U.S. patent application number 15/466322 was filed with the patent office on 2017-07-06 for auditing a transaction in a dispersed storage network.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Gary W. Grube, Timothy W. Markison, Jason K. Resch.
Application Number | 20170192684 15/466322 |
Document ID | / |
Family ID | 59227227 |
Filed Date | 2017-07-06 |
United States Patent
Application |
20170192684 |
Kind Code |
A1 |
Grube; Gary W. ; et
al. |
July 6, 2017 |
AUDITING A TRANSACTION IN A DISPERSED STORAGE NETWORK
Abstract
A method for auditing transactions within a dispersed storage
network (DSN) begins by identifying a set of audit objects for a
transaction of the transactions. The method continues by
determining sets of slice names for the set of audit objects based
on information regarding the transaction. The method continues by
generating sets of read requests regarding sets of encoded data
slices based on the sets of slice names. The method continues by
sending the sets of read requests to a set of storage units of the
DSN. In response to the sets of read requests, the method continues
by receiving and decoding a decode threshold number of encoded data
slices of each of the sets of encoded data slices to recover the
set of audit objects and analyzing the set of audit objects for DSN
operational compliance.
Inventors: |
Grube; Gary W.; (Barrington
Hills, IL) ; Markison; Timothy W.; (Mesa, AZ)
; Resch; Jason K.; (Chicago, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
59227227 |
Appl. No.: |
15/466322 |
Filed: |
March 22, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13450000 |
Apr 18, 2012 |
|
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15466322 |
|
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61483856 |
May 9, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 3/067 20130101;
G06F 3/0605 20130101; G06F 3/0644 20130101; G06F 11/3006 20130101;
G06F 2201/81 20130101; H03M 13/616 20130101; G06F 21/62 20130101;
G06F 2211/1028 20130101; G06F 16/2365 20190101; G06F 3/0659
20130101; H03M 13/1515 20130101; G06F 11/3034 20130101; G06F
2201/87 20130101; G06F 11/1076 20130101; G06F 3/0619 20130101; H04L
67/1097 20130101 |
International
Class: |
G06F 3/06 20060101
G06F003/06; G06F 11/10 20060101 G06F011/10 |
Claims
1. A method for auditing transactions within a dispersed storage
network (DSN), the method comprises: for a transaction of the
transactions, identifying, by a device of the DSN, a set of audit
objects, wherein an audit object of the set of audit objects
includes at least one record regarding the transaction, wherein the
set of audit objects is dispersed storage error encoded to produce
sets of encoded data slices; determining, by the device, sets of
slice names for the set of audit objects based on information
regarding the transaction, wherein each slice name of the sets of
slice name includes a pillar number section and a common object
section, the pillar number section contains a unique pillar number
for a corresponding encoded data slice of the sets of slice names,
the common object section contains audit object identifying
information, wherein the common object section includes an audit
vault identifier section and an audit object identifier section,
wherein the audit object identifier section includes at least some
of: a device identifier section, a timestamp section, a target
identifier section, a source identifier section, a sequence number
section, and a transaction type identifier section; generating, by
the device, sets of read requests regarding the sets of encoded
data slices based on the sets of slice names; sending, by the
device, the sets of read requests to a set of storage units of the
DSN; in response to the sets of read requests, receiving and
decoding, by the device, a decode threshold number of encoded data
slices of each of the sets of encoded data slices to recover the
set of audit objects; and analyzing, by the device, the set of
audit objects for DSN operational compliance.
2. The method of claim 1 further comprises: selecting the
transaction based on one or more of: identity of a device involved
in the transaction; a transaction type; and a pseudo random
selection process.
3. The method of claim 1, wherein the identifying the set of audit
objects comprises: when the transaction is regarding a data access
request, identifying the set of audit objects based on encoding
parameters associated with data of the data access request, wherein
the set of audit objects includes a first subset of audit objects
from a device of the transaction to storage units of the
transaction and a second subset of audit objects from the storage
units to the device.
4. The method of claim 3, wherein the analyzing the set of audit
objects comprises: determining that the first subset of audit
objects includes a first appropriate number of audit objects;
determining that the second subset of audit objects includes a
second appropriate number of audit objects; when the first and
second subsets of audit objects include the first and second
appropriate numbers, respectively, determining whether records of
audit objects of the first subset of audit objects correlate with
records of audit objects of the second subset of audit objects; and
when the records of audit objects of the first subset of audit
objects correlate with the records of audit objects of the second
subset of audit objects, indicating that the transaction passed an
audit.
5. The method of claim 4 further comprises: when the records of
audit objects of the first subset of audit objects do not correlate
with the records of audit objects of the second subset of audit
objects, determining whether non-correlation is due to obtaining
less than the second appropriate number of audit objects of the
second subset of audit objects; when the non-correlation is due to
obtaining less than the second appropriate number of audit objects
of the second subset of audit objects, determining whether a
follow-up audit object exists, wherein the follow-up audit object
includes one or more records regarding one or more of: rebuilding,
storage unit service report, storage unit off-line report, and
storage unit failure report; when the follow-up audit object
exists, indicating that the transaction passed the audit; and when
the follow-up audit object does not exist, indicating that the
transaction failed the audit.
6. The method of claim 4 further comprises: when one of the records
of audit objects of the first subset of audit objects does not
correlate with a corresponding record of the records of audit
objects of the second subset of audit objects, indicating that the
transaction failed the audit.
7. The method of claim 3, wherein the data access request includes
one of: a write operation; a read operation; a list request; and a
status report.
8. A computer readable storage device comprises: a first memory
section for storing operational instructions that, when executed by
a computing device, causes the computing device to audit
transactions within a dispersed storage network (DSN) by: for a
transaction of the transactions, identifying, a set of audit
objects, wherein an audit object of the set of audit objects
includes at least one record regarding the transaction, wherein the
set of audit objects is dispersed storage error encoded to produce
sets of encoded data slices; a second memory section for storing
operational instructions that, when executed by the computing
device, causes the computing device to: determine sets of slice
names for the set of audit objects based on information regarding
the transaction, wherein each slice name of the sets of slice name
includes a pillar number section and a common object section, the
pillar number section contains a unique pillar number for a
corresponding encoded data slice of the sets of slice names, the
common object section contains audit object identifying
information, wherein the common object section includes an audit
vault identifier section and an audit object identifier section,
wherein the audit object identifier section includes at least some
of: a device identifier section, a timestamp section, a target
identifier section, a source identifier section, a sequence number
section, and a transaction type identifier section; a third memory
section for storing operational instructions that, when executed by
the computing device, causes the computing device to: generate sets
of read requests regarding set of encoded data slices based on the
sets of slice names; and send the sets of read requests to a set of
storage units of the DSN; a fourth memory section for storing
operational instructions that, when executed by the computing
device, causes the computing device to: in response to the sets of
read requests, receiving and decoding, a decode threshold number of
encoded data slices of each of the sets of encoded data slices to
recover the set of audit objects; and a fifth memory section for
storing operational instructions that, when executed by the
computing device, causes the computing device to: analyze the set
of audit objects for DSN operational compliance.
9. The computer readable storage device of claim 8 further
comprises: a sixth memory section for storing operational
instructions that, when executed by the computing device, causes
the computing device to: select the transaction based on one or
more of: identity of a device involved in the transaction; a
transaction type; and a pseudo random selection process.
10. The computer readable storage device of claim 8, wherein the
first memory section stores further operational instructions that,
when executed by the computing device, causes the computing device
to identify the set of audit objects by: when the transaction is
regarding a data access request, identify the set of audit objects
based on encoding parameters associated with data of the data
access request, wherein the set of audit objects includes a first
subset of audit objects from a device of the transaction to storage
units of the transaction and a second subset of audit objects from
the storage units to the device.
11. The computer readable storage device of claim 10, wherein the
fifth memory section stores further operational instructions that,
when executed by the computing device, causes the computing device
to analyze the set of audit objects by: determining that the first
subset of audit objects includes a first appropriate number of
audit objects; determining that the second subset of audit objects
includes a second appropriate number of audit objects; when the
first and second subsets of audit objects include the first and
second appropriate numbers, respectively, determining whether
records of audit objects of the first subset of audit objects
correlate with records of audit objects of the second subset of
audit objects; and when the records of audit objects of the first
subset of audit objects correlate with the records of audit objects
of the second subset of audit objects, indicating that the
transaction passed an audit.
12. The computer readable storage device of claim 11, wherein the
fifth memory section stores further operational instructions that,
when executed by the computing device, causes the computing device
to: when the records of audit objects of the first subset of audit
objects do not correlate with the records of audit objects of the
second subset of audit objects, determine whether non-correlation
is due to obtaining less than the second appropriate number of
audit objects of the second subset of audit objects is received;
when the non-correlation is due to obtaining less than the second
appropriate number of audit objects of the second subset of audit
objects is received, determine whether a follow-up audit object
exists, wherein the follow-up audit object includes one or more
records regarding one or more of: rebuilding, storage unit service
report, storage unit off-line report, and storage unit failure
report; when the follow-up audit object exists, indicate that the
transaction passed the audit; and when the follow-up audit object
does not exist, indicate that the transaction failed the audit.
13. The computer readable storage device of claim 11, wherein the
fifth memory section stores further operational instructions that,
when executed by the computing device, causes the computing device
to: when one of the records of audit objects of the first subset of
audit objects does not correlate with a corresponding record of the
records of audit objects of the second subset of audit objects,
indicate that the transaction failed the audit.
14. The computer readable storage device of claim 10, wherein the
data access request includes one of: a write operation; a read
operation; a list request; and a status report.
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-in-part of U.S.
Utility application Ser. No. 13/450,000, entitled "RETRIEVING A
HYPERTEXT MARKUP LANGUAGE FILE FROM A DISPERSED STORAGE NETWORK
MEMORY", filed Apr. 18, 2012, which claims priority pursuant to 35
U.S.C. .sctn.119(e) to U.S. Provisional Application No. 61/483,856,
entitled "CONTENT DISTRIBUTION NETWORK UTILIZING A DISPERSED
STORAGE NETWORK", filed May 9, 2011, both 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.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not applicable.
INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT
DISC
[0003] Not applicable.
BACKGROUND OF THE INVENTION
[0004] Technical Field of the Invention
[0005] This invention relates generally to computer networks and
more particularly to dispersing error encoded data.
[0006] Description of Related Art
[0007] Computing devices are known to communicate data, process
data, and/or store data. Such computing devices range from wireless
smart phones, laptops, tablets, personal computers (PC), work
stations, and video game devices, to data centers that support
millions of web searches, stock trades, or on-line purchases every
day. In general, a computing device includes a central processing
unit (CPU), a memory system, user input/output interfaces,
peripheral device interfaces, and an interconnecting bus
structure.
[0008] As is further known, a computer may effectively extend its
CPU by using "cloud computing" to perform one or more computing
functions (e.g., a service, an application, an algorithm, an
arithmetic logic function, etc.) on behalