as a source for the eventual write
to bulk storage, then the extra
storage-controller-to-storage-device bandwidth for transfer to the
bulk storage may be avoided by the storage controller instructing
the storage element to transfer that data from fast storage to bulk
storage.
[0281] In some examples, avoiding extra transfers may be achieved
by transformations such as merging data stored within a first tier
of cloud storage of a cloud-based storage system. In this example,
the other two storage elements that stored a respective copy in
fast storage retain their respective copy until the final N+2
stripe has been written and committed, but otherwise outside of a
fault and recovery sequence, the other two storage elements do not
need to perform additional, corresponding, write or copies of the
respective copies. In some examples, the format of data written to
fast storage is identical to the format of data written to bulk
storage, and consequently, no data transformation is needed prior
to a transfer. In other cases, where the format of data written to
fast storage is not identical to the format of data written to bulk
storage, the transfer may include transforming the content during
the transfer from fast storage to bulk storage, where the
transformation may be based on instructions from a storage
controller, and possibly in coordination with merged content from
other stored writes in fast durable storage.
[0282] In some examples, transfers to fast storage from bulk
storage or from fast storage to bulk storage may operate
simultaneously, where such parallelism may increase bandwidth or
reduce locking contention issues within a storage controller
software implementation. For example, a storage controller may use
separate, independent and/or parallel, commands to different
storage elements, such as a command to write to a storage element
in a first tier of cloud storage and a command to read from a
second tier of cloud storage, thereby eliminating locking
contention while gaining bandwidth for transfers to and from
storage elements.
[0283] In some implementations, as an optimization of the number of
transfers between a first tier of cloud storage and a second tier
of cloud storage, if data is accumulated in fast storage for some
period of time before being transferred to bulk storage, then a
storage controller may determine that some of the content stored in
fast storage is no longer needed, or will not be requested, and in
response this determination, the storage controller may partially
or completely avoid a transfer to bulk storage. Avoiding such
transfers may occur based on the determination that data has been
overwritten, or if the data is metadata that has been reorganized
or optimized. Avoiding such transfer may also happen in cases where
deduplication operations include determining that some data has
already been written to the bulk storage somewhere in the storage
system, thereby avoiding the transfer of data that already
exists.
[0284] In some implementations, data may be transferred between a
first tier of cloud storage and a second tier of cloud storage such
that the transferred data coupled with data already present on the
second tier of cloud storage can be used to calculate new data to
be stored, such as by combining partial data into combined
formatted data, or such as calculating content for redundancy data
shards from prior intermediate content for redundancy data shards
coupled with different intermediate content for the redundancy data
shards or content from data shards.
[0285] In this example, the various storage elements of a first
tier of cloud storage of a cloud-based storage system (1400),
individually or in combination, may be used to implement multiple,
different RAID levels or combinations of RAID levels. In this
example, a RAID stripe is data that is stored among a set of memory
regions mapped across a set of storage elements, where each memory
region on a given storage element stores a portion of the RAID
stripe and may be referred to as a "strip," a "stripe element," or
a "shard."
[0286] Continuing with this example, with a simple XOR parity-based
redundancy shard, a first partial parity calculated by XOR'ing data
from a first subset of data shards and a second partial parity
calculated by XOR'ing data from a second subset of data shards can
be transferred from a first storage element which calculated the
first partial parity and from a second storage element which
calculated the second partial parity to a third storage element
which can then XOR the first and second partial parties together to
yield a complete calculated parity which can be stored into bulk
storage within the third storage element. In this example, the
first storage element and the second storage element are within a
first tier of cloud storage, and the third storage element is
within the second tier of cloud storage.
[0287] Further, in some implementations, Galois field math allows
similar partial results to be merged together to store additional
types of calculated redundancy shards, such as the typical Q shard
for a RAID-6 stripe. For example, with the Galois math described in
the paper "The mathematics of RAID-6" by H. Peter Anvin, 20 Jan.
2004, consider that the final Q shard for a 5+2 RAID-6 stripe is
calculated as:
Q=g.sup.0D.sub.0+g.sup.1D.sub.1+g.sup.2D.sub.2+g.sup.3D.sub.3+g.sup.4D.s-
ub.4
[0288] In this example, a calculation of a partial Q from just the
first two data shards could be calculated as:
Q.sub.p1=g.sup.0D.sub.0+g.sup.1D.sub.1
[0289] In this example, Q.sub.p1 may be stored by a storage
controller on some first partial Q shard storage element as part of
protecting just the first two data shards for the eventual end
resulting 5+2 stripe, and this plus an additional XOR parity stored
in yet another storage element is enough to recover from any two
faults of the devices written for this partial stripe, since as
long as the partial stripe is properly recognized as partial, the
content from g.sup.2D.sub.2+g.sup.3D.sub.3+g.sup.4D.sub.4 can be
inferred to be calculated from empty (zero) data shards D.sub.2,
D.sub.3, and D.sub.4.
[0290] Continuing with this example, a second calculation of a
partial Q from the other three data shards could further be
calculated as:
Q.sub.p2=g.sup.2D.sub.2+g.sup.3D.sub.3+g.sup.4D.sub.4
[0291] In this example, Q.sub.p2 may be stored on a second partial
Q shard storage element as part of protecting those three data
shards, and as with the first partial Q shard, when coupled with an
additionally written partial XOR parity written to another storage
element, the partial content is again protected from any two faults
since the content from g.sup.0D.sub.0+g.sup.1D.sub.1 associated
with the partial Q shard can again be inferred to be calculated
from empty (zero) data shards D.sub.0 and D.sub.1.
[0292] Continuing with this example, a storage element, such as a
storage element within the second tier of cloud storage, which
eventually receives both Q.sub.p1 and Q.sub.p2 may calculate the Q
value for the complete stripe including all five data shards as an
appropriate Galois field addition of Q.sub.p1 and Q.sub.p2.
[0293] As illustrated, FIG. 14 sets forth a flow chart illustrating
an example method for performing data storage operations within a
cloud-based storage system (1400A) that integrates fast storage and
bulk storage according to some embodiments of the present
disclosure--where fast storage may be implemented by a first tier
of cloud storage and bulk storage may be implemented by a second
tier of cloud storage.
[0294] Although depicted in less detail, the cloud-based storage
system (1400A) may be similar to the storage systems described
above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3D, and
4-8, or any combination thereof. In fact, the cloud-based storage
system (1400) may include the same, fewer, additional components as
the storage systems described above.
[0295] As described above, a cloud-based storage system (1400A)
integrates fast storage and bulk storage, where as described above,
fast storage may be implemented as one or more cloud computing
instances within a first tier of cloud computing, and bulk storage
may be implemented as a cloud-based object store within a second
tier of cloud storage.
[0296] In this example, a cloud-based storage system (1400)
receives (1402) a data storage operation (1452), such as a read or
write operation or some other data storage operation, and
determines how to make use of either the fast storage (1454), or
bulk storage (1456), or both the fast storage (1454) and bulk
storage (1456) in performing, or carrying out, the data storage
operation (1452). As described above, a data storage operation
(1452) may allow a host computer to make explicit use of all memory
types within the cloud-based storage system (1400), or in some
cases, the cloud-based storage system (1400) may provide data
storage features without revealing specific aspects of the
underlying memory types or storage elements. Examples
implementations of fast storage (1454) and bulk storage (1456), and
various techniques for providing or hiding features, are described
above with reference to the description of the memory architecture
of a cloud-based storage system corresponding to FIGS. 3D and
8-13.
[0297] In this example, the example flow chart includes: receiving
(1402), by a cloud-based storage system (1400) integrating a first
tier of cloud storage and a second tier of cloud storage, a data
storage operation (1452) from a computing device (1451); storing
(1404) data (1460) corresponding to the data storage operation
(1452) within the first tier of cloud storage (1454) in accordance
with a first storage format; and responsive to detecting a
condition for transferring data between the first tier of cloud
storage and the second tier of cloud storage, transferring (1406)
the data (1460) in the first storage format from the first tier of
cloud storage (1454) to a second data format in the second tier of
cloud storage (1456).
[0298] Receiving (1402), by a cloud-based storage system (1400)
integrating a first tier of cloud storage and a second tier of
cloud storage, a data storage operation (1452) from a computing
device (1451) may be implemented by receiving a message over a
network using an application programming interface provided by the
cloud computing environment (1401), such as depicted in FIGS. 1A-3D
and 8-13, in accordance with one or more network communication
protocols.
[0299] In this example, the data storage operation (1452) may be a
read command, a write command, an erase command, or generally, any
type of command or operation that utilizes one or more features
that the cloud-based storage system (1400) provides.
[0300] In this example, a computing device (1451) may be a remote
computing device such as a remote desktop, a host computer, a
mobile device, a virtual machine within a cloud computing
environment, or some other type of computing device or instance
executing either locally within a storage system or at a
geographically remote location.
[0301] The example method depicted in FIG. 4 also includes storing
(1404) data (1460) corresponding to the data storage operation
(1452) within the first tier of cloud storage (1454) in accordance
with a first storage format. Storing (1404) data (1460)
corresponding to the data storage operation (1452) within the first
tier of cloud computing (1454) may be implemented by storing (1404)
the data (1460) in accordance with a first data resiliency
technique implemented by one or more controllers of the cloud-based
storage system (1400) as part of storing the data (1460) within a
RAID stripe using a RAID N+2 schema, as described above.
[0302] However, in other examples, storing (1404) the data (1460)
may be implemented by storing the data, without using a data
resiliency techniques, within one or more of the cloud computing
instances of the first tier of cloud storage (1454).
[0303] The example method depicted in FIG. 4 also includes,
responsive to detecting a condition for transferring data between
the first tier of cloud storage and the second tier of cloud
storage, transferring (1406) the data (1460) in the first storage
format from the first tier of cloud storage (1454) to a second data
format in the second tier of cloud storage (1456. Detecting a
condition for transferring data between fast storage, i.e., the
first tier of cloud storage (1454) and bulk storage, i.e., the
second tier of cloud storage (1456) may be implemented using
different techniques. In one example technique, the condition for
transferring data may be successful completion of writing one or
more entire RAID stripe and calculating a corresponding parity
value. Transferring (1406) the data (1460) from fast storage (454)
to be stored as part of a dataset (1458) within bulk storage (1456)
in accordance with a second data resiliency technique may be
implemented by one or more controllers of the cloud-based storage
system (1400) moving or copying the data (1460) in the fast storage
(1454) into bulk storage (1456) as part of a RAID stripe within a
RAID M+2 schema in bulk storage (1456), as described above--where
the RAID N+2 schema used for fast (1454) is different from the RAID
M+2 schema used for bulk storage (456).
[0304] However, in other examples, transferring (1406) the data
(1460) may be implemented by copying data from block-based storage
of the one or more cloud computing instances in the first tier of
cloud storage to one or more objects within a cloud-based object
store, such as the block storage and object storage described above
with reference to FIG. 3C.
[0305] For further explanation, FIG. 15 sets forth a flow chart
illustrating an example method for staging data within a
cloud-based storage system (1400A) according to some embodiments of
the present disclosure.
[0306] The example method depicted in FIG. 15 is similar to the
example method depicted in FIG. 14, as the example method depicted
in FIG. 15 also includes: receiving (1402), by a cloud-based
storage system (1400) integrating a first tier of cloud storage and
a second tier of cloud storage, a data storage operation (1452)
from a computing device (1451); storing (1404) data (1460)
corresponding to the data storage operation (1452) within the first
tier of cloud storage (1454) in accordance with a first storage
format; and responsive to detecting a condition for transferring
data between the first tier of cloud storage and the second tier of
cloud storage, transferring (1406) the data (1460) in the first
storage format from the first tier of cloud storage (1454) to a
second data format in the second tier of cloud storage (1456).
