U.S. patent application number 15/966021 was filed with the patent office on 2018-12-13 for accessible fast durable storage integrated into a bulk storage device.
The applicant listed for this patent is Pure Storage, Inc.. Invention is credited to JOHN COLGROVE, RONALD KARR, PETER KIRKPATRICK, CONSTANTINE SAPUNTZAKIS.
Application Number | 20180357017 15/966021 |
Document ID | / |
Family ID | 64562159 |
Filed Date | 2018-12-13 |
United States Patent
Application |
20180357017 |
Kind Code |
A1 |
KARR; RONALD ; et
al. |
December 13, 2018 |
ACCESSIBLE FAST DURABLE STORAGE INTEGRATED INTO A BULK STORAGE
DEVICE
Abstract
Performing data storage operations on a storage element
integrating fast durable storage and bulk durable storage,
including: receiving, at the storage element integrating fast
durable storage and bulk durable storage, a data storage operation
from a host computer; determining, in dependence upon the data
storage operation, a selection of fast durable storage and bulk
durable storage for performing the data storage operation; and
performing, using the determined selection of fast durable storage
and bulk durable storage, the data storage operation.
Inventors: |
KARR; RONALD; (PALO ALTO,
CA) ; SAPUNTZAKIS; CONSTANTINE; (MOUNTAIN VIEW,
CA) ; KIRKPATRICK; PETER; (MOUNTAIN VIEW, CA)
; COLGROVE; JOHN; (LOS ALTOS, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Pure Storage, Inc. |
Mountain View |
CA |
US |
|
|
Family ID: |
64562159 |
Appl. No.: |
15/966021 |
Filed: |
April 30, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15697540 |
Sep 7, 2017 |
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15966021 |
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15697566 |
Sep 7, 2017 |
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15697540 |
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15697521 |
Sep 7, 2017 |
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15697566 |
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62518551 |
Jun 12, 2017 |
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62589524 |
Nov 21, 2017 |
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62631933 |
Feb 18, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 11/1076 20130101;
G06F 11/2005 20130101; G06F 3/0647 20130101; G06F 2201/84 20130101;
G06F 11/1441 20130101; G06F 11/3034 20130101; G06F 3/0617 20130101;
G06F 3/067 20130101; G06F 11/2071 20130101; G06F 11/2089 20130101;
G06F 3/061 20130101; G06F 3/0616 20130101; G06F 11/1435 20130101;
G06F 3/0685 20130101; G06F 11/0793 20130101; G06F 3/0659 20130101;
G06F 11/0727 20130101; G06F 2212/261 20130101; G06F 11/1662
20130101; G06F 11/2007 20130101; G06F 11/2094 20130101; G06F
11/3055 20130101 |
International
Class: |
G06F 3/06 20060101
G06F003/06; G06F 11/14 20060101 G06F011/14 |
Claims
1. A method of performing data storage operations on a storage
element integrating fast durable storage and bulk durable storage,
the method comprising: receiving, at the storage element
integrating fast durable storage and bulk durable storage, a data
storage operation from a host computer; determining, in dependence
upon the data storage operation, a selection of fast durable
storage and bulk durable storage for performing the data storage
operation; and performing, using the determined selection of fast
durable storage and bulk durable storage, the data storage
operation.
2. The method of claim 1, wherein the selection of fast durable
storage and bulk durable storage includes: (a) both fast durable
storage and bulk durable storage; (b) fast durable storage, but not
bulk durable storage; or (c) bulk durable storage, but not fast
durable storage.
3. The method of claim 1, wherein the selection is specified by one
or more indications within the data storage operation.
4. The method of claim 1, wherein the selection is determined by
the storage element and not specified by the data storage
operation.
5. The method of claim 1, wherein the data storage operation is a
write operation, wherein performing the write operation comprises
storing data in fast bulk storage in a RAID N+2 format data stripe,
and wherein the RAID N+2 format data stripe is stored in bulk
durable storage in a RAID M+2 format data stripe.
6. The method of claim 5, wherein RAID parity calculations are a
merger of parity calculations for multiple partial erasure coded
parity calculations within a Galois field for partially filled data
stripe updates.
7. A storage element comprising: fast durable storage; bulk durable
storage; a stored energy device; and one or more controllers
configured to: receive a data storage operation from a host
computer; determine, in dependence upon the data storage operation,
a selection of the fast durable storage and the bulk durable
storage for performing the data storage operation; and perform,
using the selection of the fast durable storage and the bulk
durable storage, the data storage operation.
8. The method of claim 7, wherein the fast durable storage
comprises nonvolatile random access memory.
9. The method of claim 7, wherein the bulk durable storage
comprises one or more solid state drives.
10. The method of claim 7, wherein the storage element further
comprises volatile random access memory, and wherein the one or
more controllers are further configured to, in response to a loss
of power to the storage element and in dependence upon energy
provided by the stored energy device, transfer data from the
volatile random access memory into one or more of the fast durable
storage or the bulk durable storage.
11. The method of claim 7, wherein the selection of fast durable
storage and bulk durable storage includes: (a) both fast durable
storage and bulk durable storage; (b) fast durable storage, but not
bulk durable storage; or (c) bulk durable storage, but not fast
durable storage.
12. The method of claim 7, wherein the selection is specified by
one or more indications within the data storage operation.
13. The method of claim 7, wherein the selection is determined by
the storage element and not specified by the data storage
operation.
14. The method of claim 7, wherein the data storage operation is a
write operation, wherein performing the write operation comprises
storing data in fast bulk storage in a RAID N+2 format data stripe,
and wherein the RAID N+2 format data stripe is stored in bulk
durable storage in a RAID M+2 format data stripe.
15. An apparatus for integrating fast durable storage and bulk
durable storage, the apparatus comprising a computer processor, a
computer memory operatively coupled to the computer processor, the
computer memory having disposed within it computer program
instructions that, when executed by the computer processor, cause
the apparatus to carry out the steps of: receiving a data storage
operation from a host computer; determining, in dependence upon the
data storage operation, a selection of fast durable storage and
bulk durable storage for performing the data storage operation; and
performing, using the determined selection of fast durable storage
and bulk durable storage, the data storage operation.
16. The apparatus of claim 15, wherein the fast durable storage
comprises nonvolatile random access memory.
17. The apparatus of claim 15, wherein the bulk durable storage
comprises one or more solid state drives.
18. The apparatus of claim 15, wherein the storage element further
comprises volatile random access memory, and wherein the one or
more controllers are further configured to, in response to a loss
of power to the storage element and in dependence upon energy
provided by the stored energy device, transfer data from the
volatile random access memory into one or more of the fast durable
storage or the bulk durable storage.
19. The apparatus of claim 15, wherein the selection of fast
durable storage and bulk durable storage includes: (a) both fast
durable storage and bulk durable storage; (b) fast durable storage,
but not bulk durable storage; or (c) bulk durable storage, but not
fast durable storage.
20. The apparatus of claim 15, wherein the selection is specified
by one or more indications within the data storage operation.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation application of U.S.
Patent Applications: Ser. Nos. 15/697,540, 15/697,566, and
15/697,521, filed Sep. 7, 2017, and claims benefit of U.S.
Provisional Patent Applications: 62/518,551, filed Jun. 12, 2017,
62/589,524, filed Nov. 21, 2017, and 62/631,933, filed Feb. 18,
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. 3C sets forth a diagram of a storage system in
accordance with some embodiments of the present disclosure.
[0016] FIG. 4 sets forth a flow chart illustrating an example
method for using accessible fast durable storage integrated into a
bulk storage device according to some embodiments of the present
disclosure.
DESCRIPTION OF EMBODIMENTS
[0017] Example methods, apparatus, and products for using
accessible fast durable storage integrated into a bulk storage
device 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.
[0018] 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 (`IAN`) 160.
[0019] 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.
[0020] The LAN 160 may also be implemented with a variety of
fabrics, devices, and protocols. For example, the fabrics for LAN
160 may include Ethernet (802.3), wireless (802.11), or the like.
Data communication protocols for use in LAN 160 may include
Transmission Control Protocol (`TCP`), User Datagram Protocol
(`UDP`), Internet Protocol (`IP`), HyperText Transfer Protocol
(`HTTP`), Wireless Access Protocol (`WAP`), Handheld Device
Transport Protocol (`HDTP`), Session Initiation Protocol (`SIP`),
Real Time Protocol (`RTP`), or the like.
[0021] Storage arrays 102A-B may provide persistent data storage
for the computing devices 164A-B. Storage array 102A may be
contained in a chassis (not shown), and storage array 102B may be
contained in another chassis (not shown), in implementations.
Storage array 102A and 102B may include one or more storage array
controllers 110A-D (also referred to as "controller" herein). A
storage array controller 110A-D may be embodied as a module of
automated computing machinery comprising computer hardware,
computer software, or a combination of computer hardware and
software. In some implementations, the storage array controllers
110A-D may be configured to carry out various storage tasks.
Storage tasks may include writing data received from the computing
devices 164A-B to storage array 102A-B, erasing data from storage
array 102A-B, retrieving data from storage array 102A-B and
providing data to computing devices 164A-B, monitoring and
reporting of disk utilization and performance, performing
redundancy operations, such as Redundant Array of Independent
Drives (`RAID`) or RAID-like data redundancy operations,
compressing data, encrypting data, and so forth.
[0022] Storage array controller 110A-D may be implemented in a
variety of ways, including as a Field Programmable Gate Array
(`FPGA`), a Programmable Logic Chip (`PLC`), an Application
Specific Integrated Circuit (`ASIC`), System-on-Chip (`SOC`), or
any computing device that includes discrete components such as a
processing device, central processing unit, computer memory, or
various adapters. Storage array controller 110A-D may include, for
example, a data communications adapter configured to support
communications via the SAN 158 or LAN 160. In some implementations,
storage array controller 110A-D may be independently coupled to the
LAN 160. In implementations, storage array controller 110A-D may
include an I/O controller or the like that couples the storage
array controller 110A-D for data communications, through a midplane
(not shown), to a persistent storage resource 170A-B (also referred
to as a "storage resource" herein). The persistent storage resource
170A-B main include any number of storage drives 171A-F (also
referred to as "storage devices" herein) and any number of
non-volatile Random Access Memory (`NVRAM`) devices (not
shown).
[0023] In some implementations, the NVRAM devices of a persistent
storage resource 170A-B may be configured to receive, from the
storage array controller 110A-D, data to be stored in the storage
drives 171A-F. In some examples, the data may originate from
computing devices 164A-B. In some examples, writing data to the
NVRAM device may be carried out more quickly than directly writing
data to the storage drive 171A-F. In implementations, the storage
array controller 110A-D may be configured to utilize the NVRAM
devices as a quickly accessible buffer for data destined to be
written to the storage drives 171A-F. Latency for write requests
using NVRAM devices as a buffer may be improved relative to a
system in which a storage array controller 110A-D writes data
directly to the storage drives 171A-F. In some implementations, the
NVRAM devices may be implemented with computer memory in the form
of high bandwidth, low latency RAM. The NVRAM device is referred to
as "non-volatile" because the NVRAM device may receive or include a
unique power source that maintains the state of the RAM after main
power loss to the NVRAM device. Such a power source may be a
battery, one or more capacitors, or the like. In response to a
power loss, the NVRAM device may be configured to write the
contents of the RAM to a persistent storage, such as the storage
drives 171A-F.
[0024] 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`).
[0025] In some implementations, the storage array controllers
110A-D may be configured for offloading device management
responsibilities from storage drive 171A-F in storage array 102A-B.
For example, storage array controllers 110A-D may manage control
information that may describe the state of one or more memory
blocks in the storage drives 171A-F. The control information may
indicate, for example, that a particular memory block has failed
and should no longer be written to, that a particular memory block
contains boot code for a storage array controller 110A-D, the
number of program-erase (`P/E`) cycles that have been performed on
a particular memory block, the age of data stored in a particular
memory block, the type of data that is stored in a particular
memory block, and so forth. In some implementations, the control
information may be stored with an associated memory block as
metadata. In other implementations, the control information for the
storage drives 171A-F may be stored in one or more particular
memory blocks of the storage drives 171A-F that are selected by the
storage array controller 110A-D. The selected memory blocks may be
tagged with an identifier indicating that the selected memory block
contains control information. The identifier may be utilized by the
storage array controllers 110A-D in conjunction with storage drives
171A-F to quickly identify the memory blocks that contain control
information. For example, the storage controllers 110A-D may issue
a command to locate memory blocks that contain control information.
