U.S. patent application number 16/050698 was filed with the patent office on 2019-11-21 for asynchronous replication of synchronously replicated data.
The applicant listed for this patent is PURE STORAGE, INC.. Invention is credited to THOMAS GILL, DAVID GRUNWALD, RONALD KARR, DAQUAN ZUO.
Application Number | 20190354628 16/050698 |
Document ID | / |
Family ID | 68533191 |
Filed Date | 2019-11-21 |
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United States Patent
Application |
20190354628 |
Kind Code |
A1 |
GRUNWALD; DAVID ; et
al. |
November 21, 2019 |
ASYNCHRONOUS REPLICATION OF SYNCHRONOUSLY REPLICATED DATA
Abstract
A storage system asynchronously replicating a synchronously
replicated dataset, where the asynchronous replication of the
asynchronously replicated dataset includes: determining, at a
target storage system, multiple work items corresponding to a
dataset stored among multiple source storage systems, wherein each
respective work item corresponds to a respective subset of the
dataset; and for each session from among a plurality of sessions
operating on the target storage system: determining one or more
computing environment factors affecting performance of replication
of data from one or more of the multiple source storage systems to
the target storage system; identifying, for a given session and
based on the one or more computing environment factors, a
respective source storage system and a quantity of work items; and
replicating, from the respective storage system, one or more
subsets of data corresponding to the quantity of work items.
Inventors: |
GRUNWALD; DAVID; (SAN
FRANCISCO, CA) ; KARR; RONALD; (PALO ALTO, CA)
; GILL; THOMAS; (MOUNTAIN VIEW, CA) ; ZUO;
DAQUAN; (MOUNTAIN VIEW, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
PURE STORAGE, INC. |
Mountain View |
CA |
US |
|
|
Family ID: |
68533191 |
Appl. No.: |
16/050698 |
Filed: |
July 31, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62674570 |
May 21, 2018 |
|
|
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62695433 |
Jul 9, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 11/2082 20130101;
G06F 2201/82 20130101; G06F 11/2097 20130101; H04L 69/40 20130101;
H04L 2012/5625 20130101; G06F 2201/84 20130101; G06F 16/1824
20190101; G06F 16/907 20190101; H04L 67/1095 20130101; G06F 11/2069
20130101; G06F 3/065 20130101; G06F 11/2094 20130101; G06F 3/0653
20130101; G06F 3/067 20130101; H04L 49/253 20130101; G06F 3/0614
20130101; G06F 3/0647 20130101; G06F 11/1446 20130101; G06F 3/0617
20130101; G06F 2201/805 20130101; G06F 2201/815 20130101; G06F
3/0635 20130101; H04L 49/356 20130101; G06F 16/275 20190101; H04L
67/1097 20130101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06F 3/06 20060101 G06F003/06 |
Claims
1. A method of asynchronously replicating synchronously replicated
data, the method comprising: determining, at a target storage
system, multiple work items corresponding to a dataset stored among
multiple source storage systems, wherein each respective work item
corresponds to a respective subset of the dataset; and for each
session from among a plurality of sessions operating on the target
storage system: determining one or more computing environment
factors affecting performance of replication of data from one or
more of the multiple source storage systems to the target storage
system; identifying, for a given session and based on the one or
more computing environment factors, a respective source storage
system and a quantity of work items; and replicating, from the
respective storage system, one or more subsets of data
corresponding to the quantity of work items.
2. The method of claim 1, wherein the dataset is synchronously
replicated among the one or more source storage systems.
3. The method of claim 1, wherein at least one of the one or more
source storage systems notifies the target storage system that the
dataset is ready to be replicated.
4. The method of claim 1, wherein the one or more of the multiple
source storage systems are selected based on the one or more
computing environment factors, and wherein the one or more source
storage systems are less than all of the multiple source storage
systems.
5. The method of claim 1, wherein the dataset is a snapshot.
6. The method of claim 1, wherein the target storage system
requests metadata describing the dataset, wherein each respective
work item is based on the metadata, and wherein the metadata
describes a hierarchical structure corresponding to distinct blocks
of data that make up the dataset.
7. The method of claim 1, wherein determining the one or more
computing environment factors occurs prior to identification of
work items to perform.
8. A storage system for asynchronously replicating a synchronously
replicated dataset, the storage system comprising a computer
processor and a computer memory operatively coupled to the computer
processor, the computer memory storing computer program
instructions that, when executed by the computer processor, cause
the storage system to carry out the steps of: determine, at a
target storage system, multiple work items corresponding to a
dataset stored among multiple source storage systems, wherein each
respective work item corresponds to a respective subset of the
dataset; and for each session from among a plurality of sessions
operating on the target storage system: determine one or more
computing environment factors affecting performance of replication
of data from one or more of the multiple source storage systems to
the target storage system; identify, for a given session and based
on the one or more computing environment factors, a respective
source storage system and a quantity of work items; and replicate,
from the respective storage system, one or more subsets of data
corresponding to the quantity of work items.
9. The storage system of claim 8, wherein the dataset is
synchronously replicated among the one or more source storage
systems.
10. The storage system of claim 8, wherein at least one of the one
or more source storage systems notifies the target storage system
that the dataset is ready to be replicated.
11. The storage system of claim 8, wherein the one or more of the
multiple source storage systems are selected based on the one or
more computing environment factors, and wherein the one or more
source storage systems are less than all of the multiple source
storage systems.
12. The storage system of claim 8, wherein the dataset is a
snapshot.
13. The storage system of claim 8, wherein the target storage
system requests metadata describing the dataset, wherein each
respective work item is based on the metadata, and wherein the
metadata describes a hierarchical structure corresponding to
distinct blocks of data that make up the dataset.
14. The storage system of claim 8, wherein determining the one or
more computing environment factors occurs prior to identification
of work items to perform.
15. An apparatus for asynchronously replicating a synchronously
replicated dataset, the apparatus comprising a computer processor
and a computer memory operatively coupled to the computer
processor, the computer memory storing computer program
instructions that, when executed by the computer processor, cause
the apparatus to carry out the steps of: determine, at a target
storage system, multiple work items corresponding to a dataset
stored among multiple source storage systems, wherein each
respective work item corresponds to a respective subset of the
dataset; and for each session from among a plurality of sessions
operating on the target storage system: determine one or more
computing environment factors affecting performance of replication
of data from one or more of the multiple source storage systems to
the target storage system; identify, for a given session and based
on the one or more computing environment factors, a respective
source storage system and a quantity of work items; and replicate,
from the respective storage system, one or more subsets of data
corresponding to the quantity of work items.
16. The apparatus of claim 15, wherein the dataset is synchronously
replicated among the one or more source storage systems.
17. The apparatus of claim 15, wherein at least one of the one or
more source storage systems notifies the target storage system that
the dataset is ready to be replicated.
18. The apparatus of claim 15, wherein the one or more of the
multiple source storage systems are selected based on the one or
more computing environment factors, and wherein the one or more
source storage systems are less than all of the multiple source
storage systems.
19. The apparatus of claim 15, wherein the target storage system
requests metadata describing the dataset, wherein each respective
work item is based on the metadata, and wherein the metadata
describes a hierarchical structure corresponding to distinct blocks
of data that make up the dataset.
20. The apparatus of claim 15, wherein determining the one or more
computing environment factors occurs prior to identification of
work items to perform.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a non-provisional application for patent
entitled to a filing date and claiming the benefit of earlier-filed
U.S. Provisional Patent Application Ser. No. 62/674,570, filed May
21, 2018, and U.S. Provisional Patent Application Ser. No.
62/695,433, filed Jul. 9, 2018.
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. 4 sets forth a block diagram illustrating a plurality
of storage systems that support a pod according to some embodiments
of the present disclosure.
[0016] FIG. 5 sets forth a block diagram illustrating a plurality
of storage systems that support a pod according to some embodiments
of the present disclosure.
[0017] FIG. 6 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. 7 sets forth diagrams of metadata representations that
may be implemented as a structured collection of metadata objects
that may represent a logical volume of storage data, or a portion
of a logical volume, in accordance with some embodiments of the
present disclosure.
[0019] FIG. 8 sets forth a diagram of a computing environment for
asynchronously replicating a synchronously replicated dataset
according to some embodiments of the present disclosure.
[0020] FIG. 9 sets forth a flow chart illustrating an example
method of asynchronously replicating a synchronously replicated
dataset according to some embodiments of the present
disclosure.
DESCRIPTION OF EMBODIMENTS
[0021] Example methods, apparatus, and products for asynchronous
replication of synchronously replicated data 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.
[0022] 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.
[0023] 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.
[0024] 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 (IF), HyperText Transfer Protocol
(`HTTP`), Wireless Access Protocol (`WAP`), Handheld Device
Transport Protocol (`HDTP`), Session Initiation Protocol (`SIP`),
Real Time Protocol (`RTP`), or the like.
[0025] 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 110 (also referred to as "controller" herein). A
storage array controller 110 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
110 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.
[0026] Storage array controller 110 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 110 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 110 may be independently coupled to the LAN 160. In
implementations, storage array controller 110 may include an I/O
controller or the like that couples the storage array controller
110 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).
[0027] In some implementations, the NVRAM devices of a persistent
storage resource 170A-B may be configured to receive, from the
storage array controller 110, 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 110 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 110 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.
[0028] 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
may include mechanical or spinning hard disk, such as hard-disk
drives (`HDD`).
[0029] In some implementations, the storage array controllers 110
may be configured for offloading device management responsibilities
from storage drive 171A-F in storage array 102A-B. For example,
storage array controllers 110 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 110, 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 110. 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 110 in conjunction
with storage drives 171A-F to quickly identify the memory blocks
that contain control information. For example, the storage
controllers 110 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.
[0030] In implementations, storage array controllers 110 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 110 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 110 that
includes the location of control information for the storage drive
171A-F. Responsive to receiving the response message, storage array
controllers 110 may issue a request to read data stored at the
address associated with the location of control information for the
storage drives 171A-F.
[0031] In other implementations, the storage array controllers 110
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.
[0032] In implementations, storage array 102A-B may implement two
or more storage array controllers 110. 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 110 (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 110 (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 110 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.
[0033] 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.
[0034] In implementations, storage array controllers 110 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 110 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.
[0035] FIG. 1B illustrates an example system for data storage, in
accordance with some implementations. Storage array controller 101
illustrated in FIG. 1B may similar to the storage array controllers
110 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.
[0036] 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.
[0037] 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.
[0038] 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.
[0039] 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.
[0040] 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.
[0041] 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.
[0042] 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.
[0043] 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.
[0044] 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.
[0045] 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.
[0046] 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.
[0047] 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.
[0048] 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.
[0049] In one embodiment, system 117 includes a dual Peripheral
Component Interconnect (`PCI`) flash storage device 118 with
separately addressable fast write storage. System 117 may include a
storage controller 119. In one embodiment, storage controller 119
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 119 as an
addressable collection of Flash pages, erase blocks, and/or control
elements sufficient to allow the storage device controller 119 to
program and retrieve various aspects of the Flash. In one
embodiment, storage device controller 119 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.
