U.S. patent application number 15/248716 was filed with the patent office on 2017-03-30 for layered queue based coordination of potentially destructive actions in a dispersed storage network memory.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Patrick A. Tamborski.
Application Number | 20170090824 15/248716 |
Document ID | / |
Family ID | 58407184 |
Filed Date | 2017-03-30 |
United States Patent
Application |
20170090824 |
Kind Code |
A1 |
Tamborski; Patrick A. |
March 30, 2017 |
LAYERED QUEUE BASED COORDINATION OF POTENTIALLY DESTRUCTIVE ACTIONS
IN A DISPERSED STORAGE NETWORK MEMORY
Abstract
Methods for use in a dispersed storage network (DSN) to
coordinate potentially harmful maintenance tasks performed on
storage units of the DSN. For each type of maintenance task to be
performed on the storage units, an ordered list (e.g., a queue) is
generated. Each entry of an ordered list corresponds to a
particular storage unit. For each ordered list, a first entry is
examined to determine whether to initiate execution of the
associated task(s). The determination includes identifying a
storage unit associated with the first entry and predicting the
impact of performing the task including, for example, the impact on
a storage set(s) that includes the identified storage unit. When
the predicted impact compares favorably to an impact threshold
level, the task is initiated and the selected entry is deleted.
When the comparison is unfavorable, the selected entry is moved to
another location in the ordered list for postponed execution.
Inventors: |
Tamborski; Patrick A.;
(Chicago, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
58407184 |
Appl. No.: |
15/248716 |
Filed: |
August 26, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62222819 |
Sep 24, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 3/0661 20130101;
G06F 3/067 20130101; H04L 67/32 20130101; G06F 3/0611 20130101;
G06F 8/65 20130101; H04L 67/06 20130101; G06F 3/0659 20130101; H03M
13/3761 20130101; G06F 3/064 20130101; H04L 67/1097 20130101; H04L
67/42 20130101; G06F 3/0619 20130101; H04L 67/34 20130101; H03M
13/1515 20130101; G06F 3/0614 20130101; G06F 11/1092 20130101; H03M
13/154 20130101; G06F 3/0653 20130101; H04L 67/02 20130101; H04L
63/061 20130101 |
International
Class: |
G06F 3/06 20060101
G06F003/06 |
Claims
1. A method for execution by one or more processing modules of one
or more computing devices of a dispersed storage network (DSN)
having a plurality of storage units, the method comprises:
generating an ordered list for each task type of a plurality of
tasks to be performed on storage units of the plurality of storage
units, including generating at least a first ordered list having a
first ordered list entry, wherein each ordered list entry of an
ordered list is associated with an individual storage unit of the
plurality of storage units; and determining whether to initiate
execution of a task associated with the first ordered list entry,
the determining including: identifying a storage unit associated
with the first ordered list entry; predicting an impact of
performing the task on the identified storage unit to generate a
predicted impact; and performing a comparison of the predicted
impact to an impact threshold level, and (1) when the comparison is
unfavorable, determining not to initiate execution of the task, and
(2) when the comparison is favorable, indicating to perform the
task on the identified storage unit.
2. The method of claim 1, wherein determining not to initiate the
execution of the task includes: identifying another position in the
first ordered list; and moving the first ordered list entry to the
another position.
3. The method of claim 1, wherein indicating to perform the task on
the identified storage unit includes: issuing a task request for
reception by the identified storage unit to facilitate execution of
the task; and removing the first ordered list entry from the first
ordered list.
4. The method of claim 1, wherein predicting an impact of
performing the task includes one or more of: identifying one or
more sets of storage units associated with the storage unit;
obtaining availability information regarding additional storage
units associated with the one or more sets of storage units; or
estimating a performance and/or storage reliability level should
the storage unit be instructed to perform the task.
5. The method of claim 1, wherein the plurality of tasks includes
at least one of the following task types: updating hardware,
rebooting software, restarting a software process, performing an
upgrade, installing a software patch, loading a new software
revision, performing an off-line test, or prioritizing tasks
associated with an online test.
6. The method of claim 1, wherein generating the first ordered list
includes maintaining a queue for a task type associated first
ordered list, wherein the first ordered list entry corresponds to a
top entry in the queue.
7. The method of claim 1, wherein generating an ordered list for
each task type of a plurality of tasks includes generating a
plurality of ordered lists, and wherein determining whether to
initiate execution of a task associated with the first ordered list
entry further includes selecting the first ordered list entry from
an ordered list of the plurality of ordered lists.
8. The method of claim 7, wherein selecting the first ordered list
entry from an ordered list of the plurality of ordered lists is
based on at least one of: the order in which respective entries in
the plurality of ordered lists were generated, the number of
entries in the plurality of ordered lists, a priority level
associated with a task type, storage unit availability levels, a
request, or a predetermination.
9. The method of claim 1, wherein generating an ordered list for
each task type of a plurality of tasks includes generating a
plurality of ordered lists, the method repeated for each ordered
list entry of the plurality of ordered lists.
10. A method for execution by one or more processing modules of one
or more computing devices of a dispersed storage network (DSN), the
DSN having a plurality of storage units, the method comprises:
generating an ordered list for at least one maintenance task type
corresponding to a maintenance task to be performed on one or more
storage units of the plurality of storage units, including
generating at least a first ordered list having a first ordered
list entry, wherein each ordered list entry of an ordered list is
associated with an individual storage unit of the one or more
storage units, and wherein execution of the maintenance task by a
storage unit results in temporary unavailability of the storage
unit; and determining whether to initiate execution of the
maintenance task associated with the first ordered list entry, the
determining including: identifying the storage unit associated with
the first ordered list entry; predicting an impact of performing
the maintenance task on the identified storage unit to generate a
predicted impact; and performing a comparison of the predicted
impact to an impact threshold level, and (1) when the comparison is
unfavorable, determining not to initiate execution of the
maintenance task, and (2) when the comparison is favorable,
indicating to perform the maintenance task on the identified
storage unit.
11. The method of claim 10, wherein the maintenance task
corresponds to at least one of the following maintenance task
types: updating hardware, rebooting software, restarting a software
process, performing an upgrade, installing a software patch,
loading a new software revision, performing an off-line test, or
prioritizing tasks associated with an online test.
12. The method of claim 10, wherein determining not to initiate the
execution of the maintenance task includes: identifying another
position in the first ordered list; and moving the first ordered
list entry to the another position.
13. The method of claim 10, wherein indicating to perform the
maintenance task on the identified storage unit further includes:
issuing a task request for reception by the identified storage unit
to facilitate execution of the maintenance task; and removing the
first ordered list entry from the first ordered list.
14. The method of claim 10, wherein predicting an impact of
performing the maintenance task includes one or more of:
identifying one or more sets of storage units associated with the
storage unit; obtaining availability information regarding
additional storage units associated with the one or more sets of
storage units; or estimating a performance and/or storage
reliability level should the storage unit be instructed to perform
the maintenance task.