It may be noted that control information may be so large that parts
of the control information may be stored in multiple locations,
that the control information may be stored in multiple locations
for purposes of redundancy, for example, or that the control
information may otherwise be distributed across multiple memory
blocks in the storage drive 171A-F.
[0026] In implementations, storage array controllers 110A-D may
offload device management responsibilities from storage drives
171A-F of storage array 102A-B by retrieving, from the storage
drives 171A-F, control information describing the state of one or
more memory blocks in the storage drives 171A-F. Retrieving the
control information from the storage drives 171A-F may be carried
out, for example, by the storage array controller 110A-D querying
the storage drives 171A-F for the location of control information
for a particular storage drive 171A-F. The storage drives 171A-F
may be configured to execute instructions that enable the storage
drive 171A-F to identify the location of the control information.
The instructions may be executed by a controller (not shown)
associated with or otherwise located on the storage drive 171A-F
and may cause the storage drive 171A-F to scan a portion of each
memory block to identify the memory blocks that store control
information for the storage drives 171A-F. The storage drives
171A-F may respond by sending a response message to the storage
array controller 110A-D that includes the location of control
information for the storage drive 171A-F. Responsive to receiving
the response message, storage array controllers 110A-D may issue a
request to read data stored at the address associated with the
location of control information for the storage drives 171A-F.
[0027] In other implementations, the storage array controllers
110A-D may further offload device management responsibilities from
storage drives 171A-F by performing, in response to receiving the
control information, a storage drive management operation. A
storage drive management operation may include, for example, an
operation that is typically performed by the storage drive 171A-F
(e.g., the controller (not shown) associated with a particular
storage drive 171A-F). A storage drive management operation may
include, for example, ensuring that data is not written to failed
memory blocks within the storage drive 171A-F, ensuring that data
is written to memory blocks within the storage drive 171A-F in such
a way that adequate wear leveling is achieved, and so forth.
[0028] In implementations, storage array 102A-B may implement two
or more storage array controllers 110A-D. For example, storage
array 102A may include storage array controllers 110A and storage
array controllers 110B. At a given instance, a single storage array
controller (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 (e.g., storage array controller 110A) may be designated
with secondary status (also referred to as "secondary controller"
herein). The primary controller may have particular rights, such as
permission to alter data in persistent storage resource 170A-B
(e.g., writing data to persistent storage resource 170A-B). At
least some of the rights of the primary controller may supersede
the rights of the secondary controller. For instance, the secondary
controller may not have permission to alter data in persistent
storage resource 170A-B when the primary controller has the right.
The status of storage array controllers 110A-D may change. For
example, storage array controller 110A may be designated with
secondary status, and storage array controller 110B may be
designated with primary status.
[0029] 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.
[0030] In implementations, storage array controllers 110A-D are
communicatively coupled, via a midplane (not shown), to one or more
storage drives 171A-F and to one or more NVRAM devices (not shown)
that are included as part of a storage array 102A-B. The storage
array controllers 110A-D may be coupled to the midplane via one or
more data communication links and the midplane may be coupled to
the storage drives 171A-F and the NVRAM devices via one or more
data communications links. The data communications links described
herein are collectively illustrated by data communications links
108A-D and may include a Peripheral Component Interconnect Express
(`PCIe`) bus, for example.
[0031] 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
110A-D described with respect to FIG. 1A. In one example, storage
array controller 101 may be similar to storage array controller
110A or storage array controller 110B. Storage array controller 101
includes numerous elements for purposes of illustration rather than
limitation. It may be noted that storage array controller 101 may
include the same, more, or fewer elements configured in the same or
different manner in other implementations. It may be noted that
elements of FIG. 1A may be included below to help illustrate
features of storage array controller 101.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] 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.
[0038] 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.
[0039] 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.
[0040] 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.
[0041] 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.
[0042] 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.
[0043] 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.
[0044] 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.
[0045] 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 device controller 119a-d. In one embodiment, storage device
controller 119a-d may be a CPU, ASIC, FPGA, or any other circuitry
that may implement control structures necessary according to the
present disclosure. In one embodiment, system 117 includes flash
memory devices (e.g., including flash memory devices 120a-n),
operatively coupled to various channels of the storage device
controller 119a-d. Flash memory devices 120a-n, may be presented to
the controller 119a-d as an addressable collection of Flash pages,
erase blocks, and/or control elements sufficient to allow the
storage device controller 119a-d to program and retrieve various
aspects of the Flash. In one embodiment, storage device controller
119a-d may perform operations on flash memory devices 120a-n
including storing and retrieving data content of pages, arranging
and erasing any blocks, tracking statistics related to the use and
reuse of Flash memory pages, erase blocks, and cells, tracking and
predicting error codes and faults within the Flash memory,
controlling voltage levels associated with programming and
retrieving contents of Flash cells, etc.
[0046] In one embodiment, system 117 may include RAM 121 to store
separately addressable fast-write data. In one embodiment, RAM 121
may be one or more separate discrete devices. In another
embodiment, RAM 121 may be integrated into storage device
controller 119a-d or multiple storage device controllers. The RAM
121 may be utilized for other purposes as well, such as temporary
program memory for a processing device (e.g., a CPU) in the storage
device controller 119a-d.
[0047] In one embodiment, system 117 may include a stored energy
device 122, such as a rechargeable battery or a capacitor. Stored
energy device 122 may store energy sufficient to power the storage
device controller 119a-d, some amount of the RAM (e.g., RAM 121),
and some amount of Flash memory (e.g., Flash memory 120a-120n) for
sufficient time to write the contents of RAM to Flash memory. In
one embodiment, storage device controller 119a-d may write the
contents of RAM to Flash Memory if the storage device controller
detects loss of external power.
[0048] In one embodiment, system 117 includes two data
communications links 123a, 123b. In one embodiment, data
communications links 123a, 123b may be PCI interfaces. In another
embodiment, data communications links 123a, 123b may be based on
other communications standards (e.g., HyperTransport, InfiniBand,
etc.). Data communications links 123a, 123b may be based on
non-volatile memory express (`NVMe`) or NVMe over fabrics (`NVMf`)
specifications that allow external connection to the storage device
controller 119a-d from other components in the storage system 117.
It should be noted that data communications links may be
interchangeably referred to herein as PCI buses for
convenience.
[0049] System 117 may also include an external power source (not
shown), which may be provided over one or both data communications
links 123a, 123b, or which may be provided separately. An
alternative embodiment includes a separate Flash memory (not shown)
dedicated for use in storing the content of RAM 121. The storage
device controller 119a-d may present a logical device over a PCI
bus which may include an addressable fast-write logical device, or
a distinct part of the logical address space of the Dual PCI
storage device 118, which may be presented as PCI memory or as
persistent storage. In one embodiment, operations to store into the
device are directed into the RAM 121. On power failure, the storage
device controller 119a-d may write stored content associated with
the addressable fast-write logical storage to Flash memory (e.g.,
Flash memory 120a-n) for long-term persistent storage.
[0050] 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 Dual PCI 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.
[0051] In one embodiment, the stored energy device 122 may be
sufficient to ensure completion of in-progress operations to the
Flash memory devices 120a-120n stored energy device 122 may power
storage device controller 119a-d and associated Flash memory
devices (e.g., 120a-n) for those operations, as well as for the
storing of fast-write RAM to Flash memory. Stored energy device 122
may be used to store accumulated statistics and other parameters
kept and tracked by the Flash memory devices 120a-n and/or the
storage device controller 119a-d. 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.
[0052] 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.
[0053] 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 storage device controllers 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.
[0054] 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
device controllers 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.
[0055] 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-d 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.
[0056] 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.
[0057] A storage device controller 119a-d may include mechanisms
for implementing high availability primitives for use by other
parts of a storage system external to the Dual PCI storage device
118. For example, reservation or exclusion primitives may be
provided so that, in a storage system with two storage controllers
providing a highly available storage service, one storage
controller may prevent the other storage controller from accessing
or continuing to access the storage device. This could be used, for
example, in cases where one controller detects that the other
controller is not functioning properly or where the interconnect
between the two storage controllers may itself not be functioning
properly.
[0058] 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.
[0059] 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.
[0060] 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.
[0061] 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.
[0062] 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.
[0063] 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.
[0064] 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.
[0065] 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.
[0066] 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.
[0067] 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.
[0068] FIG. 2B is a block diagram showing a communications
interconnect 173 and power distribution bus 172 coupling multiple
storage nodes 150. Referring back to FIG. 2A, the communications
interconnect 173 can be included in or implemented with the switch
fabric 146 in some embodiments. Where multiple storage clusters 161
occupy a rack, the communications interconnect 173 can be included
in or implemented with a top of rack switch, in some embodiments.
As illustrated in FIG. 2B, storage cluster 161 is enclosed within a
single chassis 138. External port 176 is coupled to storage nodes
150 through communications interconnect 173, while external port
174 is coupled directly to a storage node. External power port 178
is coupled to power distribution bus 172. Storage nodes 150 may
include varying amounts and differing capacities of non-volatile
solid state storage 152 as described with reference to FIG. 2A. In
addition, one or more storage nodes 150 may be a compute only
storage node as illustrated in FIG. 2B. Authorities 168 are
implemented on the non-volatile solid state storages 152, for
example as lists or other data structures stored in memory. In some
embodiments the authorities are stored within the non-volatile
solid state storage 152 and supported by software executing on a
controller or other processor of the non-volatile solid state
storage 152. In a further embodiment, authorities 168 are
implemented on the storage nodes 150, for example as lists or other
data structures stored in the memory 154 and supported by software
executing on the CPU 156 of the storage node 150. Authorities 168
control how and where data is stored in the non-volatile solid
state storages 152 in some embodiments. This control assists in
determining which type of erasure coding scheme is applied to the
data, and which storage nodes 150 have which portions of the data.
Each authority 168 may be assigned to a non-volatile solid state
storage 152. Each authority may control a range of inode numbers,
segment numbers, or other data identifiers which are assigned to
data by a file system, by the storage nodes 150, or by the
non-volatile solid state storage 152, in various embodiments.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] 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.
[0073] 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.
[0074] 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. Modes 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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.
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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).
[0085] 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.
[0086] 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.
[0087] 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.
[0088] 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.
[0089] 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.
[0090] 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.
[0091] 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.
[0092] 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.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] 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.
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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.
[0105] 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.
[0106] 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.
[0107] 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.
[0108] 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.
[0109] 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 (`NVMe`) 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.
[0110] 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.
[0111] 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.
[0112] 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.
[0113] 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.
[0114] 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.
[0115] 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.
[0116] 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 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.
[0117] 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.
[0118] 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 sort 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.
[0119] 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.
[0120] 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.
[0121] 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.
[0122] 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
RAID-6) 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.
[0123] 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.
[0124] 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.
[0125] 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.
[0126] 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.
[0127] 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.
[0128] 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."
[0129] 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.
[0130] 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.
[0131] 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.
[0132] 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.
[0133] 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.
[0134] 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.
[0135] As used within the herein embodiments, some example features
for storage system concepts are presented for storage systems,
storage system elements, different types of durable storage,
storage controllers, storage devices, storage device controllers,
and combinations of these features. Further, additional examples of
a memory component with multiple types of durable storage, multiple
ways to address data, and multiple ways to implement durably stored
data are described in application Ser. No. 15/697,540, which is
incorporated herein in its entirety.
[0136] In some examples, a storage system may be considered to
include a combination of hardware and software that implements
capabilities for storing and retrieving data on behalf of servers,
applications, databases, or any other software and/or hardware
module configured to communicate with the storage system. A storage
system may include data storage features and performance management
capabilities. Further, a storage system may implement mechanisms
that increase reliability by supporting continued operation and a
reduced probability of data loss in the event of a variety of
hardware or software component failures.
[0137] In some examples, a storage system element may be an
identifiable part, component, or module within a storage system,
where the part, component, or module may be implemented as a
circuit board, power supply, fan, interconnect, or subcomponents
thereof. In some cases, the storage system element may be
implemented as a software module or application.