[0050] 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 119 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.
[0051] In one embodiment, system 119 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 119 may write the
contents of RAM to Flash Memory if the storage device controller
detects loss of external power.
[0052] 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 119 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.
[0053] 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 119 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
119 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.
[0054] 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.
[0055] In one embodiment, the stored energy device 122 may be
sufficient to ensure completion of in-progress operations to the
Flash memory devices 107a-120n stored energy device 122 may power
storage device controller 119 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.
[0056] 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.
[0057] 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.
[0058] 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.
[0059] 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.
[0060] 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.
[0061] A storage device controller 119 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.
[0062] 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.
[0063] 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.
[0064] 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.
[0065] 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] FIG. 2B is a block diagram showing a communications
interconnect 171A-F and power distribution bus 172 coupling
multiple storage nodes 150. Referring back to FIG. 2A, the
communications interconnect 171A-F 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 171A-F 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 171A-F, 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.
[0073] 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.
[0074] 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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.
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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.
[0085] 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.
[0086] 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.
[0087] 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.
[0088] 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).
[0089] 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.
[0090] 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.
[0091] 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.
[0092] 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.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] 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.
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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.
[0105] Although not explicitly depicted in FIG. 3A, readers will
appreciate that additional hardware components and additional
software components may be necessary to facilitate the delivery of
cloud services to the storage system 306 and users of the storage
system 306. For example, the storage system 306 may be coupled to
(or even include) a cloud storage gateway. Such a cloud storage
gateway may be embodied, for example, as hardware-based or
software-based appliance that is located on premise with the
storage system 306. Such a cloud storage gateway may operate as a
bridge between local applications that are executing on the storage
array 306 and remote, cloud-based storage that is utilized by the
storage array 306. Through the use of a cloud storage gateway,
organizations may move primary iSCSI or NAS to the cloud services
provider 302, thereby enabling the organization to save space on
their on-premises storage systems. Such a cloud storage gateway may
be configured to emulate a disk array, a block-based device, a file
server, or other storage system that can translate the SCSI
commands, file server commands, or other appropriate command into
REST-space protocols that facilitate communications with the cloud
services provider 302.
[0106] In order to enable the storage system 306 and users of the
storage system 306 to make use of the services provided by the
cloud services provider 302, a cloud migration process may take
place during which data, applications, or other elements from an
organization's local systems (or even from another cloud
environment) are moved to the cloud services provider 302. In order
to successfully migrate data, applications, or other elements to
the cloud services provider's 302 environment, middleware such as a
cloud migration tool may be utilized to bridge gaps between the
cloud services provider's 302 environment and an organization's
environment. Such cloud migration tools may also be configured to
address potentially high network costs and long transfer times
associated with migrating large volumes of data to the cloud
services provider 302, as well as addressing security concerns
associated with sensitive data to the cloud services provider 302
over data communications networks. In order to further enable the
storage system 306 and users of the storage system 306 to make use
of the services provided by the cloud services provider 302, a
cloud orchestrator may also be used to arrange and coordinate
automated tasks in pursuit of creating a consolidated process or
workflow. Such a cloud orchestrator may perform tasks such as
configuring various components, whether those components are cloud
components or on-premises components, as well as managing the
interconnections between such components. The cloud orchestrator
can simplify the inter-component communication and connections to
ensure that links are correctly configured and maintained.
[0107] In the example depicted in FIG. 3A, and as described briefly
above, 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 usage of a 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. Such applications may take many forms
in accordance with various embodiments of the present disclosure.
For example, the cloud services provider 302 may be configured to
provide access to data analytics applications to the storage system
306 and users of the storage system 306. Such data analytics
applications may be configured, for example, to receive telemetry
data phoned home by the storage system 306. Such telemetry data may
describe various operating characteristics of the storage system
306 and may be analyzed, for example, to determine the health of
the storage system 306, to identify workloads that are executing on
the storage system 306, to predict when the storage system 306 will
run out of various resources, to recommend configuration changes,
hardware or software upgrades, workflow migrations, or other
actions that may improve the operation of the storage system
306.
[0108] 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.
[0109] 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.
[0110] 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.
In some embodiments, the storage resources 308 may include
non-volatile magnetoresistive random-access memory (`MRAM`),
including spin transfer torque (`STT`) MRAM, in which data is
stored through the use of magnetic storage elements. In some
embodiments, the example storage resources 308 may include
non-volatile phase-change memory (`PCM`) that may have the ability
to hold multiple bits in a single cell as cells can achieve a
number of distinct intermediary states. In some embodiments, the
storage resources 308 may include quantum memory that allows for
the storage and retrieval of photonic quantum information. In some
embodiments, the example storage resources 308 may include
resistive random-access memory (`ReRAM`) in which data is stored by
changing the resistance across a dielectric solid-state material.
In some embodiments, the storage resources 308 may include storage
class memory (`SCM`) in which solid-state nonvolatile memory may be
manufactured at a high density using some combination of
sub-lithographic patterning techniques, multiple bits per cell,
multiple layers of devices, and so on. Readers will appreciate that
other forms of computer memories and storage devices may be
utilized by the storage systems described above, including DRAM,
SRAM, EEPROM, universal memory, and many others. The storage
resources 308 depicted in FIG. 3A may be embodied in a variety of
form factors, including but not limited to, dual in-line memory
modules (`DIMMs`), non-volatile dual in-line memory modules
(`NVDIMMs`), M.2, U.2, and others.
[0111] The example storage system 306 depicted in FIG. 3B may
implement a variety of storage architectures. For example, storage
systems in accordance with some embodiments of the present
disclosure may utilize block storage where data is stored in
blocks, and each block essentially acts as an individual hard
drive. Storage systems in accordance with some embodiments of the
present disclosure may utilize object storage, where data is
managed as objects. Each object may include the data itself, a
variable amount of metadata, and a globally unique identifier,
where object storage can be implemented at multiple levels (e.g.,
device level, system level, interface level). Storage systems in
accordance with some embodiments of the present disclosure utilize
file storage in which data is stored in a hierarchical structure.
Such data may be saved in files and folders, and presented to both
the system storing it and the system retrieving it in the same
format.
[0112] The example storage system 306 depicted in FIG. 3B may be
embodied as a storage system in which additional storage resources
can be added through the use of a scale-up model, additional
storage resources can be added through the use of a scale-out
model, or through some combination thereof. In a scale-up model,
additional storage may be added by adding additional storage
devices. In a scale-out model, however, additional storage nodes
may be added to a cluster of storage nodes, where such storage
nodes can include additional processing resources, additional
networking resources, and so on.
[0113] 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.
[0114] 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.
[0115] 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.
Readers will appreciate that such data protection techniques may be
carried out, for example, by system software executing on computer
hardware within the storage system, by a cloud services provider,
or in other ways. Such data protection techniques can include, for
example, data archiving techniques that cause data that is no
longer actively used to be moved to a separate storage device or
separate storage system for long-term retention, data backup
techniques through which data stored in the storage system may be
copied and stored in a distinct location to avoid data loss in the
event of equipment failure or some other form of catastrophe with
the storage system, data replication techniques through which data
stored in the storage system is replicated to another storage
system such that the data may be accessible via multiple storage
systems, data snapshotting techniques through which the state of
data within the storage system is captured at various points in
time, data and database cloning techniques through which duplicate
copies of data and databases may be created, and other data
protection techniques. Through the use of such data protection
techniques, business continuity and disaster recovery objectives
may be met as a failure of the storage system may not result in the
loss of data stored in the storage system.
[0116] The software resources 314 may also include software that is
useful in implementing software-defined storage (`SDS`). In such an
example, the software resources 314 may include one or more modules
of computer program instructions that, when executed, are useful in
policy-based provisioning and management of data storage that is
independent of the underlying hardware. Such software resources 314
may be useful in implementing storage virtualization to separate
the storage hardware from the software that manages the storage
hardware.
[0117] The software resources 314 may also include software that is
useful in facilitating and optimizing I/O operations that are
directed to the storage resources 308 in the storage system 306.
For example, the software resources 314 may include software
modules that perform carry out various data reduction techniques
such as, for example, data compression, data deduplication, and
others. The software resources 314 may include software modules
that intelligently group together I/O operations to facilitate
better usage of the underlying storage resource 308, software
modules that perform data migration operations to migrate from
within a storage system, as well as software modules that perform
other functions. Such software resources 314 may be embodied as one
or more software containers or in many other ways.
[0118] Readers will appreciate that the various components depicted
in FIG. 3B may be grouped into one or more optimized computing
packages as converged infrastructures. Such converged
infrastructures may include pools of computers, storage and
networking resources that can be shared by multiple applications
and managed in a collective manner using policy-driven processes.
Such converged infrastructures may minimize compatibility issues
between various components within the storage system 306 while also
reducing various costs associated with the establishment and
operation of the storage system 306. Such converged infrastructures
may be implemented with a converged infrastructure reference
architecture, with standalone appliances, with a software driven
hyper-converged approach (e.g., hyper-converged infrastructures),
or in other ways.
[0119] 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.
[0120] The storage systems described above may operate to support a
wide variety of applications. In view of the fact that the storage
systems include compute resources, storage resources, and a wide
variety of other resources, the storage systems may be well suited
to support applications that are resource intensive such as, for
example, AI applications. Such AI applications may enable devices
to perceive their environment and take actions that maximize their
chance of success at some goal. Examples of such AI applications
can include IBM
[0121] Watson, Microsoft Oxford, Google DeepMind, Baidu Minwa, and
others. The storage systems described above may also be well suited
to support other types of applications that are resource intensive
such as, for example, machine learning applications. Machine
learning applications may perform various types of data analysis to
automate analytical model building. Using algorithms that
iteratively learn from data, machine learning applications can
enable computers to learn without being explicitly programmed.
[0122] In addition to the resources already described, the storage
systems described above may also include graphics processing units
(`GPUs`), occasionally referred to as visual processing unit
(`VPUs`). Such GPUs may be embodied as specialized electronic
circuits that rapidly manipulate and alter memory to accelerate the
creation of images in a frame buffer intended for output to a
display device. Such GPUs may be included within any of the
computing devices that are part of the storage systems described
above, including as one of many individually scalable components of
a storage system, where other examples of individually scalable
components of such storage system can include storage components,
memory components, compute components (e.g., CPUs, FPGAs, ASICs),
networking components, software components, and others. In addition
to GPUs, the storage systems described above may also include
neural network processors (`NNPs`) for use in various aspects of
neural network processing. Such NNPs may be used in place of (or in
addition to) GPUs and may be also be independently scalable.
[0123] As described above, the storage systems described herein may
be configured to support artificial intelligence applications,
machine learning applications, big data analytics applications, and
many other types of applications. The rapid growth in these sorts
of applications is being driven by three technologies: deep
learning (DL), GPU processors, and Big Data. Deep learning is a
computing model that makes use of massively parallel neural
networks inspired by the human brain. Instead of experts
handcrafting software, a deep learning model writes its own
software by learning from lots of examples. A GPU is a modern
processor with thousands of cores, well-suited to run algorithms
that loosely represent the parallel nature of the human brain.