15. A computing device of a group of computing devices of a
dispersed storage network (DSN) having a plurality of storage
units, the computing device comprises: a network interface; a local
memory; and a processing module operably coupled to the network
interface and the local memory, wherein the processing module
operates to: generate an ordered list for each task type of a
plurality of tasks to be performed on storage units of the
plurality of storage units, including at least a first ordered list
having a first ordered list entry, wherein each ordered list entry
of an ordered list is associated with an individual storage unit of
the plurality of storage units; and determine whether to initiate
execution of a task associated with the first ordered list entry,
including: identifying a storage unit associated with the first
ordered list entry; predicting an impact of performing the task on
the identified storage unit to generate a predicted impact; and
performing a comparison of the predicted impact to an impact
threshold level, and (1) when the comparison is unfavorable,
determining not to initiate execution of the task, and (2) when the
comparison is favorable, indicating to perform the task on the
identified storage unit.
16. The computing device of claim 15, wherein determining not to
initiate the execution of the task includes: identifying another
position in the first ordered list; and moving the first ordered
list entry to the another position.
17. The computing device of claim 15, wherein indicating to perform
the task on the identified storage unit includes: issuing a task
request, via the network interface, for reception by the identified
storage unit to facilitate execution of the task; and removing the
first ordered list entry from the first ordered list.
18. The computing device of claim 15, wherein predicting an impact
of performing the task includes: identifying one or more sets of
storage units associated with the storage unit; obtaining
availability information regarding additional storage units
associated with the one or more sets of storage units; and
estimating a performance and/or storage reliability level should
the storage unit be instructed to perform the task.
19. The computing device of claim 15, wherein generating the first
ordered list includes maintaining a queue for a task type
associated first ordered list, wherein the first ordered list entry
corresponds to a top entry in the queue.
20. The computing device of claim 15, wherein the plurality of
tasks includes at least one of the following task types: updating
hardware, rebooting software, restarting a software process,
performing an upgrade, installing a software patch, loading a new
software revision, performing an off-line test, or prioritizing
tasks associated with an online test.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority pursuant to 35 U.S.C.
.sctn.119(e) to U.S. Provisional Application No. 62/222,819,
entitled "IDENTIFYING AN ENCODED DATA SLICE FOR REBUILDING," filed
Sep. 24, 2015, which is hereby incorporated herein by reference in
its entirety and made part of the present U.S. Utility Patent
Application for all purposes.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not applicable.
INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT
DISC
[0003] Not applicable.
BACKGROUND OF THE INVENTION
[0004] Technical Field of the Invention
[0005] This invention relates generally to computer networks, and
more particularly to coordination of potentially harmful
maintenance actions in a dispersed storage network.
[0006] Description of Related Art
[0007] Computing devices are known to communicate data, process
data, and/or store data. Such computing devices range from wireless
smart phones, laptops, tablets, personal computers (PC), work
stations, and video game devices, to data centers that support
millions of web searches, stock trades, or on-line purchases every
day. In general, a computing device includes a central processing
unit (CPU), a memory system, user input/output interfaces,
peripheral device interfaces, and an interconnecting bus
structure.
[0008] As is further known, a computer may effectively extend its
CPU by using "cloud computing" to perform one or more computing
functions (e.g., a service, an application, an algorithm, an
arithmetic logic function, etc.) on behalf of the computer.
Further, for large services, applications, and/or functions, cloud
computing may be performed by multiple cloud computing resources in
a distributed manner to improve the response time for completion of
the service, application, and/or function. For example, Hadoop is
an open source software framework that supports distributed
applications enabling application execution by thousands of
computers.
[0009] In addition to cloud computing, a computer may use "cloud
storage" as part of its memory system. As is known, cloud storage
enables a user, via its computer, to store files, applications,
etc. on a remote storage system. The remote storage system may
include a RAID (redundant array of independent disks) system and/or
a dispersed storage system that uses an error correction scheme to
encode data for storage.
[0010] In a RAID system, a RAID controller adds parity data to the
original data before storing it across an array of disks. The
parity data is calculated from the original data such that the
failure of a single disk typically will not result in the loss of
the original data. While RAID systems can address certain memory
device failures, these systems may suffer from effectiveness,
efficiency and security issues. For instance, as more disks are
added to the array, the probability of a disk failure rises, which
may increase maintenance costs. When a disk fails, for example, it
needs to be manually replaced before another disk(s) fails and the
data stored in the RAID system is lost. To reduce the risk of data
loss, data on a RAID device is often copied to one or more other
RAID devices. While this may reduce the possibility of data loss,
it also raises security issues since multiple copies of data may be
available, thereby increasing the chances of unauthorized access.
In addition, co-location of some RAID devices may result in a risk
of a complete data loss in the event of a natural disaster, fire,
power surge/outage, etc.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)
[0011] FIG. 1 is a schematic block diagram of an embodiment of a
dispersed or distributed storage network (DSN) in accordance with
the present disclosure;
[0012] FIG. 2 is a schematic block diagram of an embodiment of a
computing core in accordance with the present disclosure;
[0013] FIG. 3 is a schematic block diagram of an example of
dispersed storage error encoding of data in accordance with the
present disclosure;
[0014] FIG. 4 is a schematic block diagram of a generic example of
an error encoding function in accordance with the present
disclosure;
[0015] FIG. 5 is a schematic block diagram of a specific example of
an error encoding function in accordance with the present
disclosure;
[0016] FIG. 6 is a schematic block diagram of an example of slice
naming information for an encoded data slice (EDS) in accordance
with the present disclosure;
[0017] FIG. 7 is a schematic block diagram of an example of
dispersed storage error decoding of data in accordance with the
present disclosure;
[0018] FIG. 8 is a schematic block diagram of a generic example of
an error decoding function in accordance with the present
disclosure;
[0019] FIG. 9 is a schematic block diagram of an example of DSN
memory storing a plurality of data and a plurality of task codes in
accordance with the present disclosure;
[0020] FIG. 10 is a schematic block diagram of an example of a DSN
performing tasks on stored data/storage units in accordance with
the present disclosure;
[0021] FIG. 11 is a schematic block diagram of an embodiment of a
task distribution module facilitating the example of FIG. 10 in
accordance with the present disclosure;
[0022] FIG. 12 is schematic block diagram of an embodiment of a DSN
in accordance with the present disclosure; and
[0023] FIG. 13 is a logic diagram illustrating an example of
initiating a maintenance task in accordance with the present
disclosure.
DETAILED DESCRIPTION OF THE INVENTION
[0024] FIG. 1 is a schematic block diagram of an embodiment of a
dispersed, or distributed, storage network (DSN) 10 that includes a
plurality of computing devices 12-16, a managing unit 18, an
integrity processing unit 20, and a DSN memory 22. The components
of the DSN 10 are coupled to a network 24, which may include one or
more wireless and/or wire lined communication systems; one or more
non-public intranet systems and/or public internet systems; and/or
one or more local area networks (LAN) and/or wide area networks
(WAN).
[0025] The DSN memory 22 includes a plurality of storage units 36
that may be located at geographically different sites (e.g., one in
Chicago, one in Milwaukee, etc.), at a common site, or a
combination thereof. For example, if the DSN memory 22 includes
eight storage units 36, each storage unit is located at a different
site. As another example, if the DSN memory 22 includes eight
storage units 36, all eight storage units are located at the same
site. As yet another example, if the DSN memory 22 includes eight
storage units 36, a first pair of storage units are at a first
common site, a second pair of storage units are at a second common
site, a third pair of storage units are at a third common site, and
a fourth pair of storage units are at a fourth common site. Note
that a DSN memory 22 may include more or less than eight storage
units 36. Further note that each storage unit 36 includes a
computing core (as shown in FIG. 2, or components thereof) and a
plurality of memory devices for storing dispersed storage (DS)
error encoded data.
[0026] Each of the storage units 36 is operable to store DS error
encoded data and/or to execute (e.g., in a distributed manner)
maintenance tasks and/or data-related tasks. The tasks may be a
simple function (e.g., a mathematical function, a logic function,
an identify function, a find function, a search engine function, a
replace function, etc.), a complex function (e.g., compression,
human and/or computer language translation, text-to-voice
conversion, voice-to-text conversion, etc.), multiple simple and/or
complex functions, one or more algorithms, one or more
applications, maintenance tasks such as those described below,
etc.
[0027] Each of the computing devices 12-16, the managing unit 18,
and the integrity processing unit 20 include a computing core 26,
which includes network interfaces 30-33. Computing devices 12-16
may each be a portable computing device and/or a fixed computing
device. A portable computing device may be a social networking
device, a gaming device, a cell phone, a smart phone, a digital
assistant, a digital music player, a digital video player, a laptop
computer, a handheld computer, a tablet, a video game controller,
and/or any other portable device that includes a computing core. A
fixed computing device may be a computer (PC), a computer server, a
cable set-top box, a satellite receiver, a television set, a
printer, a fax machine, home entertainment equipment, a video game
console, and/or any type of home or office computing equipment.
Note that each of the managing unit 18 and the integrity processing
unit 20 may be separate computing devices, may be a common
computing device, and/or may be integrated into one or more of the
computing devices 12-16 and/or into one or more of the storage
units 36.
[0028] Each interface 30, 32, and 33 includes software and hardware
to support one or more communication links via the network 24
indirectly and/or directly. For example, interface 30 supports a
communication link (e.g., wired, wireless, direct, via a LAN, via
the network 24, etc.) between computing devices 14 and 16. As
another example, interface 32 supports communication links (e.g., a
wired connection, a wireless connection, a LAN connection, and/or
any other type of connection to/from the network 24) between
computing devices 12 and 16 and the DSN memory 22. As yet another
example, interface 33 supports a communication link for each of the
managing unit 18 and the integrity processing unit 20 to the
network 24.
[0029] Computing devices 12 and 16 include a dispersed storage (DS)
client module 34, which enables the computing device to dispersed
storage error encode and decode data (e.g., data object 40) as
subsequently described with reference to one or more of FIGS. 3-8.
In this example embodiment, computing device 16 functions as a
dispersed storage processing agent for computing device 14. In this
role, computing device 16 dispersed storage error encodes and
decodes data on behalf of computing device 14. With the use of
dispersed storage error encoding and decoding, the DSN 10 is
tolerant of a significant number of storage unit failures (the
number of failures is based on parameters of the dispersed storage
error encoding function) without loss of data and without the need
for a redundant or backup copies of the data. Further, the DSN 10
stores data for an indefinite period of time without data loss and
in a secure manner (e.g., the system is very resistant to
unauthorized attempts at accessing the data).
[0030] In operation, the managing unit 18 performs DS management
services. For example, the managing unit 18 establishes distributed
data storage parameters (e.g., vault creation, distributed storage
parameters, security parameters, billing information, user profile
information, etc.) for computing devices 12-14 individually or as
part of a group of user devices. As a specific example, the
managing unit 18 coordinates creation of a vault (e.g., a virtual
memory block associated with a portion of an overall namespace of
the DSN) within the DSN memory 22 for a user device, a group of
devices, or for public access and establishes per vault dispersed
storage (DS) error encoding parameters for a vault. The managing
unit 18 facilitates storage of DS error encoding parameters for
each vault by updating registry information of the DSN 10, where
the registry information may be stored in the DSN memory 22, a
computing device 12-16, the managing unit 18, and/or the integrity
processing unit 20.
[0031] The managing unit 18 creates and stores user profile
information (e.g., an access control list (ACL)) in local memory
and/or within memory of the DSN memory 22. The user profile
information includes authentication information, permissions,
and/or the security parameters. The security parameters may include
encryption/decryption scheme, one or more encryption keys, key
generation scheme, and/or data encoding/decoding scheme.
[0032] The managing unit 18 creates billing information for a
particular user, a user group, a vault access, public vault access,
etc. For instance, the managing unit 18 tracks the number of times
a user accesses a non-public vault and/or public vaults, which can
be used to generate per-access billing information. In another
instance, the managing unit 18 tracks the amount of data stored
and/or retrieved by a user device and/or a user group, which can be
used to generate per-data-amount billing information.
[0033] As another example, the managing unit 18 performs network
operations, network administration, and/or network maintenance.
Network operations includes authenticating user data allocation
requests (e.g., read and/or write requests), managing creation of
vaults, establishing authentication credentials for user devices,
adding/deleting components (e.g., user devices, storage units,
and/or computing devices with a DS client module 34) to/from the
DSN 10, and/or establishing authentication credentials for the
storage units 36. Network administration includes monitoring
devices and/or units for failures, maintaining vault information,
determining device and/or unit activation status, determining
device and/or unit loading, and/or determining any other system
level operation that affects the performance level of the DSN 10.
Network maintenance includes facilitating replacing, upgrading,
repairing, and/or expanding a device and/or unit of the DSN 10.
[0034] To support data storage integrity verification within the
DSN 10, the integrity processing unit 20 (and/or other devices in
the DSN 10) may perform rebuilding of `bad` or missing encoded data
slices. At a high level, the integrity processing unit 20 performs
rebuilding by periodically attempting to retrieve/list encoded data
slices, and/or slice names of the encoded data slices, from the DSN
memory 22. Retrieved encoded slices are checked for errors due to
data corruption, outdated versioning, etc. If a slice includes an
error, it is flagged as a `bad` or `corrupt` slice. Encoded data
slices that are not received and/or not listed may be flagged as
missing slices. Bad and/or missing slices may be subsequently
rebuilt using other retrieved encoded data slices that are deemed
to be good slices in order to produce rebuilt slices. A multi-stage
decoding process may be employed in certain circumstances to
recover data even when the number of valid encoded data slices of a
set of encoded data slices is less than a relevant decode threshold
number. The rebuilt slices may then be written to DSN memory 22.
Note that the integrity processing unit 20 may be a separate unit
as shown, included in DSN memory 22, included in the computing
device 16, and/or distributed among the storage units 36. Examples
of task queuing, initiation and execution by DSN memory 22 is
discussed in greater detail with reference to FIGS. 9-13.
[0035] FIG. 2 is a schematic block diagram of an embodiment of a
computing core 26 that includes a processing module 50, a memory
controller 52, main memory 54, a video graphics processing unit 55,
an input/output (10) controller 56, a peripheral component
interconnect (PCI) interface 58, an IO interface module 60, at
least one IO device interface module 62, a read only memory (ROM)
basic input output system (BIOS) 64, and one or more memory
interface modules. The one or more memory interface module(s)
includes one or more of a universal serial bus (USB) interface
module 66, a host bus adapter (HBA) interface module 68, a network
interface module 70, a flash interface module 72, a hard drive
interface module 74, and a DSN interface module 76.
[0036] The DSN interface module 76 functions to mimic a
conventional operating system (OS) file system interface (e.g.,
network file system (NFS), flash file system (FFS), disk file
system (DFS), file transfer protocol (FTP), web-based distributed
authoring and versioning (WebDAV), etc.) and/or a block memory
interface (e.g., small computer system interface (SCSI), internet
small computer system interface (iSCSI), etc.). The DSN interface
module 76 and/or the network interface module 70 may function as
one or more of the interface 30-33 of FIG. 1. Note that the IO
device interface module 62 and/or the memory interface modules
66-76 may be collectively or individually referred to as IO
ports.
[0037] FIG. 3 is a schematic block diagram of an example of
dispersed storage error encoding of data. When a computing device
12 or 16 has data to store it disperse storage error encodes the
data in accordance with a dispersed storage error encoding process
based on dispersed storage error encoding parameters. The dispersed
storage error encoding parameters include an encoding function
(e.g., information dispersal algorithm, Reed-Solomon, Cauchy
Reed-Solomon, systematic encoding, non-systematic encoding, on-line
codes, etc.), a data segmenting protocol (e.g., data segment size,
fixed, variable, etc.), and per data segment encoding values. The
per data segment encoding values include a total, or pillar width,
number (T) of encoded data slices per encoding of a data segment
(i.e., in a set of encoded data slices); a decode threshold number
(D) of encoded data slices of a set of encoded data slices that are
needed to recover the data segment; a read threshold number (R) of
encoded data slices to indicate a number of encoded data slices per
set to be read from storage for decoding of the data segment;
and/or a write threshold number (W) to indicate a number of encoded
data slices per set that must be accurately stored before the
encoded data segment is deemed to have been properly stored. The
dispersed storage error encoding parameters may further include
slicing information (e.g., the number of encoded data slices that
will be created for each data segment) and/or slice security
information (e.g., per encoded data slice encryption, compression,
integrity checksum, etc.).
[0038] In the present example, Cauchy Reed-Solomon has been
selected as the encoding function (a generic example is shown in
FIG. 4 and a specific example is shown in FIG. 5); the data
segmenting protocol is to divide the data object into fixed sized
data segments; and the per data segment encoding values include: a
pillar width of five, a decode threshold of three, a read threshold
of four, and a write threshold of four. In accordance with the data
segmenting protocol, the computing device 12 or 16 divides the data
(e.g., a file (e.g., text, video, audio, etc.), a data object, or
other data arrangement) into a plurality of fixed sized data
segments (e.g., 1 through Y of a fixed size in range of Kilo-bytes
to Tera-bytes or more). The number of data segments created is
dependent of the size of the data and the data segmenting
protocol.
[0039] The computing device 12 or 16 then disperse storage error
encodes a data segment using the selected encoding function (e.g.,
Cauchy Reed-Solomon) to produce a set of encoded data slices. FIG.
4 illustrates a generic Cauchy Reed-Solomon encoding function,
which includes an encoding matrix (EM), a data matrix (DM), and a
coded matrix (CM). The size of the encoding matrix (EM) is
dependent on the pillar width number (T) and the decode threshold
number (D) of selected per data segment encoding values. To produce
the data matrix (DM), the data segment is divided into a plurality
of data blocks and the data blocks are arranged into D number of
rows with Z data blocks per row. Note that Z is a function of the
number of data blocks created from the data segment and the decode
threshold number (D). The coded matrix is produced by matrix
multiplying the data matrix by the encoding matrix.
[0040] FIG. 5 illustrates a specific example of Cauchy Reed-Solomon
encoding with a pillar number (T) of five and decode threshold
number of three. In this example, a first data segment is divided
into twelve data blocks (D1-D12). The coded matrix includes five
rows of coded data blocks, where the first row of X11-X14
corresponds to a first encoded data slice (EDS 1_1), the second row
of X21-X24 corresponds to a second encoded data slice (EDS 2_1),
the third row of X31-X34 corresponds to a third encoded data slice
(EDS 3_1), the fourth row of X41-X44 corresponds to a fourth
encoded data slice (EDS 4_1), and the fifth row of X51-X54
corresponds to a fifth encoded data slice (EDS 5_1). Note that the
second number of the EDS designation corresponds to the data
segment number. In the illustrated example, the value
X11=aD1+bD5+cD9, X12=aD2+bD6+cD10, . . . X53=mD3+nD7+oD11, and
X54=mD4+nD8+oD12.
[0041] Returning to the discussion of FIG. 3, the computing device
also creates a slice name (SN) for each encoded data slice (EDS) in
the set of encoded data slices. A typical format for a slice name
80 is shown in FIG. 6. As shown, the slice name (SN) 80 includes a
pillar number of the encoded data slice (e.g., one of 1-T), a data
segment number (e.g., one of 1-Y), a vault identifier (ID), a data
object identifier (ID), and may further include revision level
information of the encoded data slices. The slice name functions as
at least part of a DSN address for the encoded data slice for
storage and retrieval from the DSN memory 22.
[0042] As a result of encoding, the computing device 12 or 16
produces a plurality of sets of encoded data slices, which are
provided with their respective slice names to the storage units for
storage. As shown, the first set of encoded data slices includes
EDS 1_1 through EDS 5_1 and the first set of slice names includes
SN 1_1 through SN 5_1 and the last set of encoded data slices
includes EDS 1_Y through EDS 5_Y and the last set of slice names
includes SN 1_Y through SN 5_Y.
[0043] FIG. 7 is a schematic block diagram of an example of
dispersed storage error decoding of a data object that was
dispersed storage error encoded and stored in the example of FIG.
4. In this example, the computing device 12 or 16 retrieves from
the storage units at least the decode threshold number of encoded
data slices per data segment. As a specific example, the computing
device retrieves a read threshold number of encoded data
slices.
[0044] In order to recover a data segment from a decode threshold
number of encoded data slices, the computing device uses a decoding
function as shown in FIG. 8. As shown, the decoding function is
essentially an inverse of the encoding function of FIG. 4. The
coded matrix includes a decode threshold number of rows (e.g.,
three in this example) and the decoding matrix in an inversion of
the encoding matrix that includes the corresponding rows of the
coded matrix. For example, if the coded matrix includes rows 1, 2,
and 4, the encoding matrix is reduced to rows 1, 2, and 4, and then
inverted to produce the decoding matrix.
[0045] FIG. 9 is a schematic block diagram of an example of DSN
memory 22 storing a plurality of data and a plurality of task codes
in accordance with the present disclosure. The illustrated DSN
memory 22 includes a plurality of storage units 1-n (where, for
example, n is an integer greater than or equal to three). Each of
the storage units includes a DS client module 34, a controller 86,
one or more task execution modules 84, and memory 88.
[0046] In this example, the DSN memory 22 stores, in memory 88 of
the storage units, a plurality of dispersed storage (DS) error
encoded data (e.g., 1-n, where n is an integer greater than or
equal to two) and stores a plurality of DS encoded task codes
(e.g., 1-k, where k is an integer greater than or equal to two).
The DS error encoded data may be encoded in accordance with one or
more examples described with reference to FIGS. 3-6, and organized
(for example) in slice groupings or pillar groups. The data that is
encoded into the DS error encoded data may be of any size and/or of
any content. For example, the data may be one or more digital
books, a copy of a company's emails, a large-scale Internet search,
a video security file, one or more entertainment video files (e.g.,
television programs, movies, etc.), data files, and/or any other
large amount of data (e.g., greater than a few Terabytes).
[0047] The tasks that are encoded into a DS encoded task code may
be a simple function (e.g., a mathematical function, a logic
function, an identify function, a find function, a search engine
function, a replace function, etc.), a complex function (e.g.,
compression, human and/or computer language translation,
text-to-voice conversion, voice-to-text conversion, etc.), multiple
simple and/or complex functions, one or more algorithms, one or
more applications, maintenance-related (e.g., to support hardware
upgrades, reboot operations, process restarts, installation of
software patches), etc. The tasks may be encoded into the DS
encoded task code in a similar manner to encoded data (e.g.,
organized in slice groupings or pillar groups). Operational codes
and instructions for certain types of tasks performed by the DSN
memory 22, such as task types relating to some maintenance
operations that are not associated with DS error encoded data
stored in memory 88, may be maintained by other devices/modules of
a DSN.
[0048] In an example of operation, a DS client module of a user
device or computing device issues a dispersed storage task (DST)
request to the DSN memory 22. The DST request may include a request
to retrieve stored data, or a portion thereof, may include a
request to store data that is included with the DST request, may
include a request to perform one or more tasks on stored data, may
include a request to perform one or more tasks on data included
with the DST request, may initiate a maintenance task, etc. In the
cases where the DST request includes a request to store data or to
retrieve data, the DS client module and/or the DSN memory processes
the request. In the case where the DST request includes a request
to perform one or more tasks on data included with the DST request,
or stored data, the DS client module and/or the DSN memory process
the DST request.
[0049] Excluding certain maintenance tasks and the like, the DS
client module generally identifies data and one or more tasks for
the DSN memory to execute upon the identified data. The DST request
may be for a one-time execution of the task or for an on-going
execution of the task. As an example of the latter, as a company
generates daily emails, the DST request may be to daily search new
emails for inappropriate content and, if found, record the content,
the email sender(s), the email recipient(s), email routing
information, notify human resources of the identified email,
etc.
[0050] The controller 86 facilitates execution of tasks and/or
partial task(s). In an example, the controller 86 interprets a
partial task in light of the capabilities of the task execution
module(s) 84. The capabilities include one or more of MIPS
capabilities, processing resources (e.g., quantity and capability
of microprocessors, CPUs, digital signal processors, co-processor,
microcontrollers, arithmetic logic circuitry, and/or any other
analog and/or digital processing circuitry), availability of the
processing resources, etc. If the controller 86 determines that the
task execution module(s) 84 have sufficient capabilities, it
generates task control information. As described more fully below,
the task execution module(s) 84 and/or controller 86 may further
operate to provide status information for use in predicting the
impact of performing a given task before initiating the task.
[0051] The task control information may be a generic instruction
(e.g., perform the task on the stored slice grouping) or a series
of operational codes. In the former instance, the task execution
module 84 includes a co-processor function specifically configured
(fixed or programmed) to perform the desired task. In the latter
instance, the task execution module 84 includes a general processor
topology where the controller stores an algorithm corresponding to
the particular task. In this instance, the controller 86 provides
the operational codes (e.g., assembly language, source code of a
programming language, object code, etc.) of the algorithm to the
task execution module 84 for execution.
[0052] FIG. 10 is a schematic block diagram of an example of a DSN
performing tasks on stored data and/or storage units in accordance
with the present disclosure. In this example, two dispersed storage
(DS) client modules 1-2 are shown: the first may be associated with
a user device and the second may be associated with a processing
unit or a high priority user device (e.g., high priority clearance
user, system administrator, etc.). Each DS client module includes a
list of stored data 92 and a list of tasks codes 94. The list of
stored data 92 includes one or more entries of data identifying
information, where each entry identifies data stored in the DSN
memory 22. The data identifying information (e.g., data ID)
includes one or more of a data file name, a data file directory
listing, DSTN addressing information of the data, a data object
identifier, etc. The list of task codes 94 includes one or more
entries of task code identifying information, when each entry
identifies task codes stored in the DSN memory 22. The task code
identifying information (e.g., task ID) includes one or more of a
task file name, a task file directory listing, DSTN addressing
information of the task, another type of identifier to identify the
task, etc.
[0053] As illustrated, the list of data 92 and the list of task
codes 94 has a smaller number of entries for the first DS client
module than the corresponding lists of the second DS client module.
This may occur because the user device associated with the first DS
client module has fewer privileges in the DSN than the device
associated with the second DS client module. Alternatively, this
may occur because the user device associated with the first DS
client module serves fewer users than the device associated with
the second DS client module and is restricted by the DSN
accordingly. As yet another alternative, this may occur through no
restraints by the DSN, but rather because the operator of the user
device associated with the first DS client module has selected
fewer data and/or fewer tasks than the operator of the device
associated with the second DS client module.
[0054] In an example of operation, the first DS client module
selects one or more data entries and one or more tasks from their
respective lists (e.g., illustrated as selected data ID 96 and
selected task ID 98, respectively). The first DS client module
sends its selections to a task distribution module 90. The task
distribution module 90 may be within a stand-alone device of the
DSN, may be within the user device that contains the first DS
client module, or may be within the DSN memory 22.
[0055] Regardless of the location of the task distribution module,
it generates DST allocation information 100 from the selected task
ID 98 and the selected data ID 96. The DST allocation information
100 includes data partitioning information, task execution
information, and/or intermediate result information. The task
distribution module 90 sends the DST allocation information 100 to
the DSN memory 22. Note that examples of the DST allocation
information are described in conjunction with FIG. 11.
[0056] The DSN memory 22 interprets the DST allocation information
100 to identify the stored DS error encoded data (e.g., DS error
encoded data 2) and to identify the stored DS error encoded task
code (e.g., DS error encoded task code 1). In addition, the DSN
memory 22 interprets the DST allocation information 100 to
determine how the data is to be partitioned and how the task is to
be partitioned. The DSN memory 22 also determines whether the error
encoded data corresponding to selected data ID 96 needs to be
converted from pillar grouping to slice grouping. If so, the DSN
memory 22 converts the selected DS error encoded data into slice
groupings and stores the slice grouping DS error encoded data by
overwriting the pillar grouping DS error encoded data or by storing
it in a different location in the memory of the DSN memory 22
(i.e., does not overwrite the pillar grouping DS error encoded
data).
[0057] The DSN memory 22 partitions the data and the task as
indicated in the DST allocation information 100 and sends the
portions to selected storage units of the DSN memory 22. Each of
the selected storage units performs its partial task(s) on its
slice groupings to produce partial results. The DSN memory 22
collects the partial results from the selected storage units and
provides them, as result information 102, to the task distribution
module. The result information 102 may be the collected partial
results, one or more final results as produced by the DSN memory 22
from processing the partial results in accordance with the DST
allocation information 100, or one or more intermediate results as
produced by the DSN memory 22 from processing the partial results
in accordance with the DST allocation information 100.
[0058] The task distribution module 90 receives the result
information 102 and provides one or more final results 104
therefrom to the first DS client module. The final result(s) 104
may be result information 102 or a result(s) of processing of the
result information 102 by the task distribution module.
[0059] In concurrence with processing the selected task of the
first DS client module, the DSN may process the selected task(s) of
the second DS client module on the selected data(s) of the second
DS client module. Alternatively, the DSN may process the second DS
client module's request subsequent to, or preceding, that of the
first DS client module. Regardless of the ordering and/or parallel
processing of the DS client module requests, the second DS client
module provides its selected data ID 96 and selected task ID 98 to
a task distribution module 90. If the task distribution module 90
is a separate device of the DSN or within the DSN memory, the task
distribution modules 90 coupled to the first and second DS client
modules may be the same module. The task distribution module 90
processes the request of the second DS client module in a similar
manner as it processed the request of the first DS client
module.
[0060] FIG. 11 is a schematic block diagram of an embodiment of a
task distribution module 90 facilitating the example of FIG. 10 in
accordance with the present disclosure. The task distribution
module 90 (e.g., of a managing unit 18) includes a plurality of
tables it uses to generate dispersed storage and task (DST)
allocation information 100 for selected data and selected tasks
received from a DS client module. The tables include data storage
information 108, task storage information 110, task execution
module information 112, and task sub-task mapping information
106.
[0061] The data storage information table 108 includes a data
identification (ID) field 114, a data size field 116, an addressing
information field 118, dispersed storage (DS) information 120, and
may further include other information regarding the data, how the
data is stored, and/or how it can be processed. For example, DS
error encoded data #1 has a data ID of 1, a data size of AA (e.g.,
a byte size of a few Terabytes or more), addressing information of
Addr_1_AA, and DS parameters of 3/5; SEG_1; and SLC_1. In this
example, the addressing information may be a virtual address
corresponding to the virtual address of the first storage word
(e.g., one or more bytes) of the data and information on how to
calculate the other addresses, may be a range of virtual addresses
for the storage words of the data, physical addresses of the first
storage word or the storage words of the data, may be a list of
slice names of the encoded data slices of the data, etc. The DS
parameters may include identity of an error encoding scheme, decode
threshold/pillar width (e.g., 3/5 for the first data entry),
segment security information (e.g., SEG_1), per slice security
information (e.g., SLC_1), and/or any other information regarding
how the data was encoded into data slices.
[0062] The task storage information table 110 includes a task
identification (ID) field 122, a task size field 124, an addressing
information field 126, dispersed storage (DS) information 128, and
may further include other information regarding the task, how it is
stored, and/or how it can be used to process data. For example, DS
encoded task #2 has a task ID of 2, a task size of XY, addressing
information of Addr_2_XY, and DS parameters of 3/5; SEG_2; and
SLC_2. In this example, the addressing information may be a virtual
address corresponding to the virtual address of the first storage
word (e.g., one or more bytes) of the task and information on how
to calculate the other addresses, may be a range of virtual
addresses for the storage words of the task, physical addresses of
the first storage word or the storage words of the task, may be a
list of slices names of the encoded slices of the task code, etc.
The DS parameters may include identity of an error encoding scheme,
decode threshold/pillar width (e.g., 3/5 for the first data entry),
segment security information (e.g., SEG_2), per slice security
information (e.g., SLC_2), and/or any other information regarding
how the task was encoded into encoded task slices. Note that the
segment and/or the per-slice security information include a type of
encryption (if enabled), a type of compression (if enabled),
watermarking information (if enabled), and/or an integrity check
scheme (if enabled).
[0063] The task sub-task mapping information table 106 includes a
task field 136 and a sub-task field 138. The task field 136
identifies a task stored in the memory of DSN memory 22 and the
corresponding sub-task fields 138 indicates whether the task
includes sub-tasks and, if so, how many and if any of the sub-tasks
are ordered (i.e., are dependent on the outcome of another task) or
non-ordered (i.e., are independent of the outcome of another task).
In this example, the task sub-task mapping information table 106
includes an entry for each task stored in memory of the DSN memory
22 (e.g., task 1 through task k). In particular, this example
indicates that task 1 includes 7 sub-tasks, task 2 does not include
sub-tasks, and task k includes r number of sub-tasks (where r is an
integer greater than or equal to two).
[0064] The task execution module information table 112 includes a
storage unit ID field 130, a task execution module ID field 132,
and a task execution module capabilities field 134. The storage
unit ID field 130 includes the identity of storage units in the DSN
memory. The task execution module ID field 132 includes the
identity of each task execution unit in each storage unit. For
example, storage unit 1 includes three task executions modules
(e.g., 1_1, 1_2, and 1_3). The task execution capabilities field
134 includes identity of the capabilities of the corresponding task
execution unit. For example, task execution module 1_1 includes
capabilities X, where X includes one or more of MIPS capabilities,
processing resources (e.g., quantity and capability of
microprocessors, CPUs, digital signal processors, co-processor,
microcontrollers, arithmetic logic circuitry, and/or any other
analog and/or digital processing circuitry), availability of the
processing resources, memory information (e.g., type, size,
availability, etc.), and/or any information germane to executing
one or more tasks.
[0065] From these tables, the task distribution module 90 generates
the DST allocation information 100 to indicate where the data is
stored, how to partition the data, where the task is stored, how to
partition the task, which task execution units should perform which
partial task on which data partitions, where and how intermediate
results are to be stored, etc. If multiple tasks are being
performed on the same data or different data, the task distribution
module factors such information into its generation of the DST
allocation information.
[0066] Certain tasks performed by storage units of a DSN, including
some maintenance tasks, may adversely impact the integrity of the
DSN (e.g., cause irrecoverable data loss or unavailability of
critical services) if performed at the wrong time. Such tasks may
include, for example, updating hardware, reboot operations, process
restarts, installing software patches, and other "potentially
destructive" tasks that result in that result in temporary
unavailability of a storage unit. Novel methodologies are described
herein for coordinated execution of these types of tasks, such that
a limited number of storage units of the DSN (e.g., storage units
of a particular storage set or vault) are impacted at any one point
in time before proceeding to process other storage units.
[0067] As described more fully below in conjunction with FIGS. 12
and 13, such methodologies may involve an automated process that
operates to ensure proper function of the DSN memory while
performing tasks on a desired number of storage units. When such
tasks are to be performed, a selection of associated storage units
is added (e.g., by a managing unit 18 and/or task distribution
module 90) to an ordered list or queue corresponding to the
action(s) to be performed. Queues which have an entry are analyzed
to determine if the storage unit identified at the top of each such
queue can be occupied or otherwise made unavailable during
performance of the associated task without compromising the
reliability, for example, of a storage set/vault in which the
storage unit participates. If so, the relevant queue entry is
deleted and the task is performed. If not, the relevant queue entry
is moved to another location in the queue (e.g., the end of the
queue) and the task is re-evaluated at a later time. Processing
then continues until each queue is empty or until every storage
device has been moved to the end of a queue at least once without
progress. If no progress has been made based on the unavailability
of storage units, further processing may be delayed until the
availability of one or more storage units is restored.
[0068] Referring more particularly to FIG. 12, a schematic block
diagram of a dispersed storage network (DSN) in accordance with the
present disclosure is shown. In the illustrated embodiment, the DSN
includes storage sets 1-2, the network 24 of FIG. 1, and the
managing unit 18 of FIG. 1. Each of the storage sets 1-2 include a
set of storage units, where each storage unit may be associated
with more than one storage set. For example, storage set 1 includes
storage units 1-5 (e.g., a pillar width of five and a decode
threshold of three) and storage set 2 includes storage units 5-11
(e.g., a pillar width of seven and a decode threshold of four).
Each storage unit may be implemented utilizing the storage unit 36
of FIG. 1. Hereafter, each storage set may be interchangeably
referred to as a set of storage units. While the DSN of the
illustrated embodiment functions to initiate a maintenance task as
described more fully below, other types of tasks may be similarly
processed.