[0138] In some examples, durable storage may be a type of storage
that is designed to retain stored content in the event of software
crashes or faults, storage system reboots, storage system power
loss, or failures of nearby, or connected, storage system elements,
or some other type of fault.
[0139] In some examples, a storage controller may be a part of a
storage system that implements, or at least coordinates, advertised
capabilities of a storage system. A storage system may include one
or more storage controllers to improve reliability, to improve
performance, or to improve both reliability or performance.
[0140] In some examples, a storage device may be an element of a
storage system that comprises physical durable storage to be
presented to storage systems users, clients, or to other parts of
the storage system, in addition to any other hardware, software,
firmware, or combination of hardware, software, or firmware in
order to present usable storage.
[0141] In some examples, a storage device controller may be part of
a storage device that controls the storage device. A storage device
controller may be implemented as a CPU, an ASIC, an FPGA, or
software module that implements capabilities for managing durable
storage and interaction capabilities of the storage device.
[0142] In some examples, an integrated controller and storage
component may implement functions of a storage device controller
and a storage device, as described above--where the integrated
controller and storage component may be combined into a unified
storage element, such as a removable circuit board, that implements
both the storage device controller capabilities for interacting
with the storage controller and the durable storage capabilities on
other integrated controller and storage components. Further, the
storage device capabilities of the integrated controller and
storage component may be presented such that they may be used from
the storage device controllers on multiple integrated controller
and storage components. In some cases, CPUs and various controller
chips may serve multiple, divided, or combined purposes across the
two basic functions of implementing or coordinating the storage
system capabilities versus managing the physical hardware elements
to make stored data durable.
[0143] In some examples, a single storage device may include
multiple addressable storage classes. In some implementations,
storage devices may refer to generic SSDs, which generally emulate
disk drives and usually provide a simple address range of blocks
that is internally virtualized to map onto erase blocks
dynamically, such as using a Flash Translation Layer. In other
cases--particularly in the case of fast durable storage, mapped
durable storage, or durable registers--storage devices are presumed
to include one or more of the following components: addressable
fast durable storage, addressable bulk durable solid state storage,
and a storage device controller.
[0144] In some examples, addressable fast durable storage may be
storage with high bandwidth and low latency that supports a high
number and rate of overwrites. Fast durable storage may be
addressed with PCI transactions, for example NVMe, with direct
memory addressing by a CPU on a separate computer system, for
example, a separate storage controller, or with some other
communication channel or protocol. Addressable fast durable storage
may be a logical construct that makes use of hardware that serves
multiple functions. Fast durable storage may be implemented in
multiple ways, including: persistent high-speed memory, for
example, 3D Xpoint; volatile RAM coupled to a storage device
controller and a battery, capacitor, or generally an energy source
with sufficient energy to transfer data stored in RAM to a
different nonvolatile storage--such as reserved bulk solid state
storage--in cases of loss of external power or loss of a primary
source of power. In other words, to be durable, transferred data
from RAM into the other nonvolatile storage is identifiable and
retrievable at some time subsequent to loss of power to RAM, or
during recovery from some other type of fault. In some cases, other
types of fast durable storage memory types may be used, such as low
capacity enterprise single-level cell (SLC) Flash memory, where the
fast durable storage is designed for high bandwidth, high
overwrites, higher lifespans--which may result in the fast durable
storage having a higher price, or lower density per bit than other
types of solid state durable storage that may be used for long term
storage.
[0145] In some examples, addressable bulk durable solid state
storage may be designed for lower cost and higher density, where
the lower cost and higher density may be in exchange for higher
write or access latency and reduced lifespan in the face of high
overwrite rates. One example is flash memory, and in particular,
multi-level cell (MLC), triple-level cell, or quad-level cell (QLC)
flash memory that stores two, three, or four bits per flash memory
cell at the expense of reduced bit-level reliability, increased
write latency, more disturbance of nearby read operations, or
reduced lifespans in the face of flash erase block reuse. An
encompassing storage device may use various techniques to optimize
performance or reliability of this type of bulk storage, including
internal use of fast storage as a frontend to respond more quickly
or to allow multiple operations to be organized more effectively or
to implement internal atomic operations related to bulk memory
operations.
[0146] In some examples, a storage device controller may be
configured to perform one or more of: receiving processing requests
to store or retrieve data from addresses associated with fast
durable storage on the storage device; receiving and processing
requests to store or retrieve data from addresses associated with
bulk durable storage on a storage device; receiving and processing
requests to transfer data from fast durable storage on a storage
device to bulk durable storage on the storage device; or in
response to a power failure, transfer content from volatile memory
that is part of an implementation of fast durable storage to bulk
durable storage using stored energy from a battery, capacitor, or
some other energy storage device. Further, a storage device
controller may use CPUs associated with general storage device
controller functions or may use dedicated or secondary function
low-power CPUs, or this may be a feature built into an FPGA or
ASIC.
[0147] A storage device may further support one or more of the
following features, with corresponding implementations to: (a) map
a region of fast durable storage as I/O memory or virtual memory to
one or more CPU cores or I/O memory controllers on a storage
controller, such that CPU store operations or DMA transfers to I/O
memory or virtual memory can be persisted in case of power failure,
where this I/O memory or virtual memory can be written to directly
from a storage controller's CPU instructions with few operations
system scheduling delays; (b) receive requests to store an integer
value (e.g., an 8 byte, 16 byte, or 32 byte value) into one of an
index of memory locations, where the memory locations may be
referred to as a type of register, where the registers are durable
in case of power failure, where durability may be implemented by
writing into a dedicated region of durable fast storage; these
received requests may be piggybacked with other requests, such as
to be applied before or after or at the point in time the other
request is processed to the point of being durable; (c) operate
using the NVMe Storage Performance Development Kit API, or some
alternative API that supports high-speed storage operations without
the need for completion interrupts; such an API may exclusively
support fast durable storage, or may be supported for both fast and
bulk durable storage on the storage device--or the storage device
may internally utilize some of the fast storage at its front-end,
where such operations may be addressed to addressable bulk storage;
(d) receive requests to transfer contents of fast durable storage
or bulk solid state storage to the fast durable storage or bulk
solid state storage within the storage device, or in some cases, to
the fast or bulk storage of another storage device within a storage
system that included multiple storage devices; (e) provide multiple
interconnect interfaces, such as based on dual NVMe, SCSI, or
Ethernet interfaces, to improve reliability in cases of internal
storage system interconnect faults, or to improve performance by
providing multiple pathways within the storage system, or allowing
dedicated pathways to each of, for example, one or more storage
controllers; and (f) provide directly addressable pages and erase
blocks to storage controllers for a set of flash memory chips that
include the storage device's bulk durable storage, thus allowing
the storage controllers to handle wear leveling, failed erase block
management, and other aspects of managing flash memory life cycles
that are otherwise often handled by a controller chip paired with
flash memory chips on storage devices themselves.
[0148] In some implementations, a storage system may be built from
a collection of storage devices, including some number of storage
controllers that implement or coordinate the features and logic for
the storage system itself. An example of such a storage system is
depicted within the storage system architecture of FIG. 1D.
Further, storage controller logic may implement logic to create a
reliable storage service that may survive failures such as faults
of storage devices, individual elements of storage devices,
individual storage controllers, storage controller, or storage
device logic, power supplies, or internal networks--all while
continuing to operate and provide storage system services. Such
storage system services may include implementing a SCSI storage
array, an NFS or SMB based file server, an object server, a
database service, a service for implementing extensible storage
related applications, or combinations of such services. Storage
operations may include creating snapshots, replication, online
addition or removal of storage devices or trays of storage devices
or storage controllers or power supplies or networking interfaces,
or administrative operations such as creating, modifying, or
removing logical elements such as volumes, snapshots, replication
relationships, file systems, object stores, application or client
system relationships, among other operations.
[0149] In some implementations, storage devices may store and
retrieve data on behalf of--or coordinated through--storage
controllers, largely at the direction of storage controllers, and
are generally otherwise relatively passive participants in
contributing to the overall implementation of a storage service.
However, in some examples, storage device may be additionally
configured to assist, or offload, storage controller functions in
order for the storage controller to provide more efficient
services, such as by a storage device partially implementing
garbage collection, data scanning and scrubbing, or other such
services, or aiding in bootstrapping the storage controllers.
[0150] For further explanation, FIG. 3C sets forth a diagram
illustrating an example integrated storage controller and device
elements that integrates accessible fast bulk storage into a bulk
storage device, referred to here as a unified storage element
(320), where a storage system (124) may be implemented using
multiple unified storage elements (320). Although depicted in less
detail, the unified storage element (320) depicted in FIG. 3C may
be similar to the storage systems described above with reference to
FIGS. 1A-1D and FIGS. 2A-2G, as the unified storage element (320)
may include some or all of the components described above.
[0151] In the example architecture depicted in FIG. 3C, multiple
storage controllers (125) and multiple storage devices are
integrated into a unified storage element (320), where the unified
storage element (320) may be removable as a whole or that may be on
a single circuit board, or that may be controller by a common CPU
or controlled by multiple CPUs, FPGAs, or ASICs--where each such
unified storage element (320) within a storage system serves as
both storage controller and storage device functions. In such a
storage system, the storage controller function of a unified
storage element (320) may access the storage device function on a
plurality of unified storage elements, configured similarly to
unified storage element (320), within the storage system. In this
example, the storage controller function might be able to access
the storage device function on other unified storage elements
within the storage system, such as through internal networks within
the storage system, or the storage controller function on a first
unified storage element might have to go through the storage
controller function on a second unified storage element to get the
unified storage element storage device function.
[0152] In some examples, a reference to a storage device may refer
to either a separate storage device within a storage system or to
the storage device function within a unified storage element (320)
within a storage system. Further, in other examples, a reference to
a storage controller may refer to either a separate storage
controller within a storage system or to the storage controller
function within a unified storage element (320) within a storage
system.
[0153] In another implementation, a storage system may include
various combinations of elements comprising dedicated storage
controllers without integrated storage device capabilities, storage
devices without integrated storage controller capabilities, or
combined storage controller and device elements. Such combinations
may be useful for migrating a storage system from one generation
that operates in one way to another generation that operates in a
different way, or aspects of scale may dictate some extra numbers
of one function versus another functions--for example, a bulk
archive storage device including a large number of extended storage
devices in a system whose core is built from combined storage
controller and device elements, or a performance oriented device
that needs performance or external interconnects might benefit from
additional storage controllers, but without benefiting from
additional durable capacity.
[0154] In some examples, a storage system may be configured to be
reliable against complete failure of one or two or more storage
devices using erasure codes, such as erasure codes based on single
or double parity, as with RAID-5 or RAID-6, or against
uncorrectable faults, corruptions, or complete failures of
individual elements within a storage device--such as individual
pages or erase blocks in flash memory or of individual chips--by
reconstructing content from elsewhere within the same storage
device or through the single and double parity protection used for
complete device failure. In other words, in some examples, the
various memory components of a unified storage element (320),
individually or in combination, may be used to implement multiple,
different RAID levels or combinations of RAID levels. In the
following examples, a RAID stripe is data that is stored among a
set of memory regions mapped across a set of storage devices, where
each memory region on a given storage device stores a portion of
the RAID stripe and may be referred to as a "strip," a "stripe
element," or a "shard." Given that the storage system (306) may
simultaneously implement various combinations of RAID levels, a
"RAID stripe" may refer to all the data that is stored within a
given RAID stripe corresponding to a given RAID level. Generally,
in the following examples, a stripe, stripe element, or shard is
one or more consecutive blocks of memory on a single solid state
drive--in other words, an individual stripe, stripe element, or
shard is a portion of a RAID stripe distributed onto a single
storage device among a set of storage devices. In this way, a RAID
level may depend on how the RAID stripe is distributed among a set
of storage devices. With regard to erasure coding, generally,
erasure coding may be described in terms of N+R schemes, where for
every N units of content data, an additional R units of redundancy
data is written such that up to R failures may be tolerated while
being able to recover all N units of content data. For example,
RAID-5 is an example N+1 scheme, whereas RAID-6 describes a set of
N+2 schemes. In other examples, a scheme may be based on Galois
Fields, or other types of mathematics that can cover a wide range
of N and R combinations. In some examples, RAID parity calculations
are a merger of parity calculations for multiple partial erasure
coded parity calculations within a Galois field for partially
filled data stripe updates. Specifically, in some cases, each given
storage device within a storage system may, by default, detect
faults locally and directly using, as some examples, a checksum or
mathematical integrity check for all data that is read or written
to the given storage device. However, in cases where a given
storage device in a storage system does not perform local fault
detection, other techniques may be applied, such as various coding
techniques that store additional shards beyond a fault tolerance
(e.g., three parity shards that are usable to recover up to two
faults and up to one unit of additional corrupted data), or through
the use of encoding all data, including parity or redundancy data,
in a manner that can be used to detect corrupted data. In general,
in the embodiments described herein, a local storage device will,
by default, perform localized data integrity checks, where data
shards and redundancy shards may be distinct from each other.
However, in some examples, no such restriction to, or reliance
upon, localized integrity checking is presumed.
[0155] It should further be noted that erasure coding schemes may
be used within a storage system in a variety of ways. Traditional
storage systems, which were originally designed for spinning disk
storage devices, typically allocate large RAID-5 and RAID-6 layouts
relatively statically as a set of relatively large, such as
multi-gigabyte, N+R shards, where each shard is a unit of stored
content data or a unit of stored redundancy data. In such schemes,
overwrites of previously stored content includes replacing existing
content data shards and calculating and replacing existing
redundancy data shards using some sequence of steps to ensure that
the operations may be done safely in the event that a fault occurs
during the sequence of steps. In some cases, these sequence of
steps may be computationally intensive, for example, in some cases,
the sequence of steps includes reading old content and redundancy
data, or reading from most existing content data shards at the same
logical offset in a RAID layout in order to calculate new
redundancy data. In other examples, the sequence of steps may
include multiple persisting steps in order to ensure that an
interrupted operation can be recovered when safe operation
resumes.
[0156] In examples using spinning disk storage, faults may include
corrupted blocks, which can generally be corrected by overwriting
them to correct a temporary coding error on the spinning disk, or
by allowing the spinning disk to revector a bad block to an
alternate location. Spinning disks may also suffer from electrical
or mechanical failure from problems driving their electric motor,
seizing of the electric motor, or failure of a read/write head or
actuator. In some cases, sudden and excessive vibration can cause a
read/write head to crash, or physically make contact, with a
spinning disk surface, thereby damaging the head or scratching the
disk surface, or scattering particles within a hermetically sealed
device. In other cases, electronics can fail or the hermetic seal
may be damaged, thereby exposing the internal components to an
uncontrolled environment with moisture or dust particles that may
lead to disk failure. In short, while spinning disks may generally
be reliable, there are a great many ways in which a spinning disk
may encounter a catastrophic failure; however, there are exceptions
where failures may be fixed with internal recoveries and rewrites
for the occasional bad sector or bad track, or runs of a few bad
sectors or bad tracks.
[0157] By contrast to spinning disks, solid state storage does not
suffer from mechanical failures, and performance is not affected by
vibration, and it is unlikely that a solid state storage device
causes voltage spikes or other electrical issues. Further a solid
state storage device is not generally a single device, but rather a
collection of individual integrated circuits, or a collection of
regions within a particular integrated circuit that may age and
fail separately, such as due to a flash cell's limited number of
erase/recycle cycles or due to chip-to-chip or
cell-block-to-cell-block manufacturing variations. However,
performance of some solid state storage device technologies, in
particular flash based storage devices, can be greatly affected by
internal operations related to erase block writing, erasing, or
other management operations or--particularly for TLC and QLC flash
devices--reads performed on nearby cells. Individual elements
within solid state devices that do their own garbage collection can
become slow or unresponsive for periods of time when garbage
collection is being performed. Flash, in particular, can have much
higher rates of individual damaged blocks, or of unusable blocks,
than disk, and may require more internal error correction and more
spare blocks. Given the way flash memory works, advertising a much
lower available capacity than the raw physical capacity may be used
to extend the lifespan of flash drives by reducing the frequency of
erase cycles that result from random overwrites and delaying
garbage collection. Such lifespan extension techniques may be
coupled with algorithms to randomize the locations of written data,
perhaps with biases against or in favor of blocks that have been
erased more times than other blocks in dependence upon an expected
longevity of the stored data.
[0158] Generally, flash does not suffer from slowdowns due to
randomization of data locations because given that there is no
mechanical delay in reading a block, any read unaffected by a
concurrent write or a concurrent garbage collection is generally
quite fast irrespective of whether previous, subsequent, or
concurrent reads are from nearby or farther away locations or
logical addresses. In some cases, a penalty for turning relatively
sequential reads into relative random reads is so high for disk,
that disk based storage systems generally avoid randomizing
locations of data so that logical sequential reads by applications
stay sequential on disk, which is part of why it is so common for
RAID-5 and RAID-6 mappings to be kept relatively static, with the
mapping retained when data is overwritten (some files system based
models such as ZFS are exceptions to this). In short, in the case
of spinning disks, small overwrites to RAID-5 and RAID-6 datasets
often require a read-old-data, read-old-parity, write-new-data,
write-new-parity sequence that can be quite expensive. Simpler
mappings used for disk-based storage systems also reduce the amount
of index data that has to be kept and managed in order to determine
where a block is stored. In particular, if a random read requires
reading both an index block--to figure out where a block is
stored--and the block itself, then that includes two random reads,
and two mechanical delays, which is even worse from a performance
perspective because latency may double and throughput may be
reduced due to limited available IOPS.
[0159] Further, because solid state storage does not have
mechanical delays inherent in spinning disks, an occasional or even
frequent extra read to figure out where a block resides does not
incur a high performance penalty, consequently, scattering data as
it is written (resulting in a large index that can exceed available
cache memory that results in the need to read the index in order to
locate data) has few downsides. For example, performance impact for
a small read performed before a large read is performed will be
negligible. Further, bandwidth for servicing a large sequential
read request by issuing a set of smaller physical random reads (the
result of scattering content on writes and overwrites) is generally
only a bit less than the bandwidth for servicing large sequential
read requests by issuing a small number of larger physical reads.
By contrast to solid state storage, with spinning disks, IOPS
limits can result in throughput hits from randomized data locations
that can be multiple orders of magnitude if stored logically
sequential data is physically highly fragmented.
[0160] Consequently, because of the different characteristics of
spinning disks and solid state storage, solid state storage
solutions generally avoid overwriting stored data by writing to new
locations and onto fresh erasure coded stripes as data is
persisted. Data may be written into N dynamically allocated shards
of a few kilobytes to a few megabytes as content data is written,
with R matching redundancy shards allocated and written to match,
thereby achieving N+R recoverability. Such a protocol for
processing writes avoids replacing existing data in place, thus
avoiding the need to modify in-place redundancy data in place
through some safe update scheme. Collections of stripes may then be
written out based on whatever patterns achieve the best write
throughput, where throughput of later read requests depends on the
high IOPS rates of solid state storage that is often limited by
available throughput of I/O interconnects instead of limited by
delays in switching between sectors--at least as long as the
storage devices are properly optimized and include a sufficient
number of flash chips.
[0161] In some implementations of a unified storage element (320),
erasure codes may be used to ensure that any completely written
data can survive one or more device failures, or a combination of a
device failure and a localized data corruption or segment failure.
Further, reliability may be increased further by adding erasure
codes within a storage device, such as by calculating erasure codes
that operate across pages, erase blocks, or chips within an
individual storage device. With this further reliability, rare
cases of an isolated, corrupted or unreadable, block, erase block,
or chip within a storage device may be recovered internally without
relying on additional storage devices. This further reliability is
helpful in cases where undetected, and thus uncorrected, isolated
errors exist for an extended period of time, consequently exposing
the storage system to data loss in cases where two other storage
devices fail entirely or are removed from the storage system due to
accident or as part of a procedure to replace older drives within
newer ones, and the latent isolated corrupted blocks cannot then be
corrected by an N+R redundancy technique. In dependence upon
uncorrected errors within a device being rare or having a low
probability of occurring, redundancy data across pages, erase
blocks, or chips may be written in either a lazy fashion or as data
ages. In this example, these lazy writes may result in some lost
intra-device redundancy in cases of power failure, but there may be
a very low likelihood that this small amount of lost intra-device
redundancy may be needed to recover on restart from a rare
localized uncorrectable error coupled with a simultaneous failure
for R devices to startup. Bandwidth for writing data in such a
storage system may often be improved by writing several stripes at
once. Writing several stripes at one may be done by keeping
multiple stripes open for further updates to allow use of all
storage devices under heavy loads and to allow activity to be
balanced across large numbers of erase blocks (in the case of
flash) or large numbers of solid state storage chips. Keeping
multiple stripes open to maximize parallelism across erase blocks,
chips, or storage devices may depend upon custom storage devices
and networks or buses that support large numbers of parallel
transfers. Bandwidth for reading data generally depends upon
support for a large number of parallel transfers between storage
controllers and storage devices. Getting the best read throughput
may also need to account for, or schedule around, particular flows
of writes to storage devices, storage device and internal network
channels, solid state chips, or flash erase blocks in order to
reduce contention with write-related operations. Reads that may be
slowed down by write contention, or that may severely slow down
write operations themselves (as is common in flash devices) might
be delayed, might delay write operations, or might look for
alternate means of reading data, such as recovering data from
erasure codes stored within or across storage devices.
[0162] In some cases, stored data may be stale or no longer useful
while continuing to occupy storage space, such as, for example,
data that has been written and scattered, and overwrites that are
separately scattered from the data they overwrite, and deletion of
data through deleting snapshots or volumes or through UNMAP or TRIM
requests, or through SCSI Extended Copy or similar types of
requests. Generally, some garbage collection process may identify
pages or segments or erase blocks that are no longer used, or that
are no longer used completely, and move whatever useful data
remains in a segment to another memory location, and then allowing
pages or segments or erase blocks to be used for new stored data.
In the case of flash memory, erase blocks may be garbage collected
and erased before they can be reused for new data.
[0163] In some implementations, further optimizations may be based
on a storage model, such as staging speculative writes through fast
memory of a unified storage element (320). For example, in some
storage systems, there may be a write into some kind of fast
storage to record an operation, or record operation results, in a
way that makes the operation or the results persistent more
quickly--thus enabling one or more of the following benefits,
including: (a) enabling operations to be signaled as completed
faster; (b) enabling internal storage system processes to be
unblocked more quickly while allowing a larger queue of changes to
be built up for writing to slower bulk storage; or to reduce the
likelihood of temporary data, which may be overwritten quickly,
from being written to bulk storage in the first place, thereby
improving flash lifespans; or (c) enabling multiple operations to
be merged together for more efficient or better organized storage
operations, thereby improving flash lifespan as well as potentially
increasing throughput. Further, if individual storage devices in a
storage system provide both fast durable storage (such as 3D Xpoint
memory or DRAM backed by a battery or capacitor which is written to
flash on power failure) and bulk storage (e.g., bulk MLC, TLC, or
higher bit-per-cell Flash), then the typical means of staging
through fast storage may be improved. In a typical storage system
implementation, a storage system writes to fast storage, for
example, by writing at least two copies into at least two separate
fast memories each on separate storage devices, and, sometime
later, the storage system writes that data to the bulk storage.
Such a write operation implementation may result in two sets of
transfers from storage controllers to various storage devices, one
set of transfers to fast storage, another set of transfers to bulk
storage. These two sets of transfers use considerable extra
bandwidth between storage controllers and storage devices because
the same data is transferred at least twice, and possibly more than
twice. For example, staging a content data write by writing three
copies to fast storage on three storage devices followed by
grouping of data together into an N+2 erasure coded stripe may
result in total bandwidth that is over four times the size of the
write itself. This consumption of bandwidth may be reduced by using
an M+2 encoding for storing into the fast durable staging
space--where M is a separate number of content data shards, which
are then coupled with redundancy data shards for writing to fast
staging space; however, there may still be bandwidth usage that is
over two times the size of the write itself
[0164] One example for reducing this bandwidth overhead is for the
storage device to use the fast storage as internal staging space,
acknowledging the write more quickly to storage controllers, and
then writing to bulk storage at a later point in time--where the
later write to bulk storage from fast storage may be invisible to
the storage controllers because to the storage controller, the
write was persisted, or durably stored at the moment that the write
was acknowledge. In some cases, such a write protocol--where a
storage controller is unaware of internal transfers of data--may
eliminate the need for separately addressable fast durable storage.