[0124] Advances in deep neural networks have ignited a new wave of
algorithms and tools for data scientists to tap into their data
with artificial intelligence (AI). With improved algorithms, larger
data sets, and various frameworks (including open-source software
libraries for machine learning across a range of tasks), data
scientists are tackling new use cases like autonomous driving
vehicles, natural language processing, and many others. Training
deep neural networks, however, requires both high quality input
data and large amounts of computation. GPUs are massively parallel
processors capable of operating on large amounts of data
simultaneously. When combined into a multi-GPU cluster, a high
throughput pipeline may be required to feed input data from storage
to the compute engines. Deep learning is more than just
constructing and training models. There also exists an entire data
pipeline that must be designed for the scale, iteration, and
experimentation necessary for a data science team to succeed.
[0125] Data is the heart of modern AI and deep learning algorithms.
Before training can begin, one problem that must be addressed
revolves around collecting the labeled data that is crucial for
training an accurate AI model. A full scale AI deployment may be
required to continuously collect, clean, transform, label, and
store large amounts of data. Adding additional high quality data
points directly translates to more accurate models and better
insights. Data samples may undergo a series of processing steps
including, but not limited to: 1) ingesting the data from an
external source into the training system and storing the data in
raw form, 2) cleaning and transforming the data in a format
convenient for training, including linking data samples to the
appropriate label, 3) exploring parameters and models, quickly
testing with a smaller dataset, and iterating to converge on the
most promising models to push into the production cluster, 4)
executing training phases to select random batches of input data,
including both new and older samples, and feeding those into
production GPU servers for computation to update model parameters,
and 5) evaluating including using a holdback portion of the data
not used in training in order to evaluate model accuracy on the
holdout data. This lifecycle may apply for any type of parallelized
machine learning, not just neural networks or deep learning. For
example, standard machine learning frameworks may rely on CPUs
instead of GPUs but the data ingest and training workflows may be
the same. Readers will appreciate that a single shared storage data
hub creates a coordination point throughout the lifecycle without
the need for extra data copies among the ingest, preprocessing, and
training stages. Rarely is the ingested data used for only one
purpose, and shared storage gives the flexibility to train multiple
different models or apply traditional analytics to the data.
[0126] Readers will appreciate that each stage in the AI data
pipeline may have varying requirements from the data hub (e.g., the
storage system or collection of storage systems). Scale-out storage
systems must deliver uncompromising performance for all manner of
access types and patterns--from small, metadata-heavy to large
files, from random to sequential access patterns, and from low to
high concurrency. The storage systems described above may serve as
an ideal AI data hub as the systems may service unstructured
workloads. In the first stage, data is ideally ingested and stored
on to the same data hub that following stages will use, in order to
avoid excess data copying. The next two steps can be done on a
standard compute server that optionally includes a GPU, and then in
the fourth and last stage, full training production jobs are run on
powerful GPU-accelerated servers. Often, there is a production
pipeline alongside an experimental pipeline operating on the same
dataset. Further, the GPU-accelerated servers can be used
independently for different models or joined together to train on
one larger model, even spanning multiple systems for distributed
training. If the shared storage tier is slow, then data must be
copied to local storage for each phase, resulting in wasted time
staging data onto different servers. The ideal data hub for the AI
training pipeline delivers performance similar to data stored
locally on the server node while also having the simplicity and
performance to enable all pipeline stages to operate
concurrently.
[0127] A data scientist works to improve the usefulness of the
trained model through a wide variety of approaches: more data,
better data, smarter training, and deeper models. In many cases,
there will be teams of data scientists sharing the same datasets
and working in parallel to produce new and improved training
models. Often, there is a team of data scientists working within
these phases concurrently on the same shared datasets. Multiple,
concurrent workloads of data processing, experimentation, and
full-scale training layer the demands of multiple access patterns
on the storage tier. In other words, storage cannot just satisfy
large file reads, but must contend with a mix of large and small
file reads and writes. Finally, with multiple data scientists
exploring datasets and models, it may be critical to store data in
its native format to provide flexibility for each user to
transform, clean, and use the data in a unique way. The storage
systems described above may provide a natural shared storage home
for the dataset, with data protection redundancy (e.g., by using
RAID6) and the performance necessary to be a common access point
for multiple developers and multiple experiments. Using the storage
systems described above may avoid the need to carefully copy
subsets of the data for local work, saving both engineering and
GPU-accelerated servers use time. These copies become a constant
and growing tax as the raw data set and desired transformations
constantly update and change.
[0128] Readers will appreciate that a fundamental reason why deep
learning has seen a surge in success is the continued improvement
of models with larger data set sizes. In contrast, classical
machine learning algorithms, like logistic regression, stop
improving in accuracy at smaller data set sizes. As such, the
separation of compute resources and storage resources may also
allow independent scaling of each tier, avoiding many of the
complexities inherent in managing both together. As the data set
size grows or new data sets are considered, a scale out storage
system must be able to expand easily. Similarly, if more concurrent
training is required, additional GPUs or other compute resources
can be added without concern for their internal storage.
Furthermore, the storage systems described above may make building,
operating, and growing an AI system easier due to the random read
bandwidth provided by the storage systems, the ability to of the
storage systems to randomly read small files (50 KB) high rates
(meaning that no extra effort is required to aggregate individual
data points to make larger, storage-friendly files), the ability of
the storage systems to scale capacity and performance as either the
dataset grows or the throughput requirements grow, the ability of
the storage systems to support files or objects, the ability of the
storage systems to tune performance for large or small files (i.e.,
no need for the user to provision filesystems), the ability of the
storage systems to support non-disruptive upgrades of hardware and
software even during production model training, and for many other
reasons.
[0129] Small file performance of the storage tier may be critical
as many types of inputs, including text, audio, or images will be
natively stored as small files. If the storage tier does not handle
small files well, an extra step will be required to pre-process and
group samples into larger files. Storage, built on top of spinning
disks, that relies on SSD as a caching tier, may fall short of the
performance needed. Because training with random input batches
results in more accurate models, the entire data set must be
accessible with full performance. SSD caches only provide high
performance for a small subset of the data and will be ineffective
at hiding the latency of spinning drives.
[0130] Readers will appreciate that the storage systems described
above may be configured to support the storage of (among of types
of data) blockchains. Such blockchains may be embodied as a
continuously growing list of records, called blocks, which are
linked and secured using cryptography. Each block in a blockchain
may contain a hash pointer as a link to a previous block, a
timestamp, transaction data, and so on. Blockchains may be designed
to be resistant to modification of the data and can serve as an
open, distributed ledger that can record transactions between two
parties efficiently and in a verifiable and permanent way. This
makes blockchains potentially suitable for the recording of events,
medical records, and other records management activities, such as
identity management, transaction processing, and others.
[0131] Readers will further appreciate that in some embodiments,
the storage systems described above may be paired with other
resources to support the applications described above. For example,
one infrastructure could include primary compute in the form of
servers and workstations which specialize in using General-purpose
computing on graphics processing units (`GPGPU`) to accelerate deep
learning applications that are interconnected into a computation
engine to train parameters for deep neural networks. Each system
may have Ethernet external connectivity, InfiniBand external
connectivity, some other form of external connectivity, or some
combination thereof. In such an example, the GPUs can be grouped
for a single large training or used independently to train multiple
models. The infrastructure could also include a storage system such
as those described above to provide, for example, a scale-out
all-flash file or object store through which data can be accessed
via high-performance protocols such as NFS, S3, and so on. The
infrastructure can also include, for example, redundant top-of-rack
Ethernet switches connected to storage and compute via ports in
MLAG port channels for redundancy. The infrastructure could also
include additional compute in the form of whitebox servers,
optionally with GPUs, for data ingestion, pre-processing, and model
debugging. Readers will appreciate that additional infrastructures
are also be possible.
[0132] Readers will appreciate that the systems described above may
be better suited for the applications described above relative to
other systems that may include, for example, a distributed
direct-attached storage (DDAS) solution deployed in server nodes.
Such DDAS solutions may be built for handling large, less
sequential accesses but may be less able to handle small, random
accesses. Readers will further appreciate that the storage systems
described above may be utilized to provide a platform for the
applications described above that is preferable to the utilization
of cloud-based resources as the storage systems may be included in
an on-site or in-house infrastructure that is more secure, more
locally and internally managed, more robust in feature sets and
performance, or otherwise preferable to the utilization of
cloud-based resources as part of a platform to support the
applications described above. For example, services built on
platforms such as IBM's Watson may require a business enterprise to
distribute individual user information, such as financial
transaction information or identifiable patient records, to other
institutions. As such, cloud-based offerings of AI as a service may
be less desirable than internally managed and offered AI as a
service that is supported by storage systems such as the storage
systems described above, for a wide array of technical reasons as
well as for various business reasons.
[0133] Readers will appreciate that the storage systems described
above, either alone or in coordination with other computing
machinery may be configured to support other AI related tools. For
example, the storage systems may make use of tools like ONXX or
other open neural network exchange formats that make it easier to
transfer models written in different AI frameworks. Likewise, the
storage systems may be configured to support tools like Amazon's
Gluon that allow developers to prototype, build, and train deep
learning models."
[0134] Readers will further appreciate that the storage systems
described above may also be deployed as an edge solution. Such an
edge solution may be in place to optimize cloud computing systems
by performing data processing at the edge of the network, near the
source of the data. Edge computing can push applications, data and
computing power (i.e., services) away from centralized points to
the logical extremes of a network. Through the use of edge
solutions such as the storage systems described above,
computational tasks may be performed using the compute resources
provided by such storage systems, data may be storage using the
storage resources of the storage system, and cloud-based services
may be accessed through the use of various resources of the storage
system (including networking resources). By performing
computational tasks on the edge solution, storing data on the edge
solution, and generally making use of the edge solution, the
consumption of expensive cloud-based resources may be avoided and,
in fact, performance improvements may be experienced relative to a
heavier reliance on cloud-based resources.
[0135] While many tasks may benefit from the utilization of an edge
solution, some particular uses may be especially suited for
deployment in such an environment. For example, devices like
drones, autonomous cars, robots, and others may require extremely
rapid processing--so fast, in fact, that sending data up to a cloud
environment and back to receive data processing support may simply
be too slow. Likewise, machines like locomotives and gas turbines
that generate large amounts of information through the use of a
wide array of data-generating sensors may benefit from the rapid
data processing capabilities of an edge solution. As an additional
example, some IoT devices such as connected video cameras may not
be well-suited for the utilization of cloud-based resources as it
may be impractical (not only from a privacy perspective, security
perspective, or a financial perspective) to send the data to the
cloud simply because of the pure volume of data that is involved.
As such, many tasks that really on data processing, storage, or
communications may be better suited by platforms that include edge
solutions such as the storage systems described above.
