U.S. patent application number 17/362251 was filed with the patent office on 2022-06-09 for generating integrity information in a vast storage system.
The applicant listed for this patent is Pure Storage, Inc.. Invention is credited to Gary W. Grube, Zachary J. Mark, Timothy W. Markison, Jason K. Resch, Sebastien Vas.
Application Number | 20220179745 17/362251 |
Document ID | / |
Family ID | |
Filed Date | 2022-06-09 |
United States Patent
Application |
20220179745 |
Kind Code |
A9 |
Grube; Gary W. ; et
al. |
June 9, 2022 |
Generating Integrity Information in a Vast Storage System
Abstract
A method includes encoding data via erasure coding to produce a
plurality of data slices. The method further includes determining a
plurality of identifiers corresponding to the data. The method
further includes generating integrity information based on the
plurality of identifiers by performing a cyclic redundancy check.
The method further includes storing the plurality of data slices,
the plurality of identifiers, and the integrity information in a
storage system.
Inventors: |
Grube; Gary W.; (Barrington
Hills, IL) ; Markison; Timothy W.; (Mesa, AZ)
; Vas; Sebastien; (Sunnyvale, CA) ; Mark; Zachary
J.; (Chicago, IL) ; Resch; Jason K.; (Warwick,
RI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Pure Storage, Inc. |
Mountain View |
CA |
US |
|
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20210326205 A1 |
October 21, 2021 |
|
|
Appl. No.: |
17/362251 |
Filed: |
June 29, 2021 |
Related U.S. Patent Documents
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11080138 |
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International
Class: |
G06F 11/10 20060101
G06F011/10; G06F 3/06 20060101 G06F003/06 |
Claims
1. A method comprises: encoding data via erasure coding to produce
a plurality of data slices; determining a plurality of identifiers
corresponding to the data; generating integrity information based
on the plurality of identifiers by performing a cyclic redundancy
check; and storing the plurality of data slices, the plurality of
identifiers, and the integrity information in a storage system.
2. The method of claim 1, wherein the data is encoded in accordance
with a width, and wherein a corresponding decoding process can
accommodate a number of failures equal to the width minus an error
coding parameter utilized to encode the data.
3. The method of claim 2, wherein the width is greater than a size
of the data.
4. The method of claim 1, further comprising: performing data
storage integrity verification by periodically retrieving data
slices of the plurality of data slices from the storage system to
verify whether one or more data slices of the plurality of data
slices have been corrupted.
5. The method of claim 1, wherein the plurality of identifiers
identify a virtual memory space that maps to storage units of the
storage system.
6. The method of claim 1, wherein the plurality of identifiers are
determined in conjunction with determining a plurality of virtual
memory addresses corresponding to the plurality of data slices,
wherein each virtual memory address of the plurality of virtual
memory addresses is associated with a physical address, and wherein
the integrity information is generated based on the plurality of
virtual memory addresses.
7. The method of claim 1, wherein storing the plurality of data
slices, the plurality of identifiers, and the integrity information
in the storage system includes sending the plurality of data
slices, the plurality of identifiers, and the integrity information
to a plurality of storage units of the storage system for storage
therein.
8. The method of claim 7, wherein the plurality of data slices and
the plurality of identifiers and the integrity information are sent
to the plurality of storage units based on operational health of
the plurality of storage units.
9. The method of claim 1, wherein determining the integrity
information further comprises: generating data file integrity
information for at least some of the plurality of identifiers; and
generating the integrity information based on the data file
integrity information.
10. The method of claim 1, wherein the data is dispersed storage
error encoded in accordance with dispersed storage error coding
parameters to produce the plurality of data slices.
11. A computer comprises: an interface; a memory; and a processing
module operable to: encode data via erasure coding to produce a
plurality of data slices; determine a plurality of identifiers
corresponding to the data; generate integrity information based on
the plurality of identifiers by performing a cyclic redundancy
check; and store the plurality of data slices, the plurality of
identifiers, and the integrity information in a storage system.
12. The computer of claim 11, wherein the data is encoded in
accordance with a width, and wherein a corresponding decoding
process can accommodate a number of failures equal to the width
minus an error coding parameter algorithm utilized to encode the
data.
13. The computer of claim 12, wherein the width is greater than a
size of the data.
