U.S. patent application number 16/255181 was filed with the patent office on 2019-05-23 for method of storing encoded data slices using a distributed agreement protocol.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Manish Motwani, Jason K. Resch, Ilya Volvovski.
Application Number | 20190155689 16/255181 |
Document ID | / |
Family ID | 57683796 |
Filed Date | 2019-05-23 |
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United States Patent
Application |
20190155689 |
Kind Code |
A1 |
Motwani; Manish ; et
al. |
May 23, 2019 |
METHOD OF STORING ENCODED DATA SLICES USING A DISTRIBUTED AGREEMENT
PROTOCOL
Abstract
A system includes a plurality of functional rating modules
configured to execute a deterministic function, a normalizing
function and a scoring function using a set of storage unit
coefficients that are different for each of the functional rating
modules. The functional rating modules are configured to receive an
encoded data slice identifier, perform the deterministic function
using the encoded data slice identifier and a first storage unit
coefficient to produce an interim result, perform the normalization
function using interim result to produce a normalized interim
result, and perform the scoring function by performing a
mathematical function on the normalized interim result to produce a
score. The system also includes a ranking module configured to
receive the score from each of the plurality of functional rating
modules to produce a highest ranked set of storage units for
storing a plurality of sets of encoded data slices.
Inventors: |
Motwani; Manish; (Chicago,
IL) ; Resch; Jason K.; (Chicago, IL) ;
Volvovski; Ilya; (Chicago, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
57683796 |
Appl. No.: |
16/255181 |
Filed: |
January 23, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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15194946 |
Jun 28, 2016 |
10223201 |
|
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16255181 |
|
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62186590 |
Jun 30, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H03M 13/3761 20130101;
H04L 67/1095 20130101; G06F 3/0619 20130101; G06F 3/064 20130101;
G06F 3/065 20130101; G06F 3/067 20130101; G06F 2201/805 20130101;
G06F 16/2471 20190101; H04L 65/4076 20130101; G06F 16/273 20190101;
H04L 67/1097 20130101; H04L 67/16 20130101; H03M 13/33 20130101;
G06F 11/1662 20130101; G06F 11/1076 20130101; G06F 16/27 20190101;
G06F 16/22 20190101; G06F 16/24578 20190101; H04L 67/06 20130101;
H03M 13/1515 20130101 |
International
Class: |
G06F 11/10 20060101
G06F011/10; G06F 16/2457 20060101 G06F016/2457; H03M 13/33 20060101
H03M013/33; G06F 16/27 20060101 G06F016/27; G06F 16/22 20060101
G06F016/22; G06F 3/06 20060101 G06F003/06; H03M 13/37 20060101
H03M013/37; G06F 16/2458 20060101 G06F016/2458; H04L 29/06 20060101
H04L029/06; H04L 29/08 20060101 H04L029/08; G06F 11/16 20060101
G06F011/16 |
Claims
1. A system for selecting a set of storage units of a dispersed
storage network (DSN), the system comprises: a plurality of
functional rating modules, wherein each functional rating module of
the plurality of functional rating modules is configured to execute
a deterministic function, a normalizing function and a scoring
function using a set of storage unit coefficients, wherein the set
of storage unit coefficients differ for each of the functional
rating modules, and wherein each functional rating module is
configured to: receive an encoded data slice identifier; perform
the deterministic function using the encoded data slice identifier
and the set of storage unit coefficients to produce an interim
result; perform the normalization function using interim result to
produce a normalized interim result; and perform the scoring
function by performing a mathematical function on the normalized
interim result to produce a score; a ranking module receiving the
score from each of the plurality of functional rating modules to
produce a highest ranked set of storage units; and sending a
plurality of sets of encoded data slices to the highest ranked set
of storage units for storage therein.
2. The system of claim 1, wherein the set of storage unit
coefficients includes at least a first coefficient and a second
coefficient.
3. The system of claim 2, wherein the first coefficient is a unique
identifier for the set of storage units and the second coefficient
is a weighting factor for the set of storage units.
4. The system of claim 3, wherein the weighting factor includes an
arbitrary bias that adjusts a proportion of selections to an
associated location such that a probability that an encoded data
slice will be mapped to that location is equal to a location weight
divided by a sum of all location weights for all locations of
comparison.
5. The system of claim 1, wherein each functional rating module
generates a unique score.
6. The system of claim 1, wherein the encoded data slice identifier
corresponds to a encoded data slice name or common attributes of
set of encoded data slice names.
7. The system of claim 6, wherein, for a set of encoded data
slices, the encoded data slice identifier specifies a data segment
number, a vault ID, and a data object ID.
8. A computing device configured to execute a decentralized
agreement protocol for selecting a set of storage units of a
dispersed storage network (DSN), the computing device comprises: a
plurality of functional rating modules, wherein each functional
rating module of the plurality of functional rating modules is
configured to execute a deterministic function, a normalizing
function and a scoring function using a set of storage unit
coefficients, wherein the set of storage unit coefficients are
different for each of the functional rating modules, and wherein
each functional rating module is configured to: receive an encoded
data slice identifier; perform the deterministic function using the
encoded data slice identifier and the set of storage unit
coefficients to produce an interim result; perform the
normalization function using interim result to produce a normalized
interim result; and perform the scoring function by performing a
mathematical function on the normalized interim result to produce a
score; a ranking module receiving the score from each of the
plurality of functional rating modules to produce a highest ranked
set of storage units; and sending a plurality of sets of encoded
data slices to the highest ranked set of storage units for storage
therein.
9. The computing device of claim 8, wherein the set of storage unit
coefficients includes at least a first coefficient and a second
coefficient.
10. The computing device of claim 9, wherein the first coefficient
is a unique identifier for the set of storage units and the second
coefficient is a weighting factor for the set of storage units.
11. The computing device of claim 10, wherein the weighting factor
includes an arbitrary bias that adjusts a proportion of selections
to an associated location such that a probability that an encoded
data slice will be mapped to that location is equal to a location
weight divided by a sum of all location weights for all locations
of comparison.
12. The computing device of claim 8, wherein each functional rating
module generates a unique score.
13. The computing device of claim 8, wherein the encoded data slice
identifier corresponds to an encoded data slice name or common
attributes of set of encoded data slices names.
14. The computing device of claim 13, wherein, for a set of encoded
data slices, the encoded data slice identifier specifies a data
segment number, a vault ID, and a data object ID.
15. A computing device of a group of computing devices of a
dispersed storage network (DSN), the computing device comprises: an
interface; a local memory; and a plurality of functional rating
modules operably coupled to the interface and the local memory,
wherein each functional rating module of the plurality of
functional rating modules is configured to execute a deterministic
function, a normalizing function and a scoring function using a set
of storage unit coefficients, wherein the set of storage unit
coefficients are different for each of the functional rating
modules, and wherein each functional rating module is configured
to: receive an encoded data slice identifier; perform the
deterministic function using the encoded data slice identifier and
the set of storage unit coefficient to produce an interim result;
perform the normalization function using interim result to produce
a normalized interim result; and perform the scoring function by
performing a mathematical function on the normalized interim result
to produce a score; and a ranking module operably coupled to the
interface, the local memory and the plurality of functional rating
modules and wherein the ranking module is configured to: receive
the score from each of the plurality of functional rating modules
to produce a highest ranked set of storage units for storing a
plurality of sets of encoded data slices.
16. The computing device of claim 15, wherein the set of storage
unit coefficients includes at least a first coefficient and a
second coefficient.
17. The computing device of claim 16, wherein the first coefficient
is a unique identifier for the set of storage units and the second
coefficient is a weighting factor for the set of storage units.
18. The computing device of claim 17, wherein the weighting factor
includes an arbitrary bias that adjusts a proportion of selections
to an associated location such that a probability that an encoded
data slice will be mapped to that location is equal to a location
weight divided by a sum of all location weights for all locations
of comparison.
19. The computing device of claim 15, wherein each functional
rating module generates a unique score.
20. The computing device of claim 15, wherein, for a set of encoded
data slices, the encoded data slice identifier specifies a data
segment number, a vault ID, and a data object ID.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present U.S. Utility patent application claims priority
pursuant to 35 U.S.C. .sctn. 120 as a continuation of U.S. Utility
application Ser. No. 15/194,946, entitled "METHOD OF STORING
ENCODED DATA SLICES USING A DISTRIBUTED AGREEMENT PROTOCOL," filed
Jun. 28, 2016, which is hereby incorporated herein by reference in
its entirety and made part of the present U.S. Utility patent
application for all purposes.
[0002] U.S. Utility application Ser. No. 15/194,946 claims priority
pursuant to 35 U.S.C. .sctn. 119(e) to U.S. Provisional Application
No. 62/186,590, entitled "ACCESSING DATA WHEN TRANSFERRING THE DATA
BETWEEN STORAGE FACILITIES," filed Jun. 30, 2015.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0003] Not applicable.
INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT
DISC
[0004] Not applicable.
BACKGROUND OF THE INVENTION
Technical Field of the Invention
[0005] This invention relates generally to computer networks and
more particularly to dispersing error encoded data.
Description of Related Art
[0006] 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.
[0007] 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.
[0008] 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 an Internet storage system. The Internet 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.
[0009] In dispersed storage systems maintaining a directory and/or
file system is a challenge. In particular, the encoding and
distribution of data needs to be recorded for accurate retrieval of
the data. It is faster and easier to have the directory and/or file
system in a single shared location. Faster because the data of the
directory and/or file system is readily available and easier to
make changes to a centralized version than a distributed version.
Having the directory and/or file system in a single location
creates a single point of failure and thus undermines one or more
values of dispersed storage systems. As such, most dispersed
storage system encoded and disperse store the directory and/or file
system.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)
[0010] FIG. 1 is a schematic block diagram of an embodiment of a
dispersed or distributed storage network (DSN) in accordance with
the present invention;
[0011] FIG. 2 is a schematic block diagram of an embodiment of a
computing core in accordance with the present invention;
[0012] FIG. 3 is a schematic block diagram of an example of
dispersed storage error encoding of data in accordance with the
present invention;
[0013] FIG. 4 is a schematic block diagram of a generic example of
an error encoding function in accordance with the present
invention;
[0014] FIG. 5 is a schematic block diagram of a specific example of
an error encoding function in accordance with the present
invention;
[0015] FIG. 6 is a schematic block diagram of an example of a slice
name of an encoded data slice (EDS) in accordance with the present
invention;
[0016] FIG. 7 is a schematic block diagram of an example of
dispersed storage error decoding of data in accordance with the
present invention;
[0017] FIG. 8 is a schematic block diagram of a generic example of
an error decoding function in accordance with the present
invention;
[0018] FIG. 9 is a schematic block diagram of an embodiment of a
decentralized, or distributed, agreement protocol (DAP) in
accordance with the present invention;
[0019] FIG. 10 is a schematic block diagram of an example of
storing encoded data slices based on the DAP in accordance with the
present invention;
[0020] FIG. 11 is a schematic block diagram of another example of
storing encoded data slices based on the DAP in accordance with the
present invention;
[0021] FIG. 12 is a logic diagram of an example of a method of
storing encoded data slices based on a DAP in accordance with the
present invention; and
[0022] FIG. 13 is a schematic block diagram of an embodiment of a
decentralized, or distributed, agreement protocol (DAP) in
accordance with the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0023] 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).
[0024] 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 error encoded
data.
[0025] 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.
[0026] 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.
[0027] 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 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).
[0028] 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.
[0029] 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.
[0030] 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 a 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 a per-data-amount billing information.
[0031] 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.
[0032] The integrity processing unit 20 performs 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. For retrieved
encoded slices, they are checked for errors due to data corruption,
outdated version, etc. If a slice includes an error, it is flagged
as a `bad` slice. For encoded data slices that were not received
and/or not listed, they are flagged as missing slices. Bad and/or
missing slices are subsequently rebuilt using other retrieved
encoded data slices that are deemed to be good slices to produce
rebuilt slices. The rebuilt slices are stored in the DSN memory
22.
[0033] 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 (IO) 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.
[0034] 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.
[0035] 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.).
[0036] 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 5, a decode threshold of 3, a read threshold of 4,
and a write threshold of 4. 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.
[0037] 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.
[0038] 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.
[0039] 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. Note that one or more
of the vault ID, the data object ID, the revision information,
and/or other fields (not shown) may be referred to as a source
name. A source name is typically common information for slices
names of a plurality of sets of encoded data slices (e.g., encoded
data slices of the same data object, multiple data objects, or a
portion of a data object).
[0040] 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.
[0041] 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.
[0042] 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.
[0043] FIG. 9 is a schematic block diagram of an embodiment of a
decentralized, or distributed, agreement protocol (DAP) 80 that may
be implemented by a computing device, a storage unit, and/or any
other device or unit of the DSN to determine where to store encoded
data slices or where to find stored encoded data slices. The DAP 80
includes a plurality of functional rating modules 81. Each of the
functional rating modules 81 includes a deterministic function 83,
a normalizing function 85, and a scoring function 87.
[0044] Each functional rating module 81 receives, as inputs, a
source name 82 (which corresponds to shared information by a
plurality of sets of slice names for a plurality of sets of encoded
data slices of a data object) and storage unit (SU) coefficients
(e.g., a first functional rating module 81-1 receives SU 1
coefficients a and b). Based on the inputs, where the SU
coefficients are different for each functional rating module 81,
each functional rating module 81 generates a unique score 93 (e.g.,
an alpha-numerical value, a numerical value, etc.). The ranking
function 84 receives the unique scores 93 and orders them based on
an ordering function (e.g., highest to lowest, lowest to highest,
alphabetical, etc.), to produce a ranking of the storage units
(SUs) 86.
[0045] As a specific example, the first functional module 81-1
receives the source name 82, which corresponds to data object, a
portion of a data object, or multiple data objects and receives SU
coefficients for storage unit 1 of the storage units of the DSN.
The SU coefficients includes a first coefficient (e.g., "a") and a
second coefficient (e.g., "b"). For example, the first coefficient
is a unique identifier for the corresponding storage unit (e.g., SU
#1's ID for SU 1 coefficient "a") and the second coefficient is a
weighting factor for the storage unit. The weighting factors are
derived to ensure, over time, data is stored in the storage units
in a fair and distributed manner based on the capabilities of the
storage units.
[0046] For example, the weighting factor includes an arbitrary bias
which adjusts a proportion of selections to an associated location
such that a probability that a source name will be mapped to that
location is equal to the location weight divided by a sum of all
location weights for all locations of comparison (e.g., locations
correspond to storage units). As a specific example, each storage
unit is associated with a location weight based on storage capacity
such that, storage units with more storage capacity have a higher
location weighting factor than storage units with less storage
capacity.
[0047] The deterministic function 83, which may be a hashing
function, a hash-based message authentication code function, a mask
generating function, a cyclic redundancy code function, hashing
module of a number of locations, consistent hashing, rendezvous
hashing, and/or a sponge function, performs a deterministic
function on a combination and/or concatenation (e.g., add, append,
interleave) of the source name 82 and the first SU coefficient
(e.g., SU 1 coefficient "a") to produce an interim result 89.
[0048] The normalizing function 85 normalizes the interim result 89
to produce a normalized interim result 91. For instance, the
normalizing function 85 divides the interim result 89 by a number
of possible output permutations of the deterministic function 83 to
produce the normalized interim result. For example, if the interim
result is 4,325 (decimal) and the number of possible output
permutations is 10,000, then the normalized result is 0.4325.
[0049] The scoring function 87 performs a mathematical function on
the normalized result 91 to produce the score 93. The mathematical
function may be division, multiplication, addition, subtraction, a
combination thereof, and/or any mathematical operation. For
example, the scoring function divides the second SU coefficient
(e.g., SU 1 coefficient "b") by the negative log of the normalized
result (e.g., e.sup.y=x and/or ln(x)=y). For example, if the second
SU coefficient is 17.5 and the negative log of the normalized
result is 1.5411 (e.g., e.sup.(0.4235)), the score is 11.3555.
[0050] The ranking function 84 receives the scores 93 from each of
the function rating modules 81 and orders them to produce a ranking
of the storage units 86. For example, if the ordering is highest to
lowest and there are five storage units in the DSN, the ranking
function evaluates the scores for five storage units to place them
in a ranked order.
[0051] FIG. 10 is a schematic block diagram of an example of
storing encoded data slices based on the DAP 80. In this example, a
computing device 12-16 executes the DAP to determine where to store
encoded data slices for a first data object 40-1 and for another
data object 40-2. In this example, the DSN only includes five
storage units SU #1 through SU #5. In execution of the DAP 80, as
discussed with reference to FIG. 9, the computing device 12-16
generates a ranked ordering for the first data object 40-1 of SU 3,
SU 2, SU 5, SU 1, and SU 4. In this ranking, SU 3 has the highest
ranking and SU 4 has the lowest ranking.
[0052] In accordance with the ranking order for the first data
object (which is based on a source name of the first data object),
the computing device sends encoded data slices to the storage
units. For example, the computing device encodes data object #1 to
produce Y (e.g., three) sets of encoded data slices, each set
includes five encoded data slices. The computing device sends the
first encoded data slice of each of the three sets to storage unit
#3, sends the second encoded data slice of each of the three sets
to storage unit #2, sends the third encoded data slice of each of
the three sets to storage unit #5, sends the fourth encoded data
slice of each of the three sets to storage unit #1, and sends the
fifth encoded data slice of each of the three sets to storage unit
#4.
[0053] In accordance with the ranking order for the other data
object (which is based on a source name of the other data object),
the computing device sends encoded data slices to the storage
units. For example, the computing device encodes data object #b to
produce Z (e.g., four) sets of encoded data slices, each set
includes five encoded data slices. The computing device sends the
first encoded data slice of each of the four sets to storage unit
#2, sends the second encoded data slice of each of the four sets to
storage unit #1, sends the third encoded data slice of each of the
four sets to storage unit #4, sends the fourth encoded data slice
of each of the four sets to storage unit #3, and sends the fifth
encoded data slice of each of the four sets to storage unit #5.
[0054] The computing device will continue to use the DAP 80 with
the same coefficients while the DSN includes the five storage
units. When the DSN expands to include additional storage units,
the coefficients for the DAP 80 are updated and additional
functional rating modules are added to the DAP; one for each new
storage unit added to the DSN.
[0055] FIG. 11 is a schematic block diagram of another example of
storing encoded data slices based on the DAP when two new storage
units are added to the DSN of FIG. 10. Since there are new storage
units, the DAP 80 is expanded to include seven functional rating
modules 81: one for each storage unit in the DSN. Further, the
coefficients for each of the functional rating modules are updated.
In particular, the second coefficient is updated to reflect a new
weighting factor for the existing functional rating modules 81 and
new coefficients are created for the new functional rating modules
81.
[0056] In this example, the storage units are executing the updated
DAP 80 to which encoded data slices are to be transferred and then
to transfer them. The computing device 12-16 executes the DAP to
determine where the encoded data slices are stored for the first
data object 40-1 and for data object "b" 40-2. In this example, the
DSN now includes seven storage units SU #1 through SU #7, yet only
five are needed to store a set of encoded data slices. In execution
of the updated DAP 80, the computing device 12-16 and the storage
units all generate the same ranked ordering for the first data
object 40-1 of SU 3, SU 2, SU 5, SU 1, SU 6, SU 4, and SU 7 and
generate the same ranked ordering for data object 40-2 of SU 2, SU
1, SU 4, SU 3, SU 7, SU 5, and SU 6. The top five storage units are
selected to store the encoded data slices.
[0057] In accordance with the ranking order for the first data
object, SU 4 is now in the sixth ranked position and new storage
unit SU 6 is in the fifth ranked position. Since only the top five
ranked positions are used, SU 4 transfers it encoded data slices
(e.g., EDS 5_1 through EDS 5_Y) to storage unit SU 6. The other
encoded data slices stay stored in SU 1, SU 2, SU 3, and SU 5.
[0058] In accordance with the ranking order for data object "b", SU
5 is now in the sixth ranked position and new storage unit SU 7 is
in the fifth ranked position. Since only the top five ranked
positions are used, SU 5 transfers it encoded data slices (e.g.,
EDS 5_b through EDS 5_bZ) to storage unit SU 7. The other encoded
data slices stay stored in SU 1, SU 2, SU 3, and SU 4.
[0059] FIG. 12 is a logic diagram of an example of a method of
storing encoded data slices based on a distributed, or
decentralized, agreement protocol (DAP). The method, and/or
portions thereof, may be executed by a computing device 12-18, a
storage unit 36, a managing unit 18, and/or an integrity processing
unit 20. A computing device will be used as a representative entity
performing the method.
[0060] The method begins at step 90 where the computing device
encodes a data object in accordance with dispersed storage error
encoding parameters to produce a plurality of sets of encoded data
slices having a plurality of sets of slice names. For example, the
data object has a source name (e.g., as discussed with reference to
FIG. 6) and the dispersed storage error encoding parameters include
a pillar width number of encoded data slices in a set of encoded
data slices, a decode threshold number, a write threshold number, a
read threshold number, a segmenting scheme, an error encoding
scheme, and a slicing scheme. An example of encoding a data object
into a plurality of sets of encoded data slices was discussed with
reference to FIGS. 3-5 (e.g., Y sets where EDS 1_1-EDS 5_1 is a
first set and EDS 1_Y-EDS5_Y is a last set).
[0061] In an embodiment, the dispersed storage error encoding
parameters are selected such that the pillar width number equals a
number of storage units in the plurality of storage units. For
example, of the DSN includes five storage units, then the pillar
width number (PWN) is selected to be five.
[0062] The method continues at step 92 where the computing device
executes a distributed agreement protocol (DAP) using the unique
source name and coefficients regarding the storage units of the DSN
to produce a ranking of the storage units. For example, the
computing device executes the distributed agreement protocol to
produce unique scoring values for each storage units. Per the DAP,
the computing devices orders the unique scoring values to produce
the ranking storage units. An example of this was discussed with
reference to FIG. 9. In addition, the computing device select the
pillar width number of storage units as the storage units
associated with the first pillar width number of scoring values in
the ranking of the plurality of storage units. For example, if
there are seven storage units in the DSN and the pillar width
number is five, then the first five storage units in the ranking
will be selected.
[0063] The method continues at step 94 where the computing device
identifies the pillar width number (PWN) of storage units (SUs)
based on the ranking of the storage units. The method continues at
step 96 where the computing devices sends the plurality of sets of
encoded data slices (EDSs) to the pillar width number of storage
units for storage therein. In an embodiment, the computing device
sends a first group of encoded data slices to a first storage unit,
a second group of encoded data slices to a second storage unit, and
so on. The grouping of EDSs corresponds to a position in the set of
slices, where the first position is in the first group. Examples of
identifying storage units and sending encoded data slices to them
were discussed with reference to FIGS. 10 and 11.
[0064] The method continues at step 98 where the computing device
determines whether a storage unit (SU) has been added to the DSN.
If not, the method continues at step 100 where the computing
determines whether it has another data object to encoded. If not,
the method repeats at step 98.
[0065] When the computing device has another data object to encode,
the method repeats at step 90 for the other data object. The data
object may be encoded using the same dispersed storage error
encoding parameters as previously encoded data objects or using
different dispersed storage error encoding parameters (e.g., one or
more parameters are different).
[0066] When the DSN adds one or more storage units (SUs), the
method continues at step 102 where the computing device determines
whether the number of storage units in the DSN are equal to or
greater than twice the pillar width number. If not, the method
continues at step 104 where the computing device updates the
coefficients of the distributed agreement protocol in accordance
with the updated storage units. An example was discussed with
reference to FIG. 11.
[0067] The method continues at step 106 where the storage units
execute the distributed agreement protocol based on the source name
and the updated coefficients to produce an updated ranking of the
storage units. The method continues at step 108 where one or more
storage units transfer at least one encoded data slice to the added
storage unit based on the updated ranking of the storage units. An
example was discussed with reference to FIG. 11.
[0068] When the number of storage units in the DSN are equal to or
greater than twice the pillar width number, the method continues at
step 110 where the computing device identifies a different
distributed agreement protocol to use. The method continues at step
112 where the storage units executes the distributed agreement
protocol using a slice identifier and the coefficients regarding
the storage units to produce identified set of storage units. The
method continues at step 114 where one or more storage units
transfer at least one encoded data slice to the added storage unit
based on the identified set of storage units.
[0069] FIG. 13 is a schematic block diagram of an embodiment of a
decentralized, or distributed, agreement protocol (DAP) for
generating identified set of storage units. The DAP 80 is similar
to the DAP of FIG. 9, but uses a slice identifier 120 instead of a
source name 82, uses coefficients for a set of storage units
instead of for individual storage units, and the ranking function
84 outputs an identified set of storage units 122 instead of a
storage unit ranking 86. The slice identifier 120 corresponds to a
slice name or common attributes of set of slices names. For
example, for a set of encoded data slices, the slice identifier 120
specifies a data segment number, a vault ID, and a data object ID,
but leaves open ended, the pillar number.
[0070] In an example of the operation, each of the functional
rating modules 81 generates a score 93 for each set of the storage
units based on the slice identifier 120. The ranking function 84
orders the scores 93 to produce a ranking. But, instead of
outputting the ranking, the ranking function 84 outputs one of the
scores, which corresponds to the identified set of storage
units.
[0071] It is noted that terminologies as may be used herein such as
bit stream, stream, signal sequence, etc. (or their equivalents)
have been used interchangeably to describe digital information
whose content corresponds to any of a number of desired types
(e.g., data, video, speech, audio, etc. any of which may generally
be referred to as `data`).
[0072] 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 and corresponds to, but is not limited to, component
values, integrated circuit process variations, temperature
variations, rise and fall times, and/or thermal noise. 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.
[0073] 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.
[0074] 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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.
[0081] 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.
[0082] 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.
* * * * *