[0136] Consider a specific example of inventory management in a
warehouse, distribution center, or similar location. A large
inventory, warehousing, shipping, order-fulfillment, manufacturing
or other operation has a large amount of inventory on inventory
shelves, and high resolution digital cameras that produce a
firehose of large data. All of this data may be taken into an image
processing system, which may reduce the amount of data to a
firehose of small data. All of the small data may be stored
on-premises in storage. The on-premises storage, at the edge of the
facility, may be coupled to the cloud, for external reports,
real-time control and cloud storage. Inventory management may be
performed with the results of the image processing, so that
inventory can be tracked on the shelves and restocked, moved,
shipped, modified with new products, or discontinued/obsolescent
products deleted, etc. The above scenario is a prime candidate for
an embodiment of the configurable processing and storage systems
described above. A combination of compute-only blades and offload
blades suited for the image processing, perhaps with deep learning
on offload-FPGA or offload-custom blade(s) could take in the
firehose of large data from all of the digital cameras, and produce
the firehose of small data. All of the small data could then be
stored by storage nodes, operating with storage units in whichever
combination of types of storage blades best handles the data flow.
This is an example of storage and function acceleration and
integration. Depending on external communication needs with the
cloud, and external processing in the cloud, and depending on
reliability of network connections and cloud resources, the system
could be sized for storage and compute management with bursty
workloads and variable conductivity reliability. Also, depending on
other inventory management aspects, the system could be configured
for scheduling and resource management in a hybrid edge/cloud
environment.
[0137] The storage systems described above may also be optimized
for use in big data analytics. Big data analytics may be generally
described as the process of examining large and varied data sets to
uncover hidden patterns, unknown correlations, market trends,
customer preferences and other useful information that can help
organizations make more-informed business decisions. Big data
analytics applications enable data scientists, predictive modelers,
statisticians and other analytics professionals to analyze growing
volumes of structured transaction data, plus other forms of data
that are often left untapped by conventional business intelligence
(BI) and analytics programs. As part of that process,
semi-structured and unstructured data such as, for example,
internet clickstream data, web server logs, social media content,
text from customer emails and survey responses, mobile-phone
call-detail records, IoT sensor data, and other data may be
converted to a structured form. Big data analytics is a form of
advanced analytics, which involves complex applications with
elements such as predictive models, statistical algorithms and
what-if analyses powered by high-performance analytics systems.
[0138] The storage systems described above may also support
(including implementing as a system interface) applications that
perform tasks in response to human speech. For example, the storage
systems may support the execution intelligent personal assistant
applications such as, for example, Amazon's Alexa, Apple Siri,
Google Voice, Samsung Bixby, Microsoft Cortana, and others. While
the examples described in the previous sentence make use of voice
as input, the storage systems described above may also support
chatbots, talkbots, chatterbots, or artificial conversational
entities or other applications that are configured to conduct a
conversation via auditory or textual methods. Likewise, the storage
system may actually execute such an application to enable a user
such as a system administrator to interact with the storage system
via speech. Such applications are generally capable of voice
interaction, music playback, making to-do lists, setting alarms,
streaming podcasts, playing audiobooks, and providing weather,
traffic, and other real time information, such as news, although in
embodiments in accordance with the present disclosure, such
applications may be utilized as interfaces to various system
management operations.
[0139] The storage systems described above may also implement AI
platforms for delivering on the vision of self-driving storage.
Such AI platforms may be configured to deliver global predictive
intelligence by collecting and analyzing large amounts of storage
system telemetry data points to enable effortless management,
analytics and support. In fact, such storage systems may be capable
of predicting both capacity and performance, as well as generating
intelligent advice on workload deployment, interaction and
optimization. Such AI platforms may be configured to scan all
incoming storage system telemetry data against a library of issue
fingerprints to predict and resolve incidents in real-time, before
they impact customer environments, and captures hundreds of
variables related to performance that are used to forecast
performance load.
[0140] 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.
[0141] 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).
[0142] 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.
[0143] 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.
[0144] 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).
[0145] 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.
[0146] 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.
[0147] 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.
[0148] 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 controllers 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.
[0149] 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.
[0150] 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.
[0151] 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.
[0152] 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.
[0153] 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.
[0154] 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.
[0155] 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
[0156] 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.
[0157] 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.
[0158] 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.
[0159] 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 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 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.
[0160] 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.
[0161] 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.
[0162] 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.
[0163] 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.
[0164] 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.
[0165] 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.
[0166] 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).
[0167] 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.
[0168] 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.
[0169] 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.
[0170] 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.
[0171] 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.
[0172] 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.
[0173] 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.
[0174] 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.
[0175] 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).
[0176] 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.
[0177] 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.
[0178] 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.
[0179] 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.
[0180] 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.
[0181] 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.
[0182] 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.
[0183] 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
[0184] 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.
[0185] 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.
[0186] 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.
[0187] 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.
[0188] 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.
[0189] 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.
[0190] 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.
[0191] 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.
[0192] 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.
[0193] 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).
[0194] 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).
[0195] 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.
[0196] 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.
[0197] 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.
[0198] 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.
[0199] For further explanation, FIG. 7 sets forth diagrams of
metadata representations that may be implemented as a structured
collection of metadata objects that, together, may represent a
logical volume of storage data, or a portion of a logical volume,
in accordance with some embodiments of the present disclosure.
Metadata representations 750, 754, and 760 may be stored within a
storage system (706), and one or more metadata representations may
be generated and maintained for each of multiple storage objects,
such as volumes, or portions of volumes, stored within a storage
system (706).
[0200] While other types of structured collections of the metadata
objects are possible, in this example, metadata representations may
be structured as a directed acyclic graph (DAG) of nodes, where, to
maintain efficient access to any given node, the DAG may be
structured and balanced according to various methods. For example,
a DAG for a metadata representation may be defined as a type of
B-tree, and balanced accordingly in response to changes to the
structure of the metadata representation, where changes to the
metadata representation may occur in response to changes to, or
additions to, underlying data represented by the metadata
representation. While in this example, there are only two levels
for the sake of simplicity, in other examples, metadata
representations may span across multiple levels and may include
hundreds or thousands of nodes, where each node may include any
number of links to other nodes.
[0201] Further, in this example, the leaves of a metadata
representation may include pointers to the stored data for a
volume, or portion of a volume, where a logical address, or a
volume and offset, may be used to identify and navigate through the
metadata representation to reach one or more leaf nodes that
reference stored data corresponding to the logical address. For
example, a volume (752) may be represented by a metadata
representation (750), which includes multiple metadata object nodes
(752, 752A-752N), where leaf nodes (752A-752N) include pointers to
respective data objects (753A-753N, 757). Data objects may be any
size unit of data within a storage system (706). For example, data
objects (753A-753N, 757) may each be a logical extent, where
logical extents may be some specified size, such as 1 MB, 4 MB, or
some other size.
[0202] In this example, a snapshot (756) may be created as a
snapshot of a storage object, in this case, a volume (752), where
at the point in time when the snapshot (756) is created, the
metadata representation (754) for the snapshot (756) includes all
of the metadata objects for the metadata representation (750) for
the volume (752). Further, in response to creation of the snapshot
(756), the metadata representation (754) may be designated to be
read only. However, the volume (752) sharing the metadata
representation may continue to be modified, and while at the moment
the snapshot is created, the metadata representations for the
volume (752) and the snapshot (756) are identical, as modifications
are made to data corresponding to the volume (752), and in response
to the modifications, the metadata representations for the volume
(752) and the snapshot (756) may diverge and become different.
[0203] For example, given a metadata representation (750) to
represent a volume (752) and a metadata representation (754) to
represent a snapshot (756), the storage system (706) may receive an
I/O operation that writes to data that is ultimately stored within
a particular data object (753B), where the data object (753B) is
pointed to by a leaf node pointer (752B), and where the leaf node
pointer (752B) is part of both metadata representations (750, 754).
In response to the write operation, the read only data objects
(753A-753N) referred to by the metadata representation (754) remain
unchanged, and the pointer (752B) may also remain unchanged.
However, the metadata representation (750), which represents the
current volume (752), is modified to include a new data object to
hold the data written by the write operation, where the modified
metadata representation is depicted as the metadata representation
(760). Further, the write operation may be directed to only a
portion of the data object (753B), and consequently, the new data
object (757) may include a copy of previous contents of the data
object (753B) in addition to the payload for the write
operation.
[0204] In this example, as part of processing the write operation,
the metadata representation (760) for the volume (752) is modified
to remove an existing metadata object pointer (752B) and to include
a new metadata object pointer (758), where the new metadata object
pointer (758) is configured to point to a new data object (757),
where the new data object (757) stores the data written by the
write operation. Further, the metadata representation (760) for the
volume (752) continues to include all metadata objects included
within the previous metadata representation (750)--with the
exclusion of the metadata object pointer (752B) that referenced the
target data object, where the metadata object pointer (752B)
continues to reference the read only data object (753B) that would
have been overwritten.
[0205] In this way, using metadata representations, a volume or a
portion of a volume may be considered to be snapshotted, or
considered to be copied, by creating metadata objects, and without
actual duplication of data objects--where the duplication of data
objects may be deferred until a write operation is directed at one
of the read only data objects referred to by the metadata
representations.
[0206] In other words, an advantage of using a metadata
representation to represent a volume is that a snapshot or a copy
of a volume may be created and be accessible in constant order
time, and specifically, in the time it takes to create a metadata
object for the snapshot or copy, and to create a reference for the
snapshot or copy metadata object to the existing metadata
representation for the volume being snapshotted or copied.
[0207] As an example use, a virtualized copy-by-reference may make
use of a metadata representation in a manner that is similar to the
use of a metadata representation in creating a snapshot of a
volume--where a metadata representation for a virtualized
copy-by-reference may often correspond to a portion of a metadata
representation for an entire volume. An example implementation of
virtualized copy-by-reference may be within the context of a
virtualized storage system, where multiple block ranges within and
between volumes may reference a unified copy of stored data. In
such virtualized storage system, the metadata described above may
be used to handle the relationship between virtual, or logical,
addresses and physical, or real, addresses--in other words, the
metadata representation of stored data enables a virtualized
storage system that may be considered flash-friendly in that it
reduces, or minimizes, wear on flash memory.
[0208] In some examples, logical extents may be combined in various
ways, including as simple collections or as logically related
address ranges within some larger-scale logical extent that is
formed as a set of logical extent references. These larger
combinations could also be given logical extent identities of
various kinds, and could be further combined into still larger
logical extents or collections. A copy-on-write status could apply
to various layers, and in various ways depending on the
implementation. For example, a copy on write status applied to a
logical collection of logical collections of extents might result
in a copied collection retaining references to unchanged logical
extents and the creation of copied-on-write logical extents
(through copying references to any unchanged stored data blocks as
needed) when only part of the copy-on-write logical collection is
changed.
[0209] Deduplication, volume snapshots, or block range snapshots
may be implemented in this model through combinations of
referencing stored data blocks, or referencing logical extents, or
marking logical extents (or identified collections of logical
extents) as copy-on-write.
[0210] Further, with flash storage systems, stored data blocks may
be organized and grouped together in various ways as collections
are written out into pages that are part of larger erase blocks.