14. The computer of claim 11, wherein the processing module further
functions to: perform data storage integrity verification by
periodically retrieving data slices of the plurality of data slices
from the storage system to verify whether one or more data slices
of the plurality of data slices have been corrupted.
15. The computer of claim 11, wherein the plurality of identifiers
identify a virtual memory space that maps to storage units of the
storage system.
16. The computer of claim 11, wherein the plurality of identifiers
are determined in conjunction with determining a plurality of
virtual memory addresses corresponding to the plurality of data
slices, wherein each virtual memory address of the plurality of
virtual memory addresses is associated with a physical address, and
wherein the integrity information is generated based on the
plurality of virtual memory addresses.
17. The computer of claim 11, wherein storing the plurality of data
slices, the plurality of identifiers, and the integrity information
in the storage system includes sending the plurality of data
slices, the plurality of identifiers and the integrity information
to a plurality of storage units of the storage system for storage
therein.
18. The computer of claim 17, wherein the plurality of data slices
and the plurality of identifiers and the integrity information are
sent to the plurality of storage units based on operational health
of the plurality of storage units.
19. The computer of claim 11, wherein determining the integrity
information further comprises: generating data file integrity
information for at least some of the plurality of identifiers; and
generating the integrity information based on the data file
integrity information.
20. The computer of claim 11, wherein the data is dispersed storage
error encoded in accordance with dispersed storage error coding
parameters to produce the plurality of data slices.
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. 17/023,971, entitled "STORING INTEGRITY
INFORMATION IN A VAST STORAGE SYSTEM", filed Sep. 17, 2020, which
is a continuation-in-part (CIP) of U.S. Utility application Ser.
No. 16/137,681, entitled "CONTENT ARCHIVING IN A DISTRIBUTED
STORAGE NETWORK", filed Sep. 21, 2018, issued as U.S. Pat. No.
10,866,754 on Dec. 15, 2020, which is a continuation-in-part (CIP)
of U.S. Utility application Ser. No. 14/454,013, entitled
"COOPERATIVE DATA ACCESS REQUEST AUTHORIZATION IN A DISPERSED
STORAGE NETWORK", filed Aug. 7, 2014, issued as U.S. Pat. No.
10,154,034 on Dec. 11, 2018, which is a continuation-in-part (CIP)
of U.S. Utility application Ser. No. 13/021,552, entitled "SLICE
RETRIEVAL IN ACCORDANCE WITH AN ACCESS SEQUENCE IN A DISPERSED
STORAGE NETWORK", filed Feb. 4, 2011, issued as U.S. Pat. No.
9,063,881 on Jun. 23, 2015, which claims priority pursuant to 35
U.S.C. .sctn. 119(e) to U.S. Provisional Application No.
61/327,921, entitled "SYSTEM ACCESS AND DATA INTEGRITY VERIFICATION
IN A DISPERSED STORAGE SYSTEM", filed Apr. 26, 2010, all of which
are hereby incorporated herein by reference in their entirety and
made part of the present U.S. Utility Patent Application for all
purposes.
BACKGROUND
Technical Field
[0002] This invention relates generally to computer networks and
more particularly to dispersing error encoded data.
Description of Related Art
[0003] 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.
[0004] 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.
[0005] 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.
[0006] Various conventional storage systems are used to archive
user data. Usually, however, the data to be archived requires a
user to specify a file path to the data to be stored in an archive,
or by requiring a user to specify particular file or object name
for storage.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a schematic block diagram of an embodiment of a
dispersed or distributed storage network (DSN) in accordance with
the present invention;
[0008] FIG. 2 is a schematic block diagram of an embodiment of a
computing core in accordance with the present invention;
[0009] FIG. 3 is a schematic block diagram of an example of
dispersed storage error encoding of data in accordance with the
present invention;
[0010] FIG. 4 is a schematic block diagram of a generic example of
an error encoding function in accordance with the present
invention;
[0011] FIG. 5 is a schematic block diagram of a specific example of
an error encoding function in accordance with the present
invention;
[0012] 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;
[0013] FIG. 7 is a schematic block diagram of an example of
dispersed storage error decoding of data in accordance with the
present invention;
[0014] FIG. 8 is a schematic block diagram of a generic example of
an error decoding function in accordance with the present
invention;
[0015] FIG. 9 is a schematic block diagram of another embodiment of
a computing system in accordance with the present invention;
and
[0016] FIG. 10 is a flowchart illustrating an example of archiving
data in accordance with the present invention.
DETAILED DESCRIPTION
[0017] 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).
[0018] 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.
[0019] 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.
[0020] 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.
[0021] 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).
[0022] 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.
[0023] 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.
[0024] 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.
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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.
[0029] 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.).
[0030] 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.
[0031] 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.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] FIGS. 9 and 10 illustrate particular embodiments in which
content data stored in a user device, or data transmitted between a
user device and an external device, can be automatically and
conditionally archived using a distributed storage network (DSN).
For example, a DS processing agent inside of a device (e.g., a
smart phone, a land based phone, a laptop, desktop, the cable box,
a home security system, a home automation system, etc.) grabs
content, filters it, sorts it, and stores it in a DSN memory. For
example: banking info, home video, pictures, e-mail, SMS, class
notes, website visits, contacts, connections, grades, medical
records, social networking messaging, and/or password lists. The DS
processing agent correlates the data to preferences to determine
how much content to save, and how often to store new content. The
agent also determines operational parameters associated with the
DSN based on one or more of the data type, age, priority, status,
etc. In some implementations, the DS processing utilizes two
different DS units to store different types of critical
information, or to store particular types of critical information
in pillars associated with two different DS units.
[0038] FIG. 9 is a schematic block diagram of another embodiment of
a computing system that includes a user device domain 272, a
dispersed storage (DS) processing unit 96, such as computing device
16, and a dispersed storage network (DSN) memory 22. The user
device domain 272 includes user devices 201-203. Note that the user
device domain 272 may include any number of user devices. The DS
processing unit 96 includes a DS processing module 94 and the DSN
memory 22 includes a plurality of 1.sup.st-N.sup.th DS units. Such
user devices 201-203 of the user device domain 272 are associated
with a common user such that data, information, and/or messages
traversed by the user devices 201-203 share relationship with the
common user. The DS processing unit 96 provides user device 201
access to the DSN memory 22 when the user device 201 does not
include a DS processing module 94, such as DS client module 34.
[0039] The user devices 201-203 may include fixed or portable
devices as discussed previously (e.g., a smart phone, a wired
phone, a laptop computer, a tablet computer, a desktop computer, a
cable set-top box, a smart appliance, a home security system, a
home automation system, etc.). The user devices 201-203 may include
a computing core, one or more interfaces, the DS processing module
94 and/or a collection module 274. For example, user device 201
includes the collection module 274. User device 202 includes the
collection module 274 and the DS processing module 94. User device
3 includes the DS processing module 94 which includes the
collection module 274. The collection module 274 includes a
functional entity (e.g., a software application that runs on a
computing core or as part of a processing module) that intercepts
user data, processes the user data to produce a data
representation, and/or facilitates storage of the data
representation in the DSN memory in accordance with one or more of
metadata, preferences, and/or operational parameters (e.g.,
dispersed storage error coding parameters).
[0040] In an example of operation, the user devices 201-203
traverse the user data from time to time where the user data may
include one or more of banking information, home video, video
broadcasts, pictures from a user camera, e-mail messages, short
message service messages, class notes, website visits, web
downloads, contact lists, social networking connections, school
grades, medical records, social networking messaging, password
lists, and any other user data type associated with the user. Note
that the user data may be communicated from one user device to
another user device and/or from a user device to a module or unit
external to the computing system. Further note that the user data
may be stored in any one or more of the user devices 201-203.
[0041] In another example of operation, the collection module 274
of user device 201 intercepts medical records that are being
processed by user device 201. The collection module 274 determines
metadata based on the medical records and determines preferences
based on a user identifier (ID). The collection module 274
determines whether to archive the medical records based in part on
the medical records, the metadata, and the preferences. The
collection module 274 processes the medical records in accordance
with the preferences to produce a data representation when the
collection module 274 determines to archive the medical records.
For example, the collection module 274 of the user device 201 sends
the data representation 275 to the DS processing unit 96. The data
representation 275 may include one or more of the data, the
metadata, the preferences, and storage guidance. The DS processing
unit 96 determines operational parameters, creates encoded data
slices based on the data representation, and sends the encoded data
slices 11 to the DSN memory 22 with a store command to store the
encoded data slices 11. As another example, the collection module
274 of the user device 201 determines operational parameters based
in part on one or more of the user data, the metadata, the
preferences, and the data representation. Next, the collection
module 274 sends the data representation 275 to the DS processing
unit 96. In this example, the data representation 275 may include
one or more of the operational parameters, the metadata, the
preferences, and storage guidance. The DS processing unit 96
determines final operational parameters based in part on the
operational parameters from the collection module 274, creates
encoded data slices based on the data representation and the final
operational parameters, and sends the encoded data slices 11 to the
DSN memory 22 with a store command to store the encoded data slices
11.