[0069] In an example of operation of initiating a maintenance task,
for each maintenance task type of one or more maintenance tasks to
be performed on the storage units of the DSN, the managing unit 18
generates an ordered list (e.g., a queue) of one or more storage
units to perform the maintenance task of the maintenance task type
to produce one or more ordered lists. A maintenance task may
include one or more of updating hardware, rebooting software,
restarting a particular software process, performing an upgrade,
installing a software patch, loading a new software revision,
performing an off-line test, prioritizing tasks associated with an
online test, etc. As an example of generating the ordered list, the
managing unit 18 maintains a queue for the maintenance task type,
where each entry of the queue is associated with a unique storage
unit and where a first ordered list entry corresponds to a top
queue entry (e.g., a next entry to come out of the queue when the
queue is accessed to retrieve a next queue entry).
[0070] For a given ordered list, the managing unit 18 determines
whether to initiate execution of a maintenance task by a
corresponding storage unit for a first ordered list entry (e.g.,
top queue entry). The determining includes one or more of selecting
the top queue entry, identifying a corresponding storage unit
associated with the selected entry, predicting the impact of
performing the maintenance task of the maintenance task type
associated with the given ordered list, initiating/indicating to
perform the maintenance task when the predicted impact compares
favorably to an impact threshold level, and indicating not to
perform the maintenance task when the predicted impact compares
unfavorably to the impact threshold level.
[0071] Predicting the impact of performing a task may include one
or more of identifying one or more storage sets associated with the
storage unit, obtaining availability information regarding other
storage units associated with the one or more storage sets (e.g.,
receiving status information from a DS client module 34 or
controller 86 of each relevant storage unit), and estimating a
performance and/or storage reliability level should the storage
unit be instructed to execute the maintenance task. For example,
the managing unit 18 determines not to initiate execution of a
maintenance task for storage unit 5 when a number of other storage
units of the storage set 1 are unavailable (e.g., storage unit 2 as
indicated by status 1-5) and a resulting availability level of
storage units for the storage set 1 is less than (or compares
unfavorably to) a desired storage unit availability threshold
level; and when a number of other storage units of the storage set
2 are unavailable (e.g., storage unit 9 as indicated by status
5-11) and a resulting availability level of storage units for the
storage set 2 is less than the desired storage unit availability
threshold level. As another example, the managing unit 18
determines to perform a maintenance task for storage unit 4 when
the resulting availability level of storage units of the storage
set 1 is greater than (or compares favorably to) the desired
storage unit availability threshold level.
[0072] When not initiating the execution of the maintenance task,
the managing unit 18 moves the first ordered list entry to another
location within the ordered list. Moving the entry includes at
least one of identifying a position, such as the bottom the queue,
and moving the first ordered list entry to that identified
position. Having moved the first ordered list entry, the managing
unit 18 repeats the process for the next ordered list entry or an
entry in a different ordered list (e.g., corresponding to a
different maintenance task). Selection of an ordered list from a
plurality of ordered lists may be based on, for example, one or
more of: a first-in-first-out (FIFO) approach to task request
processing, the number of entries in respective ordered lists, a
priority level associated with a maintenance task type, storage
unit availability levels, a request, a predetermination, etc.
[0073] For certain tasks that do not depend on a particular storage
unit/set of storage units, the management unit 18 may try an
initial candidate storage unit (e.g., randomly assigned or assigned
based on availability criteria). If the predicted impact of using
the initial candidate storage unit compares unfavorably to relevant
threshold, the management unit 18 may select another candidate
storage unit and repeat the process until a favorable comparison is
identified. If an available storage unit(s) is not identified for
performing the task, the corresponding ordered list entry is moved
to another position in the ordered list or otherwise
de-prioritized.
[0074] When initiating the execution of the maintenance task, the
managing unit 18 issues a maintenance request to the storage unit
for the maintenance task and deletes the maintenance task from the
relevant ordered list. For example, the managing unit 18 issues,
via the network 24, a maintenance message 1-5 to the storage unit 4
to facilitate execution of the associated maintenance task. In
another example, the managing unit 18 issues, via the network 24, a
maintenance message 5-11 to the storage unit 8 to facilitate
execution of an associated maintenance task. Having deleted the
maintenance task, the process is repeated for the next ordered
list.
[0075] FIG. 13 is a logic diagram illustrating an example of
initiating a maintenance task in accordance with the present
disclosure. In particular, a method is presented for use in
conjunction with one or more functions and features described in
conjunction with FIGS. 1-12. The method begins or continues at step
140 where one or more processing modules (e.g., of a dispersed
storage network (DSN) managing unit 18 or computing device 16), for
each maintenance task type of one or more maintenance tasks
performed in storage units of a DSN, generates an ordered list of
one or more storage units to perform a maintenance task of the
maintenance task type to produce one or more ordered lists. For
example, the processing module maintains a queue for a given
maintenance task type, where each entry of the queue is associated
with a unique storage unit.
[0076] For a given ordered list, the method continues at step 142
where the processing module determines whether to initiate
execution of the maintenance task by a storage unit corresponding
to a first ordered list entry. For example, the processing module
selects a top queue entry, identifies a corresponding storage unit,
predicts impact of performing the maintenance task of the
maintenance task type associated with the given ordered list, and
indicates to perform the maintenance task when the predicted impact
compares favorably to an impact threshold level. The method
branches to step 144 where the processing module issues a
maintenance request when the processing module determines to
execute the maintenance task. When the processing module determines
not to execute the maintenance task, the method instead branches to
step 146 where the processing module moves the first-ordered list
entry to another location within the given ordered list. Moving the
entry includes identifying a position and moving the entry to the
identified position (e.g., to the bottom). The method then
continues to step 148 where the processing module selects a next
ordered list or determines to continue processing of entries in the
first ordered list.
[0077] When the maintenance task is to be executed, the processing
module issues (as step 144) a maintenance request to the
corresponding storage unit for the maintenance task and deletes the
maintenance task from the given ordered list. For example, the
processing module generates the maintenance/task request based on
the maintenance/task type of the maintenance task, sends the
maintenance request to the corresponding storage unit, and deletes
the ordered list entry of the maintenance task from the given
ordered list.
[0078] The method continues at step 148 where the processing module
selects a next ordered list or determines to continue processing of
entries in the first ordered list. Selecting a next ordered list
following either of steps 144 or 146 may be based on one or more
of: task pendency durations wherein multiple pending task/sub-task
requests are processed in the order in which they were generated
(i.e., a FIFO approach), the number of entries in at least some of
the ordered lists, a priority level associated with a maintenance
task type, storage unit availability levels, a request, or a
predetermination. Having selected the next ordered list, the method
loops back to step 142 where the processing module determines
whether to initiate execution of the maintenance task (e.g., of the
next ordered list).
[0079] The methods described above in conjunction with the
computing device and the storage units can alternatively be
performed by other modules of the dispersed storage network or by
other devices. For example, any combination of a first module, a
second module, a third module, a fourth module, etc. of the
computing device and the storage units may perform the method
described above. In addition, at least one memory section (e.g., a
first memory section, a second memory section, a third memory
section, a fourth memory section, a fifth memory section, a sixth
memory section, etc. of a non-transitory computer readable storage
medium) that stores operational instructions can, when executed by
one or more processing modules of one or more computing devices
and/or by the storage units of the dispersed storage network (DSN),
cause the one or more computing devices and/or the storage units to
perform any or all of the method steps described above.