[0307] However, the example method depicted in FIG. 15 differs from
the example method depicted in FIG. 14 in that FIG. 15 also
includes determining (1502) a data storage optimization that is
applicable to one or more portions of stored data within the first
tier of cloud storage (1454); modifying (1504) the one or more
portions of stored data within the first tier of cloud storage
(1454) to generate modified data (1550); and storing (1506), after
modifying the one or more portions of data, the modified data
(1550) within first tier of cloud storage (1454).
[0308] Determining (1502) a data storage optimization that is
applicable to one or more portions of stored data within the first
tier of cloud storage (1454) may be implemented by one or more
controllers of the cloud-based storage system (1400A), where the
data storage optimization may be data compression, data
deduplication, garbage collection, or some other data storage
optimization that results in a smaller storage footprint than the
original data, or that results in modified data that may provide
efficiencies other than storage size reductions--as described in
greater detail above with regard to data storage optimizations
applicable to data stored in fast storage, or the first tier of
cloud storage of a cloud-based storage system (1400A).
[0309] Modifying (1504) the one or more portions of stored data
within the first tier of cloud storage (1454) may be implemented
depending on the data storage optimization determined (1502) above,
where if the data storage optimization is one or more data
compression techniques, then the compressed data may be the
modified data (1550). Similarly, if the data storage optimization
determined (1502) above is garbage collection, then the modified
data may be the originally stored data minus, or without the one or
more portions that have been identified for garbage collection, so
the modified data (1550) is the remaining data after garbage
collection. Similarly for the case where the data storage
optimization is data deduplication or some other data storage
optimization.
[0310] Storing (1506), after modifying (1504) the one or more
portions of data, the modified data (550) within the first tier of
cloud storage (1456) may be implemented by one or more controllers
of the cloud-based storage system (1400A) writing the modified data
(1550)--generated by performing the data storage
optimizations--into a same or different location within the first
tier of cloud storage (1454).
[0311] For further explanation, FIG. 16 sets forth a flow chart
illustrating an example method for staging data within a
cloud-based storage system (1400A) according to some embodiments of
the present disclosure.
[0312] The example method depicted in FIG. 16 is similar to the
example method depicted in FIG. 15, as the example method depicted
in FIG. 16 also includes: receiving (1402), by a cloud-based
storage system (1400) integrating a first tier of cloud storage and
a second tier of cloud storage, a data storage operation (1452)
from a computing device (1451); storing (1404) data (1460)
corresponding to the data storage operation (1452) within the first
tier of cloud storage (1454) in accordance with a first storage
format; and responsive to detecting a condition for transferring
data between the first tier of cloud storage and the second tier of
cloud storage, transferring (1406) the data (1460) in the first
storage format from the first tier of cloud storage (1454) to a
second data format in the second tier of cloud storage (1456);
determining (1502) a data storage optimization that is applicable
to one or more portions of stored data within the first tier of
cloud storage (1454); modifying (1504) the one or more portions of
stored data within the first tier of cloud storage (1454) to
generate modified data (1550).
[0313] However, the example method depicted in FIG. 16 differs from
the example method depicted in FIG. 15 in that FIG. 16 also
includes storing (1602), after modifying (1504) the one or more
portions of data, the modified data (1550) within the second tier
of cloud storage (1456). Storing (1602), after modifying (1504) the
one or more portions of data, the modified data (1550) within the
second tier of cloud storage (1456) may be implemented by one or
more controllers of the cloud-based storage system (1400A) writing
the modified data (1550)--generated by performing the data storage
optimizations--into the second tier of cloud storage (1456).
[0314] For further explanation, FIG. 17 sets forth a flow chart
illustrating an example method for staging data within a
cloud-based storage system (1400A) according to some embodiments of
the present disclosure.
[0315] The example method depicted in FIG. 17 is similar to the
example method depicted in FIG. 14, as the example method depicted
in FIG. 17 also includes: receiving (1402), by a cloud-based
storage system (1400) integrating a first tier of cloud storage and
a second tier of cloud storage, a data storage operation (1452)
from a computing device (1451); storing (1404) data (1460)
corresponding to the data storage operation (1452) within the first
tier of cloud storage (1454) in accordance with a first storage
format; and responsive to detecting a condition for transferring
data between the first tier of cloud storage and the second tier of
cloud storage, transferring (1406) the data (1460) in the first
storage format from the first tier of cloud storage (1454) to a
second data format in the second tier of cloud storage (1456).
[0316] However, the example method depicted in FIG. 17 differs from
the example method depicted in FIG. 14 in that FIG. 17 also
includes determining (1702), based on received write operations and
corresponding data payload sizes over a window of time, a data
storage consumption rate; and dynamically transferring (1704), from
the first tier of cloud storage (1454) to the second tier of cloud
storage (1456) and in dependence upon the data storage consumption
rate and storage availability of the first tier of cloud storage
(1454), one or more portions of stored data (1750) at a transfer
rate that avoids stalling subsequently received data storage
operations.
[0317] Determining (1702), based on received write operations and
corresponding data payload sizes over a window of time, a data
storage consumption rate may be implemented by one or more
controllers of the cloud-based storage system (1400A) tracking and
recording, for each write operation received over a given period of
time, for example, a defined number of seconds or minutes, data
payload sizes for the data written into the first tier of cloud
storage (1454).
[0318] Further, given an aggregate, or sum of all data payload
sizes over the defined window of time, the one or more controllers
may calculate a rate at which storage in the first tier of cloud
storage (1454) is consumed, where the data storage consumption rate
may be calculated as quantity of space consumed over the period of
time, where the quantity of space consumed is the aggregate
calculation, and the period of time is the defined window of
time.
[0319] Dynamically transferring (1704), from the first tier of
cloud storage (1454) to the second tier of cloud storage (1456) and
in dependence upon the data storage consumption rate and storage
availability of the first tier of cloud storage (1454), one or more
portions of stored data (1750) at a transfer rate that avoids
stalling subsequently received data storage operations may be
implemented by one or more controllers of the cloud-based storage
system (1400A) calculating, based on a quantity of memory already
being used and a quantity of storage available, an amount of time
at which--given the data consumption rate--there is no free space,
or the remaining space is within a threshold amount of provisioned
space based on one or more storage policy criteria, such as a
quantity of provisioned cloud computing instances beyond a price
threshold.
[0320] Further, the implementation may include using the calculated
time and transferring data from the first tier of cloud storage
(1454) into the second tier of cloud storage (1456) before the
space in first tier of cloud storage (1454) is consumed, which
prevents the cloud-based storage system (1400A) from provisioning
additional cloud computing instances that may result in exceeding a
storage policy limitation on resources or price.
[0321] Example embodiments are described largely in the context of
a fully functional computer system. Readers of skill in the art
will recognize, however, that the present disclosure also may be
embodied in a computer program product disposed upon computer
readable storage media for use with any suitable data processing
system. Such computer readable storage media may be any storage
medium for machine-readable information, including magnetic media,
optical media, or other suitable media. Examples of such media
include magnetic disks in hard drives or diskettes, compact disks
for optical drives, magnetic tape, and others as will occur to
those of skill in the art. Persons skilled in the art will
immediately recognize that any computer system having suitable
programming means will be capable of executing the steps of the
method as embodied in a computer program product. Persons skilled
in the art will recognize also that, although some of the example
embodiments described in this specification are oriented to
software installed and executing on computer hardware,
nevertheless, alternative embodiments implemented as firmware or as
hardware are well within the scope of the present disclosure.
[0322] Embodiments can include be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present disclosure.
[0323] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0324] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0325] Computer readable program instructions for carrying out
operations of the present disclosure may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present disclosure.
[0326] Aspects of the present disclosure are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to some embodiments of the disclosure. It will be
understood that each block of the flowchart illustrations and/or
block diagrams, and combinations of blocks in the flowchart
illustrations and/or block diagrams, can be implemented by computer
readable program instructions.
[0327] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0328] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0329] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present disclosure. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
* * * * *