Eventual garbage collection of deleted or replaced stored data
blocks may involve moving content stored in some number of pages
elsewhere so that an entire erase block can be erased and prepared
for reuse. This process of selecting physical flash pages,
eventually migrating and garbage collecting them, and then erasing
flash erase blocks for reuse may or may not be coordinated, driven
by, or performed by the aspect of a storage system that is also
handling logical extents, deduplication, compression, snapshots,
virtual copying, or other storage system functions. A coordinated
or driven process for selecting pages, migrating pages, garbage
collecting and erasing erase blocks may further take into account
various characteristics of the flash memory device cells, pages,
and erase blocks such as number of uses, aging predictions,
adjustments to voltage levels or numbers of retries needed in the
past to recover stored data. They may also take into account
analysis and predictions across all flash memory devices within the
storage system.
[0211] To continue with this example, where a storage system may be
implemented based on directed acyclic graphs comprising logical
extents, logical extents can be categorized into two types: leaf
logical extents, which reference some amount of stored data in some
way, and composite logical extents, which reference other leaf or
composite logical extents.
[0212] A leaf extent can reference data in a variety of ways. It
can point directly to a single range of stored data (e.g., 64
kilobytes of data), or it can be a collection of references to
stored data (e.g., a 1 megabyte "range" of content that maps some
number of virtual blocks associated with the range to physically
stored blocks). In the latter case, these blocks may be referenced
using some identity, and some blocks within the range of the extent
may not be mapped to anything. Also, in that latter case, these
block references need not be unique, allowing multiple mappings
from virtual blocks within some number of logical extents within
and across some number of volumes to map to the same physically
stored blocks. Instead of stored block references, a logical extent
could encode simple patterns: for example, a block which is a
string of identical bytes could simply encode that the block is a
repeated pattern of identical bytes.
[0213] A composite logical extent can be a logical range of content
with some virtual size, which comprises a plurality of maps that
each map from a subrange of the composite logical extent logical
range of content to an underlying leaf or composite logical extent.
Transforming a request related to content for a composite logical
extent, then, involves taking the content range for the request
within the context of the composite logical extent, determining
which underlying leaf or composite logical extents that request
maps to, and transforming the request to apply to an appropriate
range of content within those underlying leaf or composite logical
extents.
[0214] Volumes, or files or other types of storage objects, can be
described as composite logical extents. Thus, these presented
storage objects can be organized using this extent model.
[0215] Depending on implementation, leaf or composite logical
extents could be referenced from a plurality of other composite
logical extents, effectively allowing inexpensive duplication of
larger collections of content within and across volumes. Thus,
logical extents can be arranged essentially within an acyclic graph
of references, each ending in leaf logical extents. This can be
used to make copies of volumes, to make snapshots of volumes, or as
part of supporting virtual range copies within and between volumes
as part of EXTENDED COPY or similar types of operations.
[0216] An implementation may provide each logical extent with an
identity which can be used to name it. This simplifies referencing,
since the references within composite logical extents become lists
comprising logical extent identities and a logical subrange
corresponding to each such logical extent identity. Within logical
extents, each stored data block reference may also be based on some
identity used to name it.
[0217] To support these duplicated uses of extents, we can add a
further capability: copy-on-write logical extents. When a modifying
operation affects a copy-on-write leaf or composite logical extent
the logical extent is copied, with the copy being a new reference
and possibly having a new identity (depending on implementation).
The copy retains all references or identities related to underlying
leaf or composite logical extents, but with whatever modifications
result from the modifying operation. For example, a WRITE, WRITE
SAME, XDWRITEREAD, XPWRITE, or COMPARE AND WRITE request may store
new blocks in the storage system (or use deduplication techniques
to identify existing stored blocks), resulting in modifying the
corresponding leaf logical extents to reference or store identities
to a new set of blocks, possibly replacing references and stored
identities for a previous set of blocks. Alternately, an UNMAP
request may modify a leaf logical extent to remove one or more
block references. In both types of cases, a leaf logical extent is
modified. If the leaf logical extent is copy-on-write, then a new
leaf logical extent will be created that is formed by copying
unaffected block references from the old extent and then replacing
or removing block references based on the modifying operation.
[0218] A composite logical extent that was used to locate the leaf
logical extent may then be modified to store the new leaf logical
extent reference or identity associated with the copied and
modified leaf logical extent as a replacement for the previous leaf
logical extent. If that composite logical extent is copy-on-write,
then a new composite logical extent is created as a new reference
or with a new identity, and any unaffected references or identities
to its underlying logical extents are copied to that new composite
logical extent, with the previous leaf logical extent reference or
identity being replaced with the new leaf logical extent reference
or identity.
[0219] This process continues further backward from referenced
extent to referencing composite extent, based on the search path
through the acyclic graph used to process the modifying operation,
with all copy-on-write logical extents being copied, modified, and
replaced.
[0220] These copied leaf and composite logical extents can then
drop the characteristic of being copy on write, so that further
modifications do not result in an additional copy. For example, the
first time some underlying logical extent within a copy-on-write
"parent" composite extent is modified, that underlying logical
extent may be copied and modified, with the copy having a new
identity which is then written into a copied and replaced instance
of the parent composite logical extent. However, a second time some
other underlying logical extent is copied and modified and with
that other underlying logical extent copy's new identity being
written to the parent composite logical extent, the parent can then
be modified in place with no further copy and replace necessary on
behalf of references to the parent composite logical extent.
[0221] Modifying operations to new regions of a volume or of a
composite logical extent for which there is no current leaf logical
extent may create a new leaf logical extent to store the results of
those modifications. If that new logical extent is to be referenced
from an existing copy-on-write composite logical extent, then that
existing copy-on-write composite logical extent will be modified to
reference the new logical extent, resulting in another copy,
modify, and replace sequence of operations similar to the sequence
for modifying an existing leaf logical extent.
[0222] If a parent composite logical extent cannot be grown large
enough (based on implementation) to cover an address range
associated that includes new leaf logical extents to create for a
new modifying operation, then the parent composite logical extent
may be copied into two or more new composite logical extents which
are then referenced from a single "grandparent" composite logical
extent which yet again is a new reference or a new identity. If
that grandparent logical extent is itself found through another
composite logical extent that is copy-on-write, then that another
composite logical extent will be copied and modified and replaced
in a similar way as described in previous paragraphs. This
copy-on-write model can be used as part of implementing snapshots,
volume copies, and virtual volume address range copies within a
storage system implementation based on these directed acyclic
graphs of logical extents. To make a snapshot as a read-only copy
of an otherwise writable volume, a graph of logical extents
associated with the volume is marked copy-on-write and a reference
to the original composite logical extents are retained by the
snapshot. Modifying operations to the volume will then make logical
extent copies as needed, resulting in the volume storing the
results of those modifying operations and the snapshots retaining
the original content. Volume copies are similar, except that both
the original volume and the copied volume can modify content
resulting in their own copied logical extent graphs and
subgraphs.
[0223] Virtual volume address range copies can operate either by
copying block references within and between leaf logical extents
(which does not itself involve using copy-on-write techniques
unless changes to block references modifies copy-on-write leaf
logical extents). Alternately, virtual volume address range copies
can duplicate references to leaf or composite logical extents,
which works well for volume address range copies of larger address
ranges. Further, this allows graphs to become directed acyclic
graphs of references rather than merely reference trees.
Copy-on-write techniques associated with duplicated logical extent
references can be used to ensure that modifying operations to the
source or target of a virtual address range copy will result in the
creation of new logical extents to store those modifications
without affecting the target or the source that share the same
logical extent immediately after the volume address range copy
operation.
[0224] Input/output operations for pods may also be implemented
based on replicating directed acyclic graphs of logical extents.
For example, each storage system within a pod could implement
private graphs of logical extents, such that the graphs on one
storage system for a pod have no particular relationship to the
graphs on any second storage system for the pod. However, there is
value in synchronizing the graphs between storage systems in a pod.
This can be useful for resynchronization and for coordinating
features such as asynchronous or snapshot based replication to
remote storage systems. Further, it may be useful for reducing some
overhead for handling the distribution of snapshot and copy related
processing. In such a model, keeping the content of a pod in sync
across all in-sync storage systems for a pod is essentially the
same as keeping graphs of leaf and composite logical extents in
sync for all volumes across all in-sync storage systems for the
pod, and ensuring that the content of all logical extents is
in-sync. To be in sync, matching leaf and composite logical extents
should either have the same identity or should have mappable
identities. Mapping could involve some set of intermediate mapping
tables or could involve some other type of identity translation. In
some cases, identities of blocks mapped by leaf logical extents
could also be kept in sync.
[0225] In a pod implementation based on a leader and followers,
with a single leader for each pod, the leader can be in charge of
determining any changes to the logical extent graphs. If a new leaf
or composite logical extent is to be created, it can be given an
identity. If an existing leaf or composite logical extent is to be
copied to form a new logical extent with modifications, the new
logical extent can be described as a copy of a previous logical
extent with some set of modifications. If an existing logical
extent is to be split, the split can be described along with the
new resulting identities. If a logical extent is to be referenced
as an underlying logical extent from some additional composite
logical extent, that reference can be described as a change to the
composite logical extent to reference that underlying logical
extent.
[0226] Modifying operations in a pod thus comprises distributing
descriptions of modifications to logical extent graphs (where new
logical extents are created to extend content or where logical
extents are copied, modified, and replaced to handle copy-on-write
states related to snapshots, volume copies, and volume address
range copies) and distributing descriptions and content for
modifications to the content of leaf logical extents. An additional
benefit that comes from using metadata in the form of directed
acyclic graphs, as described above, is that I/O operations that
modify stored data in physical storage may be given effect at a
user level through the modification of metadata corresponding to
the stored data in physical storage--without modifying the stored
data in physical storage. In the disclosed embodiments of storage
systems, where the physical storage may be a solid state drive, the
wear that accompanies modifications to flash memory may be avoided
or reduced due to I/O operations being given effect through the
modifications of the metadata representing the data targeted by the
I/O operations instead of through the reading, erasing, or writing
of flash memory. Further, as noted above, in such a virtualized
storage system, the metadata described above may be used to handle
the relationship between virtual, or logical, addresses and
physical, or real, addresses--in other words, the metadata
representation of stored data enables a virtualized storage system
that may be considered flash-friendly in that it reduces, or
minimizes, wear on flash memory.
[0227] Leader storage systems may perform their own local
operations to implement these descriptions in the context of their
local copy of the pod dataset and the local storage system's
metadata. Further, the in-sync followers perform their own separate
local operations to implement these descriptions in the context of
their separate local copy of the pod dataset and their separate
local storage system's metadata. When both leader and follower
operations are complete, the result is compatible graphs of logical
extents with compatible leaf logical extent content. These graphs
of logical extents then become a type of "common metadata" as
described in previous examples. This common metadata can be
described as dependencies between modifying operations and required
common metadata. Transformations to graphs can be described as
separate operations within a set of or more predicates that may
describe relationships, such as dependencies, with one or more
other operations. In other words, interdependencies between
operations may be described as a set of precursors that one
operation depends on in some way, where the set of precursors may
be considered predicates that must be true for an operation to
complete. A fuller description of predicates may be found within
application Reference Ser. No. 15/696,418, which is included herein
by reference in its entirety. Alternately, each modifying operation
that relies on a particular same graph transformation that has not
yet been known to complete across the pod can include the parts of
any graph transformation that it relies on. Processing an operation
description that identifies a "new" leaf or composite logical
extent that already exists can avoid creating the new logical
extent since that part was already handled in the processing of
some earlier operation, and can instead implement only the parts of
the operation processing that change the content of leaf or
composite logical extents. It is a role of the leader to ensure
that transformations are compatible with each other. For example,
we can start with two writes come that come in for a pod. A first
write replaces a composite logical extent A with a copy of formed
as composite logical extent B, replaces a leaf logical extent C
with a copy as leaf logical extent D and with modifications to
store the content for the second write, and further writes leaf
logical extent D into composite logical extent B. Meanwhile, a
second write implies the same copy and replacement of composite
logical extent A with composite logical extent B but copies and
replaces a different leaf logical extent E with a logical extent F
which is modified to store the content of the second write, and
further writes logical extent F into logical extent B. In that
case, the description for the first write can include the
replacement of A with B and C with D and the writing of D into
composite logical extent B and the writing of the content of the
first write into leaf extend B; and, the description of the second
write can include the replacement of A with B and E with F and the
writing of F into composite logical extent B, along with the
content of the second write which will be written to leaf extent F.
A leader or any follower can then separately process the first
write or the second write in any order, and the end result is B
copying and replacing A, D copying and replacing C, F copying
replacing E, and D and F being written into composite logical
extent B. A second copy of A to form B can be avoided by
recognizing that B already exists. In this way, a leader can ensure
that the pod maintains compatible common metadata for a logical
extent graph across in-sync storage systems for a pod.
[0228] Given an implementation of storage systems using directed
acyclic graphs of logical extents, recovery of pods based on
replicated directed acyclic graphs of logical extents may be
implemented. Specifically, in this example, recovery in pods may be
based on replicated extent graphs then involves recovering
consistency of these graphs as well as recovering content of leaf
logical extents. In this implementation of recovery, operations may
include querying for graph transformations that are not known to
have completed on all in-sync storage systems for a pod, as well as
all leaf logical extent content modifications that are not known to
have completed across all storage systems for the pod. Such
querying could be based on operations since some coordinated
checkpoint, or could simply be operations not known to have
completed where each storage system keeps a list of operations
during normal operation that have not yet been signaled as
completed. In this example, graph transformations are
straightforward: a graph transformation may create new things, copy
old things to new things, and copy old things into two or more
split new things, or they modify composite extents to modify their
references to other extents. Any stored operation description found
on any in-sync storage system that creates or replaces any logical
extent can be copied and performed on any other storage system that
does not yet have that logical extent. Operations that describe
modifications to leaf or composite logical extents can apply those
modifications to any in-sync storage system that had not yet
applied them, as long as the involved leaf or composite logical
extents have been recovered properly.
[0229] In another example, as an alternative to using a logical
extent graph, storage may be implemented based on a replicated
content-addressable store. In a content-addressable store, for each
block of data (for example, every 512 bytes, 4096 bytes, 8192 bytes
or even 16384 bytes) a unique hash value (sometimes also called a
fingerprint) is calculated, based on the block content, so that a
volume or an extent range of a volume can be described as a list of
references to blocks that have a particular hash value. In a
synchronously replicated storage system implementation based on
references to blocks with the same hash value, replication could
involve a first storage system receiving blocks, calculating
fingerprints for those blocks, identifying block references for
those fingerprints, and delivering changes to one or a plurality of
additional storage systems as updates to the mapping of volume
blocks to referenced blocks. If a block is found to have already
been stored by the first storage system, that storage system can
use its reference to name the reference in each of the additional
storage systems (either because the reference uses the same hash
value or because an identifier for the reference is either
identical or can be mapped readily). Alternately, if a block is not
found by the first storage system, then content of the first
storage system may be delivered to other storage systems as part of
the operation description along with the hash value or identity
associated with that block content. Further, each in-sync storage
system's volume descriptions are then updated with the new block
references. Recovery in such a store may then include comparing
recently updated block references for a volume. If block references
differ between different in-sync storage systems for a pod, then
one version of each reference can be copied to other storage
systems to make them consistent. If the block reference on one
system does not exist, then it be copied from some storage system
that does store a block for that reference. Virtual copy operations
can be supported in such a block or hash reference store by copying
the references as part of implementing the virtual copy
operation.
[0230] For further explanation, FIG. 4 sets forth a flow chart
illustrating an example method for synchronizing metadata among
storage systems synchronously replicating a dataset 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-3C, or any
combination thereof. In fact, the storage systems (402, 404, 406)
depicted in FIG. 4 may include the same, fewer, additional
components as the storage systems described above.
[0231] As described above, metadata may be synchronized among
storage systems that are synchronously replicating a dataset. Such
metadata may be referred to as common metadata, or shared metadata,
that is stored by a storage system on behalf of a pod related to
the mapping of segments of content stored within the pod to virtual
address within storage objects within the pod, where information
related to those mappings is synchronized between member storage
systems for the pod to ensure correct behavior--or better
performance--for storage operations related to the pod. In some
examples, a storage object may implement a volume or a snapshot.
The synchronized metadata may include: (a) information to keep
volume content mappings synchronized among the storage systems in
the pod; (b) tracking data for recovery checkpoints or for
in-progress write operations; (c) information related to the
delivery of data and mapping information to a remote storage system
for asynchronous or periodic replication.
[0232] Information to keep volume content mappings synchronized
among the storage systems in the pod may enable efficient creating
of snapshots, which in turn enables that subsequent updates, copies
of snapshots, or snapshot removals may be performed efficiently and
consistently across the pod member storage systems.
[0233] Tracking data for recovery checkpoints or for in-progress
write operations may enable efficient crash recovery and efficient
detection of content or volume mappings that may have been
partially or completely applied on individual storage systems for a
pod, but that may not have been completely applied on other storage
systems for the pod.
[0234] Information related to the delivery of data and mapping
information to a remote storage system for asynchronous or periodic
replication may enable more than one member storage system for a
pod to serve as a source for the replicated pod content with
minimal concerns for dealing with mismatches in mapping and
differencing metadata used to drive asynchronous or periodic
replication.
[0235] In some examples, shared metadata may include descriptions
for, or indications of, a named grouping, or identifiers for, of
one or more volumes or one or more storage objects that are a
subset of an entire synchronously replicated dataset for a
pod--where such a of volumes or storage objects of a dataset may be
referred to as a consistency group. A consistency group may be
defined to specify a subset of volumes or storage objects of the
dataset to be used for consistent snapshots, asynchronous
replication, or periodic replication. In some examples, a
consistency group may be calculated dynamically, such as by
including all volumes connected to a particular set of hosts or
host network ports, or that are connected to a particular set of
applications or virtual machines or containers, where the
applications, virtual machines, or containers may operate on
external server systems or may operate on one or more of the
storage systems that are members of a pod. In other examples, a
consistency group may be defined according to user selections of a
type of data or set of data, or specifications of a consistency
group similar to the dynamic calculation, where a user may specify,
for example through a command or management console, that a
particular, or named, consistency group be created to include all
volumes connected to a particular set of hosts or host network
ports, or be created to include data for a particular set of
applications or virtual machines or containers.
[0236] In an example using a consistency group, a first consistency
group snapshot of a consistency group may include a first set of
snapshots for all volumes or other storage objects that are members
of the consistency group at the time of the first dataset snapshot,
with a second consistency group snapshot of the same consistency
group including a second set of snapshots for the volumes or other
storage objects that are members of the consistency group at the
time of the second dataset snapshot. In other examples, a snapshot
of the dataset may be stored on one or more target storage systems
in an asynchronous manner. Similarly, asynchronous replication of a
consistency group may account for dynamic changes to member volumes
and other storage objects of the consistency group, where
consistency group snapshots of the consistency group at either the
source or the target of the asynchronous replication link include
the volumes and other storage objects that are members in
relationship to the consistency group at the time that the dataset
snapshot relates to. In the case of a target of an asynchronous
replication connection, the time that the dataset snapshot relates
to depends on the dynamic dataset of the sender as it was received
and was in process at the time of the consistency group snapshot on
the target. For example, if a target of an asynchronous replication
is, say, 2000 operations behind, where some of those operations are
consistency group member changes, where a first set of such changes
are more than 2000 operations ago for the source, and a second set
of changes are within the last 2000, then a consistency group
snapshot at that time on the target will account for the first set
of member changes and will not account for the second set of
changes. Other uses of the target of asynchronous replication may
similarly account for the nature of the time of the dataset for the
consistency group in determining the volumes or other storage
objects (and their content) for those uses. For example, in the
same case of asynchronous replication being 2000 operations behind,
use of the target for a disaster recovery failover might start from
a dataset that includes the volumes and other storage objects (and
their content) as they were 2000 operations ago at the source. In
this discussion, concurrent operations at the source (e.g., writes,
storage object creations or deletions, changes to properties that
affect inclusion or exclusion of volumes or other storage objects
or other data from a consistency group, or other operations that
were in progress and not signaled as completed at a same point in
time) might not have a single well-defined ordering, so the count
of operations only needs to represent some plausible ordering based
on any allowed ordering of concurrent operations on the source.
[0237] As another example using consistency groups, in the case of
periodic replication based on replication of consistency group
snapshots, each replicated consistency group snapshot would include
the volumes and other storage objects at the time each consistency
group snapshot was formed on the source. Ensuring that membership
in a consistency group is kept consistent by using common, or
shared, metadata, ensures that a fault--or other change which may
cause the source of replication, or the system that forms a dataset
snapshot, to switch from one storage system in a pod to
another--does not lose information needed for properly handling
those consistency group snapshots or the consistency group
replication. Further, this type of handling may allow for multiple
storage systems that are members of a pod to concurrently serve as
source systems for asynchronous or periodic replication.
[0238] Further, synchronized metadata describing mapping of
segments to storage objects is not limited to mappings themselves,
and may include additional information such as sequence numbers (or
some other value for identifying stored data), timestamps,
volume/snapshot relationships, checkpoint identities, trees or
graphs defining hierarchies, or directed graphs of mapping
relationships, among other storage system information.
[0239] For further explanation, FIG. 8 sets forth a diagram of a
computing environment supporting an implementation of asynchronous
replication of synchronously replicated data in accordance with
some embodiments of the present disclosure.
[0240] As discussed above with regard to FIGS. 4-7, a synchronously
replicated dataset is resilient to multiple types of failures in
part due to being replicated among more than a single storage
system. However, data resiliency may be further improved by
replicating a dataset that is being synchronously replicated among
storage systems to a storage system that is not part of the storage
systems that are synchronously replicating the dataset. In other
words, using the terminology introduced above, a dataset within a
pod may be asynchronously replicated to one or more storage systems
that are not members of the pod. However, in some examples, because
a pod is a logical construct that is independent from the physical
storage devices implementing the pod, a synchronously replicated
dataset within the pod may be asynchronously replicated to one or
more storage devices that also implement the pod.
[0241] In the example computing environment depicted in FIG. 8,
multiple storage systems (800A-800N) may synchronously replicate a
dataset (804) within a pod (802), where another storage system
(820) is connected over a network (801) to the multiple storage
systems (800A-800N), but where the other storage system (820) is
not a member of the multiple storage systems (800A-800N) that are
synchronously replicating the dataset (804). In this example, the
multiple storage systems (800A-800N) may be referred to as source
storage systems and the other storage system (820) may be referred
to as a target storage system.
[0242] Further, in this example, because the dataset (804) being
replicated is synchronously replicated among the multiple storage
systems (800A-800N), portions of the dataset (804) may be
asynchronously copied from more than one of the storage systems
(800A-800N) either serially, in parallel, or both serially and in
parallel. As depicted in FIG. 8, a given storage system among the
multiple storage systems may establish one or more connections to a
target storage system (820), where each connection may transmit a
respective portion of the dataset (804). In this example, a first
storage system (800A) may include source connections (806A-806P),
and an Nth storage system (800N) of the active cluster of storage
systems (800A-800N) may include source connections
(810A-810Q)--where source connections (806A-806P) transmit
respective portions of dataset (804), depicted as subsets
(808A-808P), and so on for each storage system until the Nth
storage system (800N), where source connections (810A-810Q)
transmit respective portions of dataset (804), depicted as subsets
(812A-812Q).
[0243] Correspondingly, in this example, the target storage system
(820) may include a respective target connection for each of the
connections on the multiple storage systems, where the target
connections (820A-820P, . . . , 824A-824Q) may respectively receive
a portion of the dataset (804), where the respective portions are
depicted as subsets (808A-808P, . . . , 812A-812Q). In this
example, a given connection may be a network layer connection, such
as a TCP connection; however, in other examples, and in general,
any type of link or connection that may reliably transport data
across a network may be used.
[0244] Further, the extent to which each storage system (800A-800N)
is responsible for replicating a portion of the dataset (804) may
be based on a variety of factors, including one or more of:
physical proximity, where closer storage systems may be assigned a
greater portion of the dataset (804) to replicate as compared to
other source storage systems; source storage system performance
characteristics, where storage systems with greater computational
or physical resources may be assigned a greater portion of the
dataset (804) to replicate as compared to other source storage
systems; source storage systems workloads or busyness, including
expected workloads or busyness, where source storage systems with
greater workload or busyness or greater expected workloads or
busyness may be assigned a smaller portion of the dataset (804) to
replicate as compared to other source storage systems; network
bandwidth availability, where source storage systems with greater
available bandwidth availability are assigned a greater portion of
the dataset (804) to replicate as compared to other source storage
systems; network latency characteristics, where source storage
systems with smaller network latencies are assigned a greater
portion of the dataset (804) to replicate as compared to other
source storage systems; including other factors that may affect
storage system performance in replicating one or more portions of
the dataset (804).
[0245] In other examples, a rule that specifies which portions of a
dataset are replicated by which storage systems may be defined by a
user. Generally, multiple rules may be triggered under different
circumstances, or under specific combinations of events. For
example, a rule may specify that one or more source storage systems
have their network bandwidth limited, or throttled, in accordance
with a rule specifying a particular balance of network loads among
the set of source storage systems. In some examples, a rule may
specify that a given replication process for a given dataset not
consume more than X % of network bandwidth on given storage
systems, where each storage systems may be limited according to a
respective percentage or quantity of network data over a period of
time. In other examples, a controller on the source storage system
may implement one or more rules for one or more other storage
systems based on telemetry data, or performance metrics, for one or
more of the source storage systems, where the telemetry data, or
performance metrics, indicate an expected future load or event that
may consume computing resources. For example, a given storage
system may have a scheduled workload activity, and to reduce the
impact of the schedule workload activity on a replication process,
and in dependence upon the scheduled workload activity or telemetry
data, a controller may reduce the replication load on the given
storage system and increase the replication load on one or more of
the remaining source storage systems.
[0246] In some examples, replication of the dataset (804) may
continue in the event of one or more storage systems (800A-800N)
falling out of synchronization. For example, if the dataset (804)
is a portion of the synchronized data being storage among the
multiple storage systems (800A-800N), and the dataset (804) is
synchronized while other data may not be synchronized, then the
replication of the dataset (804) may continue without the dataset
(804) becoming inconsistent. In the case of the dataset (804) being
a snapshot, if two storage systems are in-sync when a snapshot is
taken, and then the two storage systems fall out-of-sync, if the
target storage system is pulling from the most recent snapshot on
the two storage systems, then the target storage system may
continue replicating the snapshot if the most recent snapshot is
in-sync.
[0247] With reference to FIG. 8, in the example of the dataset
(804) being a snapshot, a member of the active cluster, such as
storage system (800A) among storage systems (800A-800N), may notify
the target storage system (820) that a snapshot has been taken,
where storage system (800A) may send the notification based on
being a leader. A leader is described in greater detail above with
reference to FIGS. 4-7, however, in other examples, the active
cluster, storage systems (800A-800N), may have no particular
leader, and one or more of the storage systems (800A-800N) may send
a notification to the target storage system (820) in response to
the dataset being ready for replication, such as a snapshot having
been successfully taken.
[0248] As depicted in FIG. 8, a dataset (804) may be split into
multiple subsets (808A-808P, . . . , 812A-812Q) transmitted from
multiple source connections (806A-806P, . . . , 810A-810Q) to
multiple target connections (820A-820P, . . . , 824A-824Q) via
multiple target connections (820A-820P, . . . , 824A-824Q) over a
network (801), where as discussed below, different mappings between
source connections and target sessions are possible.
[0249] Continuing with this example, in response to the target
storage system (820) receiving a notification that a dataset is
ready to be replicated from one or more of the storage systems
(800A-800N), the target storage system (820) may schedule multiple
replication sessions to fetch metadata and/or data corresponding to
the dataset (804) from one or more of the source storage systems
(800A-800N) providing a portion, or subset, of the dataset (804).
In this example, each session may correspond to a previously
established connection, such as a long-lived TCP connection, where
in some examples, multiple sessions may be multiplexed onto each
connection.
[0250] Further, in this example, the target storage system (820)
may create a work queue that includes a list of each portion, or
subset, of data that is to be fetched, or received, for the dataset
(804). While in this example, a queue is implemented, in other
examples, other data structures may be used or created, where the
data structure may include dataset information regarding the
dataset (804) that is created in response to receiving a
notification of the dataset (804) to be transferred, where the
dataset information may describe characteristics of the dataset,
such as address space(s), size, or other characteristics of the
dataset (804). In this example, a given session may pull, or
request, data in accordance with a portion, or subset, of data
described within a work queue entry.
[0251] In some examples, a given work queue entry may also identify
a source storage system (800A-800N) from which to pull, or request,
data, where the source storage system may be determined when the
queue entry is made, and where the determination of the source
storage system may be based on telemetry, or performance metrics or
characteristics, of the source storage systems (800A-800N). In
other examples, a work queue entry may simply identify a subset of
the dataset (804), where the source storage system (800A-800N) is
determined dynamically when the work queue entry is retrieved, and
where the dynamic determination of the source storage system may be
made based on telemetry data for one or more of the source storage
system, or performance metrics or characteristics, of the source
storage systems (800A-800N), or based on current network
conditions, or a combination of these factors.
[0252] Continuing with this example, a given session among the
multiple sessions, having accessed a work queue entry, may then
handle requesting, pulling, or fetching data corresponding to the
work queue entry and, in response to receiving the requested data,
write the data within the target storage system (840). In some
examples, each session may work on several items of work
corresponding to several different work queue entries, where each
session may work on more than one item of work simultaneously. In
some examples, a session may form a pipeline with several stages,
such as looking up metadata on the source, checking for duplicates
on the target, and fetching data from the source--where tasks
corresponding to each stage of the pipeline may be performed
independently of each other.
[0253] However, in some examples, the number of work items that a
given session may handle may be limited, such as by the number of
stages in a pipeline. In some examples, responsive to a session
claiming a maximum amount of work items on which the session may
work simultaneously, the session may be prevented from claiming
additional work items from the work queue--a constraint that when
applied to all sessions provides a flow control mechanism to the
multiple sessions. In this way, work items in the work queue may
self-balance among the multiple sessions, which results in
balancing network traffic among the one or more storage systems
replicating the dataset (804)--where in some cases, the multiple
sessions may be using different TCP connections, including
different physical network interfaces and/or different network
paths. For example, if a particular session is slow, the flow
control mechanism serves to limit the number of work items the
particular session may obtain from the work queue. Similarly,
faster sessions may claim and process a quantity of work items from
the work queue at a speed at which the faster sessions are
operating, thereby preventing the faster sessions from becoming
idle. Another benefit of the flow control mechanism described is
that if any given session stalls or fails, a monitoring process on
the target storage system may return the work item for the stalled
or failed session into the work queue, where a stall or fail may be
determined by a timeout event for the given session. In some
embodiments, an alternative way of considering balancing work items
among the multiple sessions is the use of a "shortest queue"
priority model, where work is given to whichever network path has
the least currently outstanding work. Continuing with this example,
if paths complete work at the same rate, then this results in an
even load across all paths; however, if some paths complete work
faster--for whatever reason, including less load on a given
controller, a shorter network link, a better network link,
temporary network congestion, among other reasons--then more work
is given to paths that complete work faster.
[0254] In some examples, the work queue may be populated by one or
more of the sessions. Further, in some examples, a work item may be
a small chunk of data or metadata, such as a 1 MB chunk, or the
work item may be a larger chunk of logical space. In some examples,
each work item may correspond to a node or extent within a BDAG
representation of a dataset, or snapshot, such as the BDAG
representation described above with reference to FIG. 7. In other
examples, in dependence upon a size of a chunk, portion, or subset,
of data to transfer, the session may either request or fetch and
write the complete quantity of data or make multiple requests or
fetches to retrieve the data in smaller parts. For example, if the
size of a chunk is small, a single request or fetch may be made to
retrieve the data for the chunk, however, if the size of the chunk
is large, then multiple requests or fetches may be made to retrieve
the data for the chunk--where small or large may be determined
according to a threshold value, such as a 1 MB, 16 MB, or some
other specified number of bytes.
[0255] In some implementations, a cost for handling different work
items from the work queue may be different, where cost may be
measured in terms of expected time to complete, storage space,
network bandwidth, processing cycles, or some other resource metric
such as time to fetch an extent or break down a node into smaller
nodes. In this example, a given session may estimate a cost of
completing a work item, and as work proceeds through the given
session's pipeline, the estimate may be updated, which results in
allowing a session to accept additional work items before
completing a current work item.
[0256] For further explanation, FIG. 9 sets forth a flow chart
illustrating steps that may be performed to asynchronously
replicate a synchronously replicated dataset according to some
embodiments of the present disclosure. Although depicted in less
detail, the storage systems (800A-800N, 820) depicted in FIG. 9 may
be similar to the storage systems described above with reference to
FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, and FIGS. 4-7, or any
combination thereof. In fact, the storage systems (800A-800N, 820)
depicted in FIG. 9 may include the same, fewer, additional
components as the storage systems described above.
[0257] In the example method depicted in FIG. 9, a target storage
system (820) may determine (902), at the target storage system
(820), multiple work items corresponding to a dataset (804) stored
among multiple source storage systems (800A-800N), where each
respective work item corresponds to a respective subset of the
dataset (804). Determining (902), at the target storage system
(820), the multiple work items corresponding to the dataset (804)
stored among the multiple source storage systems (800A-800N) may be
implemented by a controller of the target storage system (820)
receiving a notification from one or more of the source storage
systems (800A-800N) indicating that a dataset is ready for
replication. In this example, in response to receiving the
notification, the target storage system (820) may request
information describing the dataset (804) that may be used to
identify and transfer the dataset (804), where the information
describing the dataset (804) may be a metadata structure as
described above with reference to FIGS. 7 and 8. For example, as
described above with reference to FIG. 8, the metadata structure
may include nodes that correspond to blocks of the dataset, and
where the target storage system (820) may create work items for
each block to be transferred; however, in other examples, a work
item may correspond to other divisions or subsets of the dataset
(804).
[0258] The example method depicted in FIG. 9 also includes, for
(904) each session from among a plurality of sessions (822A-822P,
826A-826Q) operating on the target storage system (820):
determining (906) one or more computing environment factors (953)
affecting performance of the replication of data from one or more
of the multiple source storage systems (800A-800N) to the target
storage system (820); identifying (908), for a given session and
based on the one or more computing environment factors (953), a
respective source storage system and a quantity of work items; and
replicating (910), from the respective source storage system, one
or more subsets of data corresponding to the quantity of work
items.
[0259] Determining (906) the one or more computing environment
factors (953) affecting performance of the replication of data from
on or more of the multiple source storage systems (800A-800N) to
the target storage system (820) may be implemented by the target
storage system (820) receiving one or more of: performance metrics
for one or more of the source storage systems (800A-800N),
performance metrics for one or more other storage systems that are
configured similarly to the source storage systems (800A-800N),
telemetry data indicating current or expected workloads, or metrics
describing current or expected network traffic conditions between
one or more of the source storage systems (800A-800N) and the
target storage system (820). In some examples, the computing
environment factors (953) may be received after replication of a
dataset (804) has already begun, and in such a case, the target
storage system (820) may dynamically adjust, or rebalance,
replication workloads for each of the source storage systems in
dependence upon the computing environment factors (953) received or
determined after replication has begun. In other examples,
computing environment factors (953) may include quality-of-service
(QOS) metrics for workloads that are operating on a given storage
system, where the throughput may be limited or modulated in
accordance with QOS metrics, including allowing replication when
more cycles are available from a workload that is below a QOS
metric or guarantee, while replication is fully able to make use of
cycles that are available beyond the QOS metric or guarantee for
the storage system QOS-affected workloads.
[0260] Identifying (908), for a given session and based on or more
of the computing environment factors (953), a respective source
storage system and a quantity of work items (956) may be
implemented as described above with reference to FIG. 8, where a
session may remove one or more work items (956) from a work queue
(830) for replicating one or more subsets of the dataset (804), for
example, in dependence upon network performance for a corresponding
connection to a corresponding storage system, or in dependence upon
other ones of the factors (953) as discussed above with reference
to FIG. 8. Further, the work item may include identifying
information (954) for the one or more source storage systems from
which to replicate the one or more subsets of the dataset (804)
indicated within the one or more work items (956).
[0261] Replicating (910), from the respective source storage
system, the one or more of the subsets of data corresponding to the
quantity of work items (956) may be implemented as described above
with reference to FIG. 8. For example, the given session may
dequeue the quantity of work items (956), where each work item
identifies a portion, or subset, of the dataset (804), and the
session may use the information within the work item to initiate a
replication of that portion or subset of data (804) from one or
more source storage systems identified using the source storage
system identifying information (954), as described above with
reference to FIG. 8.
[0262] In this way, for each of the multiple session, and until
each work item is processed, a portion or subset of the dataset
(804) is replicated, and when the last work item is processed, and
the last portion or subset of the dataset (804) is replicated, the
entire dataset (804) will exist on the target storage system
(820).
[0263] Readers will appreciate that the methods described above may
be carried out by any combination of storage systems described
above. Furthermore, any of the storage systems described above may
also pair with storage that is offered by a cloud services provider
such as, for example, Amazon.TM. Web Services (`AWS`), Google.TM.
Cloud Platform, Microsoft.TM. Azure, or others. In such an example,
members of a particular pod may therefore include one of the
storage systems described above as well as a logical representation
of a storage system that consists of storage that is offered by a
cloud services provider. Likewise, the members of a particular pod
may consist exclusively of logical representations of storage
systems that consist of storage that is offered by a cloud services
provider. For example, a first member of a pod may be a logical
representation of a storage system that consists of storage in a
first AWS availability zone while a second member of the pod may be
a logical representation of a storage system that consists of
storage in a second AWS availability zone.
[0264] To facilitate the ability to synchronously replicate a
dataset (or other managed objects such as virtual machines) to
storage systems that consist of storage that is offered by a cloud
services provider, and perform all other functions described in the
present application, software modules that carry out various
storage system functions may be executed on processing resources
that are provided by a cloud services provider. Such software
modules may execute, for example, on one or more virtual machines
that are supported by the cloud services provider such as a block
device Amazon.TM. Machine Image (AMP) instance. Alternatively, such
software modules may alternatively execute in a bare metal
environment that is provided by a cloud services provider such as
an Amazon.TM. EC2 bare metal instance that has direct access to
hardware. In such an embodiment, the Amazon.TM. EC2 bare metal
instance may be paired with dense flash drives to effectively form
a storage system. In either implementation, the software modules
would ideally be collocated on cloud resources with other
traditional datacenter services such as, for example,
virtualization software and services offered by VMware.TM. such as
vSAN.TM.. Readers will appreciate that many other implementations
are possible and are within the scope of the present
disclosure.
[0265] Readers will appreciate that in situations where a dataset
or other managed object in a pod is retained in an on-promises
storage system and the pod is stretched to include a storage system
whose resources are offered by a cloud services provider, the
dataset or other managed object may be transferred to the storage
system whose resources are offered by a cloud services provider as
encrypted data. Such data may be encrypted by the on-promises
storage system, such that the data that is stored on resources
offered by a cloud services provider is encrypted, but without the
cloud services provider having the encryption key. In such a way,
data stored in the cloud may be more secure as the cloud has no
access to the encryption key. Similarly, network encryption could
be used when data is originally written to the on-premises storage
system, and encrypted data could be transferred to the cloud such
that the cloud continues to have no access to the encryption
key.
[0266] Through the use of storage systems that consist of storage
that is offered by a cloud services provider, disaster recovery may
be offered as a service. In such an example, datasets, workloads,
other managed objects, and so on may reside on an on-premises
storage system and may be synchronously replicated to a storage
system whose resources are offered by a cloud services provider. If
a disaster does occur to the on-premises storage system, the
storage system whose resources are offered by a cloud services
provider may take over processing of requests directed to the
dataset, assist in migrating the dataset to another storage system,
and so on. Likewise, the storage system whose resources are offered
by a cloud services provider may serve as an on-demand, secondary
storage system that may be used during periods of heavy utilization
or as otherwise needed. Readers will appreciate that user
interfaces or similar mechanisms may be designed that initiate many
of the functions described herein, such that enabling disaster
recovery as a service may be as simple as performing a single mouse
click.
[0267] Through the use of storage systems that consist of storage
that is offered by a cloud services provider, high availability may
also be offered as a service. In such an example, datasets,
workloads, other managed objects, that may reside on an on-premises
storage system may be synchronously replicated to a storage system
whose resources are offered by a cloud services provider. In such
an example, because of dedicated network connectivity to a cloud
such as AWS Direct Connect, sub-millisecond latency to AWS from
variety of locations can be achieved. Applications can therefore
run in a stretched cluster mode without massive expenditures
upfront and high availability may be achieved without the need for
multiple, distinctly located on-premises storage systems to be
purchased, maintained, and so on. Readers will appreciate that user
interfaces or similar mechanisms may be designed that initiate many
of the functions described herein, such that enabling applications
may be scaled into the cloud by performing a single mouse
click.
[0268] Through the use of storage systems that consist of storage
that is offered by a cloud services provider, system restores may
also be offered as a service. In such an example, point-in-time
copies of datasets, managed objects, and other entities that may
reside on an on-premises storage system may be synchronously
replicated to a storage system whose resources are offered by a
cloud services provider. In such an example, if the need arises to
restore a storage system back to a particular point-in-time, the
point-in-time copies of datasets and other managed objects that are
contained on the storage system whose resources are offered by a
cloud services provider may be used to restore a storage
system.
[0269] Through the use of storage systems that consist of resources
that are offered by a cloud services provider, data that is stored
on an on-premises storage system may be natively piped into the
cloud for use by various cloud services. In such an example, the
data that is in its native format as it was stored in the
on-premises storage system, may be cloned and converted into a
format that is usable for various cloud services. For example, data
that is in its native format as it was stored in the on-premises
storage system may be cloned and converted into a format that is
used by Amazon.TM. Redshift such that data analysis queries may be
performed against the data. Likewise, data that is in its native
format as it was stored in the on-premises storage system may be
cloned and converted into a format that is used by Amazon.TM.
DynamoDB, Amazon.TM. Aurora, or some other cloud database service.
Because such conversions occurs outside of the on-premises storage
system, resources within the on-premises storage system may be
preserved and retained for use in servicing I/O operations while
cloud resources that can be spun-up as needed will be used to
perform the data conversion, which may be particularly valuable in
embodiments where the on-premises storage system operates as the
primary servicer of I/O operations and the storage systems that
consist of resources that are offered by a cloud services provider
operates as more of a backup storage system. In fact, because
managed objects may be synchronized across storage systems, in
embodiments where an on-premises storage system was initially
responsible for carrying out the steps required in an extract,
transform, load (`ETL`) pipeline, the components of such a pipeline
may be exported to a cloud and run in a cloud environment. Through
the use of such techniques, analytics as a service may also be
offered, including using point-in-time copies of the dataset (i.e.,
snapshots) as inputs to analytics services.
[0270] Readers will appreciate that applications can run on any of
the storage systems described above, and in some embodiments, such
applications can run on a primary controller, a secondary
controller, or even on both controllers at the same time. Examples
of such applications can include applications doing background
batched database scans, applications that are doing statistical
analysis of run-time data, and so on.
[0271] 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.
[0272] 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.
[0273] 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.
[0274] 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.
[0275] 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.
[0276] 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.
[0277] 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.
[0278] 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.
[0279] 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.
[0280] Readers will appreciate that the steps described herein may
be carried out in a variety of ways and that no particular ordering
is required. It will be further understood from the foregoing
description that modifications and changes may be made in various
embodiments of the present disclosure without departing from its
true spirit. The descriptions in this specification are for
purposes of illustration only and are not to be construed in a
limiting sense. The scope of the present disclosure is limited only
by the language of the following claims.
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