However, there is significant flexibility to be gained by allowing
the storage controllers to manage the process of transferring data
between fast storage and bulk storage, rather than having the
storage devices do these transfers implicitly behind the scenes and
hiding the fast durable storage from the storage controllers. In
other words, storage system implementations gain a lot of
flexibility for optimizing their overall operations by allowing
higher level aspects of the implementation to record to fast
persistent storage early in a processing pipeline, and losing that
flexibility to gain reduced backend bandwidth may be an
unacceptable tradeoff.
[0165] In some embodiments, a solution to reduce bandwidth for
backend data transfers is to utilize staging a write through fast
storage. In this example, if the write is first written as three
copies to fast storage on the respective fast storage of three
separate storage devices, where the contents of that multi-copy
write are aggregated with the contents of other writes to form an
N+2 protected stripe in bulk storage, then if one of the storage
devices selected serving as fast storage is the same storage device
to be selected for the eventual write to bulk storage, then the
extra storage-controller-to-storage-device bandwidth for transfer
to the bulk storage may be avoided by the storage controller
instructing the storage device to transfer that data from fast
storage to bulk storage. In some cases, avoiding extra transfers
may be achieved by transformations such as merging. In this
example, the other two storage devices that stored a respective
copy in fast storage retain their respective copy until the final
N+2 stripe has been written and committed, but otherwise outside of
a fault and recovery sequence, the other two storage devices do not
need to perform additional, corresponding, write or copies of the
respective copies. In some examples, the format of data written to
fast storage is identical to the format of data written to bulk
storage, and consequently, no data transformation is needed prior
to a transfer. In other cases, where the format of data written to
fast storage is not identical to the format of data written to bulk
storage, the transfer may include transforming the content during
the transfer from fast storage to bulk storage, where the
transformation may be based on instructions from a storage
controller, and possibly in coordination with merged content from
other stored writes in fast durable storage.
[0166] In some examples, transfers to fast storage from bulk
storage or from fast storage to bulk storage may operate
simultaneously, where such parallelism may increase bandwidth or
reduce locking contention issues within a storage controller
software implementation. For example, separate CPU cores in a
storage controller may use separate channels to storage devices,
thereby eliminating locking contention while gaining bandwidth for
every storage controller CPU core that transfers to storage
devices.
[0167] Further, separation of transfers into fast storage from the
transfer of content from fast storage to bulk storage allows
further types of optimizations. For example, data may be written as
M+R.sub.1 stripes in fast bulk storage to be stored in an N+R.sub.2
stripe in bulk storage, or data may partially or completely fill a
subset of shards within an N+R stripe resulting in intermediate M+R
stripes where M<N. In some example, R, R.sub.1, and R.sub.2 may
be equal, but in other examples, may be defined to be distinct
values. Further, as an example, within a 10+2 stripe, there may be
enough accumulated data to write partial segments as a 5+2 stripe,
thereby allowing fast write data to be written with reliability
against two failures, but with only 40% additional overhead rather
than 200% additional overhead that would be required for writing 3
complete copies. In this example, after all of the N+2 shards are
complete, the corrected P and Q parity (given a RAID-6 erasure code
format) may be written to storage devices, and the storage devices
with fast durable storage that includes the completed data shards
may be instructed to write the completed data shards from fast
storage to bulk storage.
[0168] In this example, the overhead of a write of three
independent copies to fast storage followed by an optimized N+R
transfer to bulk storage may incur bandwidth consumption such that
slightly more than three times as much data to be transferred from
storage controllers to storage devices--compared with four times as
much without the instruction to transfer from fast storage to bulk
storage within a storage device. However, the previous example may
reduce the overage transfers in between fast storage and bulk
storage to 160% of the total data--and in examples with wider
stripes, the overhead could be reduced further still.
[0169] In some examples, multiple M+R partial stripes may be
written concurrently into fast storage, including for the same N+R
stripes or for different stripes. In this example, each M+R stripe
may be protected by its own partial or complete parity shards. The
partial or complete parity shards should be identifiable in the
written data, such as by recording the M+2 layout in the content
written out to each section of fast storage, or by recording a
sequence number or other identifier that can be compared across
storage devices during some later recovery.
[0170] In some embodiments, a variation may be to interleave small
writes across partial sections of most or all shards of an N+R
stripe, calculating, say, P and Q one section at a time. When the
complete contents for an N+R stripe are available, a storage
controller may instruct the storage devices to transfer now
complete shards to respective bulk storage. In some cases, the
format of the partial stripes may include headers or other
information that differs slightly from the format that will be
written to bulk storage; if so, the storage device may be
instructed to transform the format and to calculate updated
redundancy shards.
[0171] Further, if data is accumulated in fast storage for some
period of time before being transferred to bulk storage, then the
storage controllers may determine that some of the content is no
longer needed, or will not be requested, and in response this
determination, the storage controllers may partially or completely
avoid a transfer to bulk storage. Avoiding such transfers may occur
based on the determination that data has been overwritten, or if
the data is metadata that has been reorganized or optimized.
Avoiding such transfer may also happen in cases where deduplication
operations include determining that some data has already been
written to the bulk storage somewhere in the storage system,
thereby avoiding the transfer of data that already exists. In other
examples, partially replaced data, such as in the case of M+R
stripes or partially written shards in an N+R stripe might become
invalid or unnecessary before being transferred to bulk storage,
and as a result, parts of the final N+R stripe may be replaced,
eliminated, or reorganized prior to any such transfer.
[0172] In different embodiments, because fast storage space may be
limited, the storage system implementation ensures that data that
is to ultimately be written to bulk storage is actually written to
bulk storage and made reliable across storage devices in a
sufficiently timely manner that the storage system does not
normally have to pause waiting for fast storage to be freed up for
reuse.
[0173] In some implementations, consistent recovery may be
performed with varying subsets of storage devices. For example, if
a storage controller fails and reboots, or one controller fails and
another controller takes over for the failed controller, or when
some number of storage devices go offline and then come back
online, or when a storage system is stopped through some sequence
of events (power failure, power surge, an inadvertent
administrative shutdown command, or a bug, among other examples),
the storage service may have to be restarted and recovered from the
available storage devices. During that recovery, there may be a
second event that results in the storage service going offline and
then later being recovered, and that sequence of interrupted
recoveries may repeat itself many times. During each recovery, a
different set of storage devices may come online, or some storage
devices may come online much slower than other storage devices,
resulting in the storage service deciding to recover from the
online storage devices, but with a different set of such storage
devices each time. Such a sequence of interrupted recovering may
present an issue for data that is stored redundantly across storage
devices, such as through writing the same data to multiple such
storage devices (RAID-1 or mirroring) or through writing an erasure
code (such as RAID-5 or RAID-6) across multiple storage devices
intended to achieve reliability in the face of one or more storage
device failures.
[0174] As one example of handling interrupted recoveries, consider
storage devices utilized in a storage system that is
two-drive-failure redundant, such as one that writes N+2 segments
of data as RAID-6 style stripes formed as some number, say N, units
of data (e.g., shards) and two similarly sized shards storing P and
Q parity. Consider further that segments are written out as new
data flows into a storage system, with no fixed correspondence of
the virtual address of stored data to the physical location of that
data, rather than being partially rewritten in place to fixed
locations as has been typical of traditional RAID storage
systems.
[0175] In this example, the content of a storage system may be
considered to be committed and recoverable content may be
determined after a restart that only had access to the persisted
content. However, there may be a problem: the recoverable content
depends on the specific set of storage devices that are available
and ready at the time recovery starts. If N or N+1 drives out of
N+2 drives were written for a particular N+2 stripe prior to the
fault that preceded a recovery, then recoverability depends on
which drives are available when recovery starts. If N of the drives
that were written are available on recovery, then the content of
that RAID stripe is recoverable. If N-1 of the drives that were
written are available for recovery, then at least some data from
that stripe is not recoverable, yet one or two drives that were not
written for some stripes might be available during startup, so
there are combinations of one or two drive failures that can lose
some partially written data and other combinations of one or two
drive failures that do not lose the same partially written
data.
[0176] Continuing with this example, one solution for this problem
is to operate at startup, based on whatever data is recoverable,
and to ignore any data that is not recoverable. Under normal
operation, an incomplete write of all shards of an N+2 stripe
should only happen if some in-progress writes did not complete
prior to a storage system or storage controller fault, or if a
storage device fails resulting in a persistent partial failure. In
this way, a storage device failure may be handled by writing the
failed shards to new locations prior to considering the write
complete, or by somehow persistently marking the device as having
failed so that it will not be considered as a viable source for
up-to-date content on a storage system or storage controller
startup.
[0177] To conclude with this example, as soon as a write of a
stripe is complete (or as soon as any data is N+2 redundant through
one or more techniques), the storage system logic may consider the
write to be durable and recoverable and therefore the storage
system may move on to some new set of writes that depend on that
previous write being guaranteed recoverable. For example, a first
write might store data that represents the establishment of a
snapshot of a volume, and a second write might represent new data
written to that volume after the snapshot. The second write is
written within the context of structures established by the first
write.
[0178] In some embodiments, as an alternative, the snapshot and the
write may be concurrent such that the write is included in the
snapshot (thus being associated with one version of the durable
structures for the volume and its snapshot), or the write could be
excluded from the snapshot (thus being associated with another
version of the durable structures for the volume and its
snapshots), or either the snapshot or the write or both might be
backed out as never having happened, which are all consistent and
acceptable outcomes if neither the snapshot request nor the write
were completed and signaled to a requester as completed.
[0179] Continuing with this example, while the snapshot and the
write are being processed, the storage system or a controller might
fault a first time, and the snapshot--at the time of the first
fault--may have been written to N out of N+2 devices, while the
write might have been written to M out of M+2 devices. In this
example, during recovery, the storage system may take action to
clean up or move forward data structures so that the storage system
is ready to process new writes. Further, if one or two devices are
not available during recovery, then either the snapshot, or the
write, or both, or neither might be recoverable without the two
unavailable devices. Subsequent first recovery actions may depend
on which devices are not available, which may involve writing down
new data that is protected by some alternate L+2 data protection
scheme on some other set of devices to finalize a clean structure
for the volume and its chain of snapshots and of the data that does
or does not fall into one or another of the snapshots. In this
example, if the writing of the L+2 redundant data is written to
only L devices before a second storage system or storage controller
fault, then subsequent second recovery of the first recover actions
may also depend on which storage devices are available during the
second recovery. However, a different two devices may be
unavailable during this second recovery, thereby resulting in a
different answer for whether the snapshot or the write are
recoverable and some independent answer for whether the first
recovery action is recoverable. Such a scenario may cause a problem
if the first recovery actions determine one answer for whether a
snapshot or write are recoverable but the second recovery
determines a different answer for whether the snapshot or the write
is recoverable. Note that determining that written information is
recoverable can be calculated from the available stored data,
whereas unrecoverable information may simply be unknown to recovery
so that recovery may not explicitly determine that it is
unrecoverable. As an example, for a 2+2 RAID-6 stripe, say that
only two data shards were written prior to a fault that resulted in
a later recovery, then, if only those two shards are available
during recovery then the data is recoverable. Otherwise, in this
example, if only the P and Q shards are available during recovery,
then there is no available knowledge of the stripe at all.
[0180] In this example, there may exist the following scenario: the
first recovery determines that both the snapshot and the write into
the volume are recoverable, but the recovery action must then
determine whether the write should be included in the snapshot or
excluded from the snapshot. The first recovery actions might then
include writing a record that the snapshot exists and has a name
and might include metadata changes that include the write content
in the snapshot, with a reference to the write content added to a
table.
[0181] Continuing with this scenario, if during the second
recovery, the write of the L+2 data from the first recovery actions
is recovered, as is the snapshot, but the write included in the
snapshot is not itself recovered, then there may be metadata
associated with the snapshot that includes data which is not itself
recoverable (and may be entirely unknown given the set of devices
available). Such a result may be inconsistent and may corrupt
metadata, depending on how the storage system implementations
handles this scenario.