[0042] In yet another example of operation, the collection module
274 of user device 202 intercepts banking records that are being
viewed by user device 202. The collection module 274 determines
metadata based on the banking records and determines preferences
based on a user ID. The collection module 274 determines whether to
archive the banking records based on the banking records, the
metadata, and the preferences. The collection module 274 processes
the banking records in accordance with the preferences to produce a
data representation when the collection module determines to
archive the banking records. For example, the collection module 274
sends the data representation to the DS processing module 94 of the
2.sup.nd DS such that the data representation may include one or
more of the metadata, the preferences, and storage guidance. The DS
processing module 94 determines operational parameters, creates
encoded data slices based on the data representation, and sends the
encoded data slices 11 to the DSN memory 22 with a store command to
store the encoded data slices 11. As another example, the
collection module 274 determines operational parameters based on
one or more of the user data (e.g., the banking records), the
metadata, the preferences, and the data representation. The
collection module 274 sends the data representation to the DS
processing module 94 of the 2.sup.nd DS unit, wherein the data
representation includes one or more of the operational parameters,
the metadata, the preferences, and storage guidance. The DS
processing module 94 determines final operational parameters based
in part on the operational parameters from the collection module,
creates encoded data slices based on the data representation and
the final operational parameters, and sends the encoded data slices
11 to the DSN memory 22 with a store command to store the encoded
data slices 11.
[0043] In a further example of operation, the collection module 274
of user device 203 intercepts home video files that are being
processed by user device 203. The collection module 274 determines
metadata based on one or more of the home video files and
determines preferences based in part on a user ID. The collection
module 274 determines whether to archive the home video files based
on the home video files, the metadata, and the preferences. The
collection module 274 processes the home video files in accordance
with the preferences to produce a data representation when the
collection module 274 determines to archive the home video files.
For example, the collection module 274 sends the data
representation to the DS processing module 94 of the 3.sup.rd DS
unit, wherein the data representation includes one or more of the
metadata, the preferences, and storage guidance. The DS processing
module 94 determines operational parameters, creates encoded data
slices based on the data representation and the operational
parameters, and sends the encoded data slices 11 to the DSN memory
22 with a store command to store the encoded data slices 11. As
another example, the collection module 274 determines operational
parameters based on one or more of the user data (e.g., the home
video files), the metadata, the preferences, and the data
representation. The collection module 274 sends the data
representation to the DS processing module 94 of the 3.sup.rd DS
unit, wherein the data representation includes one or more of the
operational parameters, the metadata, the preferences, and storage
guidance. The DS processing module 94 determines final operational
parameters based on the operational parameters from the collection
module 274, creates encoded data slices based on the data
representation and the final operational parameters, and sends the
encoded data slices 11 to the DSN memory 22 with a store command to
store the encoded data slices 11.
[0044] FIG. 10 is a flowchart illustrating an example of archiving
data. The method begins with step 276 where the processing module
captures user data. Such capturing may include one or more of
monitoring a data stream between a user device and an external
entity, monitoring a data stream internally between functional
elements within the user device, and retrieving stored data from a
memory of the user device. The method continues at step 278 where
the processing module determines metadata, wherein the metadata may
include one or more of a user identifier (ID), a data type, a
source indicator, a destination indicator, a context indicator, a
priority indicator, a status indicator, a time indicator, and a
date indicator. Such a determination may be based on one or more of
the captured user data, current activity or activities of the user
device (e.g., active processes, machines state, input/output
utilization, memory utilization, etc.), geographic location
information, clock information, a sensor input, a user record, a
lookup, a command, a predetermination, and message. For example,
the processing module determines the metadata to include a banking
record data type indicator and a geographic location-based context
indicator when the processing module determines the banking data
type and geographic location information.
[0045] The method continues with step 280 where the processing
module determines preferences, wherein the preferences may include
one or more of archiving priority by data type, archiving
frequency, context priority, status priority, volume priority,
performance requirements, and reliability requirements. Such a
determination may be based on one or more of the user ID, the user
data, the metadata, context information, a lookup, a
predetermination, a command, a query response, and a message. The
method continues at step 282 where the processing module determines
whether to archive data based on one or more of the metadata,
context information, a user ID, a lookup, the preferences, and a
comparison of the metadata to one or more thresholds. For example,
the processing module determines to archive data when the metadata
indicates that the user data comprises new banking records. As
another example, the processing module determines to not archive
data when the metadata indicates that the user data comprises
routine website access information. The method repeats back to step
276 when the processing module determines not to archive data. The
method continues to step 284 when the processing module determines
to archive data.
[0046] The method continues at step 284 where the processing module
processes the user data to produce a data representation, wherein
the data representation may be in a compressed and/or a transformed
form to facilitate storage in a dispersed storage network (DSN)
memory. The processing module processes the data based on one or
more of the captured data, the metadata, the preferences, a
processing method table lookup, a command, a message, and a
predetermination. For example, the processing module processes the
user data to produce a data representation where a size of the data
representation facilitates an optimization of DSN memory storage
efficiency. For instance, the data representation size may be
determined to align with a data segment and data slice sizes such
that memory is not unnecessarily underutilized as data blocks are
stored in dispersed storage (DS) units of the DSN memory.
[0047] The method continues at step 286 where the processing module
determines operational parameters. Such a determination may be
based on one or more of the data representation, the captured user
data, the metadata, the preferences, a processing method table
lookup, a command, a message, and a predetermination. For example,
the processing module determines a pillar width and decode
threshold such that an above average reliability approach to
storing the data representation is provided when the processing
module determines that the metadata indicates that the user data
comprises very high priority financial records requiring a very
long term of storage without failure.
[0048] The method continues at step 288 where the processing module
facilitates storage of the data representation in the DSN memory.
For example, the processing module dispersed storage error encodes
the data representation utilizing the operational parameters to
produce encoded data slices. Next, the processing module sends the
encoded data slices to the DS units of the DSN memory for storage
therein.
[0049] 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, text, graphics, audio, etc. any of
which may generally be referred to as `data`).
[0050] As may be used herein, the terms "substantially" and
"approximately" provides an industry-accepted tolerance for its
corresponding term and/or relativity between items. For some
industries, an industry-accepted tolerance is less than one percent
and, for other industries, the industry-accepted tolerance is 10
percent or more. Other examples of industry-accepted tolerance
range from less than one percent to fifty percent.
Industry-accepted tolerances correspond to, but are not limited to,
component values, integrated circuit process variations,
temperature variations, rise and fall times, thermal noise,
dimensions, signaling errors, dropped packets, temperatures,
pressures, material compositions, and/or performance metrics.
Within an industry, tolerance variances of accepted tolerances may
be more or less than a percentage level (e.g., dimension tolerance
of less than +/-1%). Some relativity between items may range from a
difference of less than a percentage level to a few percent. Other
relativity between items may range from a difference of a few
percent to magnitude of differences.
[0051] 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".
[0052] 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.
[0053] 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.
[0054] As may be used herein, one or more claims may include, in a
specific form of this generic form, the phrase "at least one of a,
b, and c" or of this generic form "at least one of a, b, or c",
with more or less elements than "a", "b", and "c". In either
phrasing, the phrases are to be interpreted identically. In
particular, "at least one of a, b, and c" is equivalent to "at
least one of a, b, or c" and shall mean a, b, and/or c. As an
example, it means: "a" only, "b" only, "c" only, "a" and "b", "a"
and "c", "b" and "c", and/or "a", "b", and "c".
[0055] As may also be used herein, the terms "processing module",
"processing circuit", "processor", "processing circuitry", 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, processing circuitry, 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, processing
circuitry, 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,
processing circuitry, 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, processing circuitry 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,
processing circuitry 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.
[0056] 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.
[0057] 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.
[0058] 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 one or more other routines.
In addition, a flow diagram may include an "end" and/or "continue"
indication. The "end" and/or "continue" indications reflect that
the steps presented can end as described and shown or optionally be
incorporated in or otherwise used in conjunction with one or more
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.
[0059] 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.
[0060] 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.
[0061] 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.
[0062] 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.
[0063] 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.
* * * * *