[0080] As may be used herein, the terms "substantially" and
"approximately" provides an industry-accepted tolerance for its
corresponding term and/or relativity between items. Such an
industry-accepted tolerance ranges from less than one percent to
fifty percent. Such relativity between items ranges from a
difference of a few percent to magnitude differences. As may also
be used herein, the term(s) "configured to", "operably coupled to",
"coupled to", and/or "coupling" includes direct coupling between
items and/or indirect coupling between items via an intervening
item (e.g., an item includes, but is not limited to, a component,
an element, a circuit, and/or a module) where, for an example of
indirect coupling, the intervening item does not modify the
information of a signal but may adjust its current level, voltage
level, and/or power level. As may further be used herein, inferred
coupling (i.e., where one element is coupled to another element by
inference) includes direct and indirect coupling between two items
in the same manner as "coupled to". As may even further be used
herein, the term "configured to", "operable to", "coupled to", or
"operably coupled to" indicates that an item includes one or more
of power connections, input(s), output(s), etc., to perform, when
activated, one or more its corresponding functions and may further
include inferred coupling to one or more other items. As may still
further be used herein, the term "associated with", includes direct
and/or indirect coupling of separate items and/or one item being
embedded within another item.
[0081] As may be used herein, the term "compares favorably",
indicates that a comparison between two or more items, signals,
etc., provides a desired relationship. For example, when the
desired relationship is that signal 1 has a greater magnitude than
signal 2, a favorable comparison may be achieved when the magnitude
of signal 1 is greater than that of signal 2 or when the magnitude
of signal 2 is less than that of signal 1. As may be used herein,
the term "compares unfavorably", indicates that a comparison
between two or more items, signals, etc., fails to provide the
desired relationship.
[0082] As may also be used herein, the terms "processing module",
"processing circuit", "processor", and/or "processing unit" may be
a single processing device or a plurality of processing devices.
Such a processing device may be a microprocessor, micro-controller,
digital signal processor, microcomputer, central processing unit,
field programmable gate array, programmable logic device, state
machine, logic circuitry, analog circuitry, digital circuitry,
and/or any device that manipulates signals (analog and/or digital)
based on hard coding of the circuitry and/or operational
instructions. The processing module, module, processing circuit,
and/or processing unit may be, or further include, memory and/or an
integrated memory element, which may be a single memory device, a
plurality of memory devices, and/or embedded circuitry of another
processing module, module, processing circuit, and/or processing
unit. Such a memory device may be a read-only memory, random access
memory, volatile memory, non-volatile memory, static memory,
dynamic memory, flash memory, cache memory, and/or any device that
stores digital information. Note that if the processing module,
module, processing circuit, and/or processing unit includes more
than one processing device, the processing devices may be centrally
located (e.g., directly coupled together via a wired and/or
wireless bus structure) or may be distributedly located (e.g.,
cloud computing via indirect coupling via a local area network
and/or a wide area network). Further note that if the processing
module, module, processing circuit, and/or processing unit
implements one or more of its functions via a state machine, analog
circuitry, digital circuitry, and/or logic circuitry, the memory
and/or memory element storing the corresponding operational
instructions may be embedded within, or external to, the circuitry
comprising the state machine, analog circuitry, digital circuitry,
and/or logic circuitry. Still further note that, the memory element
may store, and the processing module, module, processing circuit,
and/or processing unit executes, hard coded and/or operational
instructions corresponding to at least some of the steps and/or
functions illustrated in one or more of the Figures. Such a memory
device or memory element can be included in an article of
manufacture.
[0083] One or more embodiments have been described above with the
aid of method steps illustrating the performance of specified
functions and relationships thereof. The boundaries and sequence of
these functional building blocks and method steps have been
arbitrarily defined herein for convenience of description.
Alternate boundaries and sequences can be defined so long as the
specified functions and relationships are appropriately performed.
Any such alternate boundaries or sequences are thus within the
scope and spirit of the claims. Further, the boundaries of these
functional building blocks have been arbitrarily defined for
convenience of description. Alternate boundaries could be defined
as long as the certain significant functions are appropriately
performed. Similarly, flow diagram blocks may also have been
arbitrarily defined herein to illustrate certain significant
functionality.
[0084] To the extent used, the flow diagram block boundaries and
sequence could have been defined otherwise and still perform the
certain significant functionality. Such alternate definitions of
both functional building blocks and flow diagram blocks and
sequences are thus within the scope and spirit of the claims. One
of average skill in the art will also recognize that the functional
building blocks, and other illustrative blocks, modules and
components herein, can be implemented as illustrated or by discrete
components, application specific integrated circuits, processors
executing appropriate software and the like or any combination
thereof.
[0085] In addition, a flow diagram may include a "start" and/or
"continue" indication. The "start" and "continue" indications
reflect that the steps presented can optionally be incorporated in
or otherwise used in conjunction with other routines. In this
context, "start" indicates the beginning of the first step
presented and may be preceded by other activities not specifically
shown. Further, the "continue" indication reflects that the steps
presented may be performed multiple times and/or may be succeeded
by other activities not specifically shown. Further, while a flow
diagram indicates a particular ordering of steps, other orderings
are likewise possible provided that the principles of causality are
maintained.
[0086] The one or more embodiments are used herein to illustrate
one or more aspects, one or more features, one or more concepts,
and/or one or more examples. A physical embodiment of an apparatus,
an article of manufacture, a machine, and/or of a process may
include one or more of the aspects, features, concepts, examples,
etc. described with reference to one or more of the embodiments
discussed herein. Further, from Figure to Figure, the embodiments
may incorporate the same or similarly named functions, steps,
modules, etc. that may use the same or different reference numbers
and, as such, the functions, steps, modules, etc. may be the same
or similar functions, steps, modules, etc. or different ones.
[0087] Unless specifically stated to the contra, signals to, from,
and/or between elements in a figure of any of the figures presented
herein may be analog or digital, continuous time or discrete time,
and single-ended or differential. For instance, if a signal path is
shown as a single-ended path, it also represents a differential
signal path. Similarly, if a signal path is shown as a differential
path, it also represents a single-ended signal path. While one or
more particular architectures are described herein, other
architectures can likewise be implemented that use one or more data
buses not expressly shown, direct connectivity between elements,
and/or indirect coupling between other elements as recognized by
one of average skill in the art.
[0088] The term "module" is used in the description of one or more
of the embodiments. A module implements one or more functions via a
device such as a processor or other processing device or other
hardware that may include or operate in association with a memory
that stores operational instructions. A module may operate
independently and/or in conjunction with software and/or firmware.
As also used herein, a module may contain one or more sub-modules,
each of which may be one or more modules.
[0089] As may further be used herein, a computer readable memory
includes one or more memory elements. A memory element may be a
separate memory device, multiple memory devices, or a set of memory
locations within a memory device. Such a memory device may be a
read-only memory, random access memory, volatile memory,
non-volatile memory, static memory, dynamic memory, flash memory,
cache memory, and/or any device that stores digital information.
The memory device may be in a form a solid state memory, a hard
drive memory, cloud memory, thumb drive, server memory, computing
device memory, and/or other physical medium for storing digital
information. A computer readable memory/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.
[0090] While particular combinations of various functions and
features of the one or more embodiments have been expressly
described herein, other combinations of these features and
functions are likewise possible. The present disclosure is not
limited by the particular examples disclosed herein and expressly
incorporates these other combinations.
* * * * *