[0182] Further, during a second recovery, actions may be taken to
complete recovered actions by writing further data and metadata
based on what was recovered, and a fault might occur during that
recovery, resulting in a third recovery based on perhaps a
different set of available and unavailable devices.
[0183] While it may be that the likelihood of some of these
scenarios is low because such scenarios may depend on particular
combinations of faults during narrow time windows during the
writing of redundant data after just enough data has been written
for the data to be recoverable with one or two devices unavailable
during recovery--without more data having been written so the data
is guaranteed to be recoverable no matter which two devices are
subsequently unavailable. In other words, such scenarios may
require a sequence of faults and recoveries where each fault
happens during one of these narrow time windows, and they require
that a different set of one or two devices be unavailable during
subsequent recoveries. However, while these scenarios may only
occur infrequently, or rarely, these scenarios are possible, and
implementations should have strategies to mitigate them.
[0184] In some implementations, strategies to prevent such
scenarios may include using two-phase commit, where data is written
out as a set of pending changes, and once all changes are written,
a much smaller commit is written out. However, the commit may have
the same problem. In other words, if a storage system is expected
to recover with two failed storage devices, then at least three
copies of the commit must be written, or it must itself be written
using an N+2 redundancy scheme. As an example, if one or two of
three copies are written, then one recovery sequence may be aware
of the commit, while a second recovery sequence may fail to be
aware the commit, or vice versa. In this example, if the existence
of the commit itself is used as a basis for subsequent steps in
recovery, and if a second recovery depends for correctness on
seeing the same commit (or lack thereof) to ensure that anything
written in subsequent steps is handled correctly, then the same
issue applies where inconsistency between a first recovery and a
second recovery can lead to corruption.
[0185] Further, the examples herein may implement any N+R scheme,
where R is a number of shards representing redundancy data, where
any N valid, written, and available shards (whether data or
redundancy shards) may be used to recover content. Further, if at
least N shards, but fewer than N+R shards, are written prior to a
fault, then if at least N of the written shards are available
during recovery, then the associated data is recoverable. However,
if fewer than N of the written shards are available during
recovery, then some of the content may not be recoverable. Further
in this example, if there is a second fault during a first recovery
that leads to a second recovery, or an eventual third fault leading
to a third recovery, and so on, then a different set of available
devices may, as described above, alter the set of written stripes
for which a sufficient number of shards are available.
[0186] In some implementations of a unified storage element (320),
recovery may be based on recording available storage device lists.
Such available storage device lists may provide a solution for
determining a consistent set of recoverable data across multiple
reboots, where the solution may include terminating efforts for a
second recovery that detects failed storage devices if that second
recovery follows a first recovery with an incompatible, or
different, set of detected failed storage devices. Such a recovery
protocol may operate in one of two ways: during recovery, before
making any other changes that could lead to inconsistencies in
subsequent recoveries, an available storage device list is
generated that indicates a set of devices that are included in the
given recovery sequence. The available storage device list may be
written to all available storage devices. Until the available
storage device list is written to all available storage devices,
and until the writes are successful to a sufficient number of those
storage devices that a subsequent detection on recovery is
guaranteed, then further recovery is prevented from proceeding.
[0187] However, this solution may present a problem: how can a list
of available devices be recorded in a way that is reliably
recoverable on a second, third, or fourth recovery each with an
inconsistent set of available devices? While maintaining such a
list may be difficult, this is simpler information than a set of
all erasure coded stripes. One example solution, in a storage
system that is redundant against R failures, includes writing the
list of available devices to at least 2.times.R+1 storage devices
before proceeding further with recovery. In this example, with an R
of two (2), a subsequent recovery that is missing a different set
of two storage devices will see at least three of those lists of
available storage devices. Alternately, if the writing of the list
of available storage devices had been written to R storage devices
or less, a subsequent recovery might not see the list of available
storage devices, or might see only one copy of the list. If a
subsequent recovery does not see the list of available storage
devices at all, then the previous recovery could not have advanced
to making any incompatible changes, and a new list of available
storage devices can be expressed to a different set of 2.times.R+1
storage devices. If a subsequent recovery does not see at least R+1
copies of a list of available storage devices, a prior recovery
could not have advanced to the point of making changes and the
current list of available devices could be written out.
Alternately, if a subsequent recovery sees any copy of the prior
list of available storage devices, it could use that list of
available storage devices.
[0188] Continuing with this solution, regardless of the manner in
which the list of available storage devices is made reliable, once
a fault during a first recovery has led to analysis for a second
recovery, if it is determined that the available storage devices
during analysis for the second recovery does not sufficiently
overlap with the available storage devices during a first recovery
that might have proceeded past the point of writing the list of
available storage devices, then recovery is stopped. If a
sufficient number of those storage devices do come back online,
then the second recovery can proceed after that point, but not
before. In other words, in this example, the union of the list of
available storage devices from a first recovery and the list of
available storage devices from a second recovery cannot be a set
larger than R devices.
[0189] In some implementations of a unified storage element (320),
recovery may be based on identities of allowed stripes. In this
example, a solution may be more tolerant of different sets of
storage devices being available on subsequent recovery from prior
interrupted recoveries. For example, a set of allowed commit
identities may be defined to be associated with data that is
allowed in the storage system, and a set of disallowed commit
identities may be defined to be associated with data that is not
allowed in the storage system for one or more reasons.
[0190] In this example solution, there may be several types of
data, including one or more of: erasure coded stripes, each of
which is associated with at least one commit identity, recent
commits of commit identities, a set of allowed commit identities, a
set of disallowed commit identities, or a set of future commit
identities.
[0191] Continuing with this example, when writing data into an
erasure coded stripe, until that data is written completely, such
that it is guaranteed to be recovered by any sufficiently large set
of storage devices available during a recovery, a storage
controller may not consider the data committed. Such a protocol
that delays data as being committed may include waiting until all
shards of, say, an N+R stripe are fully written before anything in
that N+R stripe can commit. In this case, there may be only one
commit identity associated with the stripe, where the commit
identity may be stored somewhere in the stripe that should be
available during recovery. If parts of a stripe can be made durable
and recoverable without making the entire stripe durable and
recoverable, as suggested in previous sections, such as by writing
sub-regions of shards with matching dual parity, or by mirroring
writes to fast memory or by writing partial M+R shards of an N+R
stripe (or any of the other techniques described previously), then
commit identities may be associated with those partial elements of
a stripe, but then the rest of this argument applies to that
partial stripe which must still be completely persisted before
proceeding to committing that partial stripe.
[0192] Further in this example, before any such written data may be
relied upon (before it is considered recoverable, durable content
of the storage system), the commit identities associated with the
written data must be written down to ensure they will end up in the
set of allowed commit identities. The set of allowed commit
identities may be written into headers for subsequent shards, or
the set of allowed commit identities may be written into fast
storage on storage devices. In other cases, with regard to the
storage devices above, the sets of allowed or disallowed commit
identities may be stored in memory mapped durable storage on
storage devices, or written to durable registers on storage
devices. Writing the sets of allowed or disallowed commit
identities into headers for subsequent shards may depend on waiting
for sufficient new data to trigger that new data being persisted
into new shards. In some examples, writing commit identities
directly into fast memory, or memory mapped durable storage, or
durable registers may be done quickly, allowing written data to be
considered durably recoverable more quickly.
[0193] For data to be considered reliably committed, such that the
storage system implementation may continue operating in reliance
upon the data having been reliably committed, associated commit
identities for the data considered reliably committed must be
written to at least R+1 storage devices, though commit identities
could be written to more devices than R+1 before proceeding.
[0194] In some implementations, recovery of recently written data
may be based upon identifying commit identities for the data being
recovered, and upon determining whether those commit identities
were written out as committed. In this example, if the identified
commit identities are determined to be committed, then the data has
been written completely and may be safely considered to have been
committed. In other words, if the identified commit identities are
determined to be committed, then the data may be recoverable
regardless of which R or fewer subsets of an N+R stripe are not
available. To ensure that a subsequent recovery will also see the
commit identities as committed (possibly using a different subset
of available storage devices), recovered commits of commit
identities should be written down again, if they had not already
been written to a sufficient number of storage devices to ensure
they are recoverable.
[0195] In some examples, a situation may arise where a recovery is
unable to identify the commit records for a set of commit
identities. In one example solution, during a given recovery
process, if the given recovery process determines the list of
commit identities whose commits the given recovery process did not
find, then that list of commit identities may be written into a
disallowed list, where the disallowed list explicitly removes a
commit identity from being considered valid content for the storage
system, or where the disallowed list stores an indication that the
commit identity is invalid. More specifically, a recovery process
may generate two lists: (a) the allowed list that represents the
set of all commit identities which represent valid content for the
storage system, and (b) the disallowed list that represents a set
of commit identities that specifically do not represent valid
content for the storage system. Once both the allowed list and the
disallowed list have been determined, the allowed list and the
disallowed list may be written out as new data, in some cases
represented by a set of new commit identities and committed by
writing those new commit identities to a sufficient number of
storage devices.
[0196] Continuing with this example, one solution to determine the
set of commit identities to add to the disallowed set is to
determine which commit identities exist in written data but that
lack persisted commits of those commit identities found during
recovery. Another example solution to determine the set of missing
commit identities is to establish, during operation of the storage
system, a set of allowed future commit identities, and to make the
set of allowed future commit identities durable before any of those
commit identities can be used for writing new data. This results in
three lists: (a) allowed commit identities, (b) disallowed commit
identities, and (c) potential future commit identities. All three
lists can be written together or separately, where the three lists
may be associated with commit identities for the writes that
persist the lists, and committed by writing commit records for
those commit identities to a sufficient number of storage devices.
In some examples, determining commit identities to add into the
disallowed list during recovery may include reading the last
committed future commit identities list (where the committed future
commit identities list may depend upon finding the last such list
for which a commit record could be recovered), determining the
subset of the committed future commit identities list for which no
commit record was found, and then adding that subset to the list of
disallowed commit identities.
[0197] In this example, the lists of allowed, disallowed, and
future commit identities can be simplified by making commit
identities sequential. During normal operation that does not
include faults and corresponding recoveries, the future commit
identities list may be described as, or may indicate, a range of
numbers from some already used and fully committed number to some
future number that has not yet been fully committed. At storage
system or storage controller startup or after a recovery, the start
of that range of numbers may be set to some value that must be past
any commit identity that might have been missed due an unavailable
storage device. Before the sequence numbers within the range have
all been used, a new range may be written, where the process of
writing the new range may use and commit at least one commit
identity--consequently, the new range should be written out prior
to using the last sequence number within the current range.
Further, as data is written, committed, and the data commits are
added to the allowed list, the beginning of the range may be
advanced to a commit identity prior to any still in progress write
and commit.
[0198] Continuing with this example, the use of ranges of
sequential identifiers may also simplify the allowed and disallowed
lists. For example, during recovery, any commit identity not
already on the disallowed list that precedes the first number on
the future commit identity range may be considered allowed--unless
the commit identity was already disallowed such as in a previous
incomplete recovery. Further, even if one of those commit
identities had never been used, the commit identity could not have
been associated with partially written and committed data, which
creates opportunities for compacting allowed and disallowed lists
into a set of ranges. In this example, the set of commit identities
between the start of the future commit identities range and one
prior to the first number in the range for which a commit record of
a commit identity is found is disallowed, creating one range.
Further, the set of commit identities between one after the last
number in the future commit identity range for which a commit
record of a commit identity is found and the last number in the
future commit identity range itself is also disallowed, thereby
creating another range. In some cases, other commit identities in
the future commit identity range may produce a messier set which
may include individual commit identities that are allowed or
disallowed, or potentially compressible subranges where all commit
identities in a range are allowed or disallowed.
[0199] To complete this example, an additional step or
consideration for using ranges may be to track instances of
encountering an error while writing out data prior to the data
being committed. For example, if an erasure coded partial or
complete stripe cannot be written out completely due, for example,
to an erase block or storage device failure, then the content may
be rewritten to new partial or complete stripes on a new set of
devices. In this case, the first failed attempt at writing the data
should be invalidated, and this invalidation may be done by adding
any commit identities associated with the failed writes to the
disallowed list. Otherwise, the simple range model described in the
previous paragraph may fail to disallow the incomplete data on a
recovery and might add the data to the allowed list.
[0200] In some implementations of a unified storage element (320),
the data and recovery models may be implemented without use of
multiple addressable storage classes--where some storage class
activity may be performed invisibly by the storage system. However,
given that a unified storage element (320) may include fast durable
storage and bulk durable storage, providing multiple addressable
storage classes may provide a good basis for speeding up data
storage and system recovery, and a good basis for making data
storage and system recovery work better with flash storage than it
would without multiple addressable storage classes. Examples are
provided below.
[0201] As one example, as mentioned previously, commit identities
may be committed by writing them to memory mapped durable storage,
fast durable storage, or durable registers on a sufficient number
of storage devices. Additionally, transferring data for a partial
or complete stripe to fast durable storage may allow a staging of
data to a storage device before it is transferred to bulk storage,
where operation of that bulk storage might rely on restricted modes
of operation to get the best performance or the longest storage
device lifespan. Further, allowed, disallowed, and future commit
identities, as discussed above, particularly future commit
identities, may be implemented by writing these different types of
identity data to fast durable storage. Particularly, in some cases,
a range of future commit identities may be written as two numbers,
and possibly stored in durable mapped storage or durable registers,
thereby allowing any identity within a range (of perhaps a few
hundred potential commit identities) to be used, and allowing the
future range to be extended or moved by writing down one or two
numbers to alter the beginning or end of the range.
[0202] As another example, reorganizing or reformatting of data
stored in fast durable storage as it is transferred to bulk storage
may not require additional commit phases because the data may, with
high probability, already be guaranteed to be on a sufficient
number of storage devices before a storage controller instructs it
to reorganize or reformat the data during transfer.
[0203] As another example, models of mirroring content to multiple
storage devices prior to converting it to, say, a RAID-6 format, or
models of writing M+R content prior to transforming it into N+R
content may need to associate commit identities with M+R content.
In this example, for any other partial committable write of a
complete erasure coded stripe to commit, it will have at least one
commit identity associated with the content so that it can operate
through the commit algorithm. Further, the recording of a commit
identity could itself be written as a subsequent partial
committable write even to the same erasure coded stripe.
[0204] In some implementations of a unified storage element (320),
the features of a unified storage element (320) may be applied to
various redundancy models. Further, storage devices may also have
internal redundancy, or storage controllers may store redundant
data within storage devices to handle localized failures of
individual blocks, erase blocks, or chips. Most of the following
examples consider storage devices that fail completely, or that do
not power up and make themselves available in a timely fashion.
However, in some cases, some data in, say, an N+R stripe might be
unavailable due to a failed or corrupted read within an otherwise
operating and available storage device. Further, internal
redundancy may reduce the number of cases where this becomes a
problem, to the point that it is statistically implausible, but
internal redundancy may also fail, and some storage devices may not
deploy enough of it to recover from all non-catastrophic failure
modes.
[0205] Continuing with this example, handling such failures may be
solved by an implementation that avoids coming online if exactly R
devices do not boot properly, and the implementation may instead
wait to come online until the number of unavailable storage devices
drops to R-1 or fewer. Alternately, an implementation might
determine the recoverability of all data that might have been in
flight at the time of the fault that preceded recovery, and then
ensure that none of the data encountered an unrecoverable error
before proceeding to making changes that might affect subsequent
recoveries. As an alternative, if a latent corruption is
encountered in a block for which at least one additional (but not
currently available) redundancy shard might have been written, the
storage system may pause or fault waiting to determine if a storage
device for that redundant shard eventually comes back online.
[0206] Further, note that storage systems may use various schemes
for different data. For example, some data may be written as two
mirrored copies that are safe from one failure, other data might be
written in a RAID-5 style N+1 scheme, and other data might be
written in a RAID-6 style N+2 scheme, or even using three or four
mirrored copies or using N+3 schemes for triple failure redundancy
(perhaps for critical but low-volume metadata). Further, different
data might be striped or mirrored across different subsets of
devices, where some subsets may overlap in various ways and other
subsets might not. Any statements about the interrelation between
complete, recoverable, incomplete, and unrecoverable data should
then consider the completion and recoverability model associated
with each type of data, so for example if content of a RAID-6
stripe follows and depends on a supposedly completed RAID-5 or
two-mirrored-copy write, then the RAID-6 stripe's
dual-failure-redundant validity during recovery may depend on the
recoverability of single-failure-redundant content, regardless of
how the set of storage devices for each written dataset do or do
not overlap.
[0207] Further, storage systems may divide up storage devices such
that redundancy operates within constrained groups of devices
(possibly organized around natural divisions such as enclosures or
internal networks and busses). Such constrained groups of devices
may be called pools, or write groups, or RAID groups, or referred
to by other names. In this example, such a constrained group of
devices is referred to as a write group, where a principle behind a
write groups is that any N+R stripe that utilizes any storage
device within the write group will only store shards on other
storage devices in the same write group. For example, if there are
12 storage devices in write group A and 12 storage devices in write
group B, then any pairing of data and redundancy will be
constrained within either the 12 storage devices in write group A
or the 12 storage devices in write group B. In some cases, write
groups for a storage system may be different sizes and not uniform
sizes. Further, as storage is added, write groups may be extended
to include more storage devices, or additional write groups may be
added to the storage system. In some cases, incremental addition of
storage devices may cause making a choice between making existing
write groups too large or making a new write group that is too
small--in such a case, the storage system may split existing write
groups and transform existing content to match the new write group
layouts. These constraints may limit damage caused by failure of
devices by limiting the intersections of failed devices across all
stripes. As an illustrative example: if two devices failed in write
group A and one device failed in write group B, no N+2 stripe could
encounter all three failed devices while writing out stripes or
during recovery because any stripe stored in write group A which
might include write group A's two failed devices will not include
the failed storage device in write group B, and no stripe in write
group B which might include write group B's one failed device will
not include either of the two failed storage devices in write group
A.
[0208] To complete this example, in a storage system that
implements write groups or some similar constraint in allocating
shards of redundant data, previous discussions concerning numbers
of failed storage devices that allow continued operation or a
successful recovery should apply those rules to such individual
groups of storage devices, rather than to the entire storage
system.
[0209] In some additional implementations, erasure coding of staged
data to fast durable storage and to bulk storage may be performed
within a storage system. For example, consider a storage system
with two or more controllers, where each controller is connected to
a plurality of storage devices, where multiples ones of the storage
devices are connected to one or more of the storage controllers,
and where at least some of storage devices include both (1)
addressable fast durable storage that supports a high transfer rate
or a predictable low latency or a high and sustained overwrite rate
or a combination of such properties, and (2) addressable bulk
storage that may be substantially larger than the addressable fast
durable storage or may not support high overwrite rates without
degradation of storage lifespan or might have worse or less
predictable latency or some combination of these properties. In
this example, each type of storage is addressable and separately
accessible for at least read and write from each of their connected
storage controllers, and where the storage devices may further
support commands issued from connected storage controllers to
transfer content to other storage devices possibly from the
addressable fast durable storage and possibly from the addressable
bulk storage, or may further support commands to transfer specific
content from fast durable storage to bulk storage, possibly with
some transformations to format or calculated erasure codes.
[0210] In some implementations, some functions of a storage device
or of a storage controller may be combined or co-resident on
unified elements (i.e., removable elements or circuit boards) of a
storage system, where one or more of the storage controller
functions on one such element may communicate as described herein,
and where storage device functions may be implemented on separate
such elements.
[0211] For example, a storage controller may persist state
associated with operations that should be persisted quickly, where
that persisted state is at least the minimum needed to ensure
recoverability of the operation in cases of faults due to software,
hardware, or power loss, to fast addressable storage across
multiple storage devices to ensure reliability against faults, and
where the persisted state is written as a dynamically allocated
erasure coded stripe or simple mirrored replicas, in patterns that
allow recovery in the face of faults in up to R.sub.1 storage
devices. In this example, the operation may be completed to bulk
storage by writing whatever content remains after deduplication,
compression, or other possibly invalidating operations (for example
overwrites, deletions, unmaps/trim calls, virtual copy operations)
to an erasure coded stripe formed across the bulk storage of
multiple storage devices, where the erasure coded stripe allows
recovery in the face of faults in up to R.sub.2 storage
devices.
[0212] In this example, R.sub.1 and R.sub.2 may be the same. For
example, if a RAID-6 scheme involving P and Q parity shards
according to typical RAID-6 models of erasure coding is used for
storing both fast write content to fast durable storage and
longer-duration content to bulk storage, R.sub.1 and R.sub.2 might
both be 2. Likewise, with a RAID-5 scheme, both R.sub.1 and R2
might be 1. Other schemes such as those involving Galois fields can
support R.sub.1 and/or R.sub.2 of 3 or more. The data shards
associated with content written to fast durable storage might be
one number, say M, such that writes of fast content comprise
M+R.sub.1 stripes written to dynamically allocated shards across
addressable fast durable storage of M+R.sub.1 storage devices,
while data shards associated with content written to bulk storage
might be another number, say N (which may be the same or different
number as M) such that writes of longer-duration content comprise
N+R.sub.2 stripes written to shards across addressable bulk storage
of N+R.sub.2 storage devices.
[0213] In some implementations, the writing of content to either
fast durable storage or bulk durable storage need not be written as
uniform-width erasure coded stripes. For example, some data written
to fast durable storage might be written as M.sub.1+R.sub.1 while
other data might be written as M.sub.2+R.sub.1 where
M.sub.1.noteq.M.sub.2. Similar variations might apply to data
written to bulk storage. Further, if less than a full stripe's
worth of data is available to be written, fewer shards (or
subregions of complete shards) might be written, together with
intermediate calculated redundancy content (e.g., P or P and Q
shards for RAID-5 or RAID-6) calculated from the data so far
available. As further data is ready to be stored, additional shards
(or additional subregions of complete shards) might be written
together with updated redundancy content. Further in this example,
partially written content to bulk storage might result in matching
intermediate redundancy content being written first to addressable
fast durable storage (separately from any redundancy scheme
associated purely with writes of data content to fast durable
storage) with a final version of redundancy content written to bulk
storage as completed shards of redundancy when complete
corresponding data shards have been fully written.
[0214] In some implementations, if content written to fast durable
storage is sufficiently similar to content to be written to bulk
storage, then one or more storage controllers may utilize
capabilities of storage devices such that the content to be stored
in the addressable bulk storage for a specific storage device is
first directed to fast durable storage on the same storage device
(or alternately, content stored in fast durable storage is
eventually stored into bulk storage on the same storage device),
and then transferred (perhaps with some reformatting) from fast
durable storage to bulk durable storage directly by the storage
device, under direction from operations running on the one or more
storage controllers (using the previously described commands to
transfer and possibly reformat content).
[0215] In some implementations, if content written to fast durable
storage is similar to content that is written to bulk storage on a
separate storage device, then if the storage devices and
interconnect support it, and a command is available to do so, and
there is efficiency to be gained in doing so (such as due to
relative availability of interconnect bandwidth), one or more
storage controllers may direct storage devices to transfer (and
possibly reformat) content between the fast durable storage on a
first, source storage device and the bulk storage of a second,
target storage device.
[0216] In some example implementations, it may be possible for data
to transfer between a first storage device and a second storage
device such that the transferred data coupled with data already
present on the second storage device (such as in either fast
durable storage and bulk storage or possibly transferred from a
third or further additional storage devices) can be used to
calculate new data to be stored, such as combining partial data
into combined formatted data, or such as calculating content for
redundancy data shards from prior intermediate content for
redundancy data shards coupled with different intermediate content
for the redundancy data shards or content from data shards. For
example, with a simple XOR parity-based redundancy shard, a first
partial parity calculated by XOR'ing data from a first subset of
data shards and a second partial parity calculated by XOR'ing data
from a second subset of data shards can be transferred from a first
storage device which calculated the first partial parity and from a
second storage device which calculated the second partial parity to
a third storage device which can then XOR the first and second
partial parties together to yield a complete calculated parity
which can be stored into bulk durable storage within the third
storage device.
[0217] Further, in some implementations, Galois field math allows
similar partial results to be merged together to store additional
types of calculated redundancy shards, such as the typical Q shard
for a RAID-6 stripe. For example, with the Galois math described in
the paper "The mathematics of RAID-6" by H. Peter Anvin, 20 Jan.
2004, consider that the final Q shard for a 5+2 RAID-6 stripe is
calculated as:
Q=g.sup.0D.sub.0+g.sup.1D.sub.1+g.sup.2D.sub.2+g.sup.3D.sub.3+g.sup.4D.s-
ub.4
[0218] In this example, a calculation of a partial Q from just the
first two data shards could be calculated as:
Q.sub.p1=g.sup.0.revreaction.D.sub.0+g.sup.1D.sub.1
[0219] In this example, Q.sub.p1 may be stored by a storage
controller on some first partial Q shard storage device as part of
protecting just the first two data shards for the eventual end
resulting 5+2 stripe, and this plus an additional XOR parity stored
in yet another storage device is enough to recover from any two
faults of the devices written for this partial stripe, since as
long as the partial stripe is properly recognized as partial, the
content from g.sup.2D.sub.2+g.sup.3D.sub.3+g.sup.4D.sub.4 can be
inferred to be calculated from empty (zero) data shards D.sub.2,
D.sub.3, and D.sub.4.
[0220] Continuing with this example, a second calculation of a
partial Q from the other three data shards could further be
calculated as:
Q.sub.p2=g.sup.2D.sub.2+g.sup.3D.sub.3+g.sup.4D.sub.4
[0221] In this example, Q.sub.p2 may be stored on a second partial
Q shard storage device as part of protecting those three data
shards, and as with the first partial Q shard, when coupled with an
additionally written partial XOR parity written to another storage
device, the partial content is again protected from any two faults
since the content from g.sup.0D.sub.0+g.sup.1D.sub.1 associated
with the partial Q shard can again be inferred to be calculated
from empty (zero) data shards D.sub.0 and D.sub.1.
[0222] Continuing with this example, a storage device which
eventually received both Q.sub.p1 and Q.sub.p2 may calculate the Q
value for the complete stripe including all five data shards as an
appropriate Galois field addition of Q.sub.p1 and Q.sub.p2.
[0223] In some implementations of a unified storage element (320),
the features of a unified storage element (320) may be applied to
implement data redundancy using commit identities, as described
above, and as additionally described as follows. For example, given
a storage system of the form described above, but where some or all
storage devices (or the storage device aspect of combined storage
elements) may or may not have separately addressable fast durable
storage and bulk durable storage, or where storage devices may or
may not further support the recording of numbers in durable mapped
memory or addressable durable registers, various schemes may be
used that are based at least in part on commit identities as
described above to ensure corruption-free sequences of storage
system and storage device faults and recoveries which are
themselves interrupted by faults where subsequent recoveries are
subject to inconsistent sets of available, slow to become
available, or faulted storage devices.
[0224] In these examples using commit identities, one consequence
is to ensure that within the context of a storage system based on a
redundancy level of R.sub.c for all written content, incompletely
written erasure coded stripes do not result in inconsistent actions
taken by subsequent recoveries in such a sequence of faults,
recoveries and unavailable storage devices. As one example, with a
scheme that relies on three lists of commit identities, which may
be efficiently represented as ranges of numeric commit identities,
which represent and correspond to: allowed (verified as durably
committed) content, disallowed content, and potential content that
should be verified during a recovery. In this example, these three
lists are content that is written to fast durable storage, bulk
durable storage, durable persistent mapped memory, durable
registers, or various combinations of these types of memory by
using mirroring or erasure coding with a redundancy level
sufficient to handle R.sub.id faults, which generally matches the
requirements for storage device fault handling for the storage
device itself. However, in other examples, different content may
use different levels of redundancy (for example, key metadata may
be written with higher redundancy than bulk data, or transactional
content or fast write content may be written to a smaller number of
devices that are distinct from devices used for bulk storage). In
general, R.sub.id should be no less than the highest R.sub.c value
associated with any other storage system content. However, in some
examples, it is also possible that segregated storage devices will
be used to record some or all updates to lists of commit
identities, where the segregated storage devices (e.g., specialized
NVRAM devices) operate with a reduced redundancy level.
[0225] In some embodiments, storage system content (generally
including commit identity lists, though in some cases these commit
identity lists may be an exception, particularly with durable
registers that record ranges) is written to storage devices with
associated commit identities, generally inline with the content
itself (such as being written within shards, pages, or segments
that also store data or metadata that represents the durable
content stored by the storage system). In some examples, written
content may be associated with a commit identity that is in the
potential content list, where once completely written with the
required level of redundancy across storage devices (e.g.,
N+R.sub.c for typical content, or R.sub.id+1-way mirrored or
N.sub.id+R.sub.id erasure coded for content storing lists), the
associated commit identities may be added to the allowed list,
where the allowed list may be written out. In this example, after
the allowed list update that includes a commit identity has been
completely written across storage devices with a corresponding
redundancy, the storage system may be assured to be recoverable by
any subsequent recovery. Further in this example, if there is an
issue in writing content (such as due to a storage device fault
while it is being written), either matching content may be written
to an alternate device or the commit identity associated with the
content may be added to the disallowed list and written out and the
entire update can be performed again as a fresh update to new
locations and with new commit identities. However, in some
examples, even though the writing of an allowed or disallowed list
may itself utilize commit identities from the pending list for the
writing of the list itself, and even though general content writes
depend on associated commit identities being added to the allowed
list and written out, after an allowed list is written out with
required redundancy, the associated content is committed without
the allowed list update being further committed with an additional
update to the allowed list (otherwise, there may be an infinite
progression in order to commit data).
[0226] In some implementations, during recovery, storage system
content may be scanned to reconstruct the allowed commit identity
list, the disallowed commit identity list, and the potential commit
identity list. Entries added to the potential commit identity list
may be confirmed by determining whether the potential commit
identity list update was committed, which can be based on the use
of a previously committed and confirmed entry in the potential
commit identity list. In this example, a commit identity in the
recovered potential commit identity list may be tested for whether
it may have been associated with an addition to the allowed list.
Further in this example, even a partial update adding an identity
to the allowed list should exist only if the associated content had
been completely written and completed as a fully redundant mirrored
or erasure coded stripe, so it is safe to conclude that such a
commit identity may be safely recovered into the allowed list even
if it was only partially written out with the required redundancy.
However, in this example, an identity in the recovered potential
commit identity list for which there is no verifiable entry added
to the allowed list, even as a partially written update (or
possibly in the case of an update which was clearly only partially
written), can be added to the disallowed commit identity list. In
this example, to handle the case of one recovery failing to find a
partially written allowed commit identity and a subsequent recovery
that does find the partially written allowed commit, the second
recovery may use the existence of the commit identity in the
disallowed list to overrule the addition of the commit identity to
the confirmed allowed list.
[0227] In some implementations, as part of a recovery that depends
on the use of confirmed written data, the potential commit identity
list should be resolved into a list of committed identities because
they showed up in an at least partially written allowed list
update, with all other potential commit identities (including any
potential commit identities that might possibly have been partially
written) resolved into the disallowed list. Further, in this
example, prior to a recovery process acting on the results of any
such content, the allowed and disallowed list updates should be
written out and committed. In this example, at this point, any
written content associated with a commit identity may be determined
to have been allowed (it was fully written and committed) or to be
disallowed (it may or may not have been fully written, but it had
not been successfully and completely committed and was backed out
before that had completed). Further, in this example, a partially
written update to an allowed list may end up disallowed in a
sequence of recoveries and faults, where in such a case, such an
update to the allowed list ends up being discarded because the
update is not allowed. In this way, in this example, sequences of
faults and recoveries may prevent missing updates in one recovery
from infecting a subsequent recovery that observes the update.
[0228] In some implementations, during the normal running of a
storage system, it is possible that writes of erasure coded content
may not complete successfully due to, for example, a fault
affecting writes or in receiving status for a write completion. In
such cases, entries may be added to a disallowed list to
effectively negate the associated written content (by disallowing
its related commit identity).
[0229] In some implementations, during normal operation, updates to
lists of commit identities may be persisted by writing into
successive updates that follow the content that the commit identity
is intended to commit. In some examples, alternately, or
additionally, some updates to lists of commit identities, or some
additions of ranges of identities to the potential commit identity
list or to the allowed commit identity list, may be written to
durable registers or to durable memory interfaces on supported
storage devices. In some examples, support for up to R device
faults can be supported either by writing an M+R erasure code
containing these list updates, or by storing R+1 copies of the list
updates (or ranges) on R+1 separate supported storage devices. An
R+1 scheme is particularly suitable with durable registers or
durable memory.
[0230] For further explanation, FIG. 4 sets forth a flow chart
illustrating an example method for performing data storage
operations on a storage element integrating fast durable storage
and bulk durable storage according to some embodiments of the
present disclosure. Although depicted in less detail, the unified
storage element (320) 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 element,
such as unified storage element (320) may include the same, fewer,
additional components as the storage systems described above.
[0231] As described above, a unified storage element (320)
integrates fast durable storage (454) and bulk durable storage
(456). In this example, the unified storage element (320) receives
a data storage operation, such as a read or write operation, and
determines how to make use of either the fast durable storage
(454), or bulk durable storage (456), or both the fast durable
storage (454) and bulk durable storage (456) in performing, or
carrying out, the data storage operation. As described above, a
data storage operation may allow a host computer to make explicit
use of all memory types within the unified storage element (320),
or in some cases, the unified storage element (320) may provide
data storage features without revealing specific aspects of the
underlying memory types. Examples implementations of fast durable
storage (454) and bulk durable storage (456) are described above
with reference to the description of the memory architecture of a
unified storage element (320).
[0232] The example method depicted in FIG. 4 includes receiving
(402), at a storage element integrating fast durable storage (454)
and bulk durable storage (456), a data storage operation (452) from
a host computer (450), where the data storage operation (450) may
be a read command, a write command, an erase command, or generally,
any type of command or operation that utilizes one or more features
that the unified storage element (320) provides. In this example,
the storage element may be a unified storage element (320), and a
host computer (450) may be a remote computing device such as a
remote desktop, a mobile device, a virtual machine within a cloud
computing environment, or some other type of computing device or
instance executing either locally within a storage system or at a
geographically remote location. Receiving (402), at the storage
element integrating fast durable storage (454) and bulk durable
storage (456), the data storage operation (452) may be implemented
by the unified storage element (320) receiving a message at a
communication port connected to a storage area network (158), or a
local area network (160), such as depicted in FIG. 1A, in
accordance with one or more network communication protocols.
[0233] The example method depicted in FIG. 4 also includes
determining (404), in dependence upon the data storage operation
(452), a selection of fast durable storage (454) and bulk durable
storage (456) for performing the data storage operation (452).
Determining (404), in dependence upon the data storage operation
(452), the selection of fast durable storage (454) and bulk durable
storage for performing the data storage operation (452) may be
implemented according to multiple, different techniques as
described above with regard to the features of a unified storage
element (320)--where read and write protocols may be implemented
and provided such that control and transfer of data in between fast
durable storage (454) and bulk durable storage (456) is transparent
and provided according to one or more API commands, or where read
and write protocols may be implemented and provided such that
internal transfers between fast durable storage (454) and bulk
durable storage (456) is not visible. As one example, a single
write operation may include parameters that specify some portion of
data be written to bulk durable storage and also parameters that
specify that some other portion of data be written to fast durable
storage or one or more registers--where such a write operation may
be considered an atomic write operation. In other examples,
depending on the API, or depending on a supported subset of
operations provided a given user, different data storage operation
may specify different uses of the various memory components,
including fast durable storage (454) and bulk durable storage
(456), of the unified storage element (320).
[0234] The example method of FIG. 4 also includes performing (406),
using the determined selection of fast durable storage (454) and
bulk durable storage (456), the data storage operation (452).
Performing (406), using the determined selection of fast durable
storage (454) and bulk durable storage (456), the data storage
operation (452) may be implemented as described above with
reference to the features of a unified storage element (320).
[0235] Example embodiments are described largely in the context of
a fully functional computer system for synchronizing metadata among
storage systems synchronously replicating a dataset. 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.
[0236] 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.
[0237] 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.
[0238] 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.
[0239] 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.
[0240] 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.
[0241] 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.
[0242] 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.
[0243] 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.
[0244] Readers will appreciate that the steps described herein may
be carried out in a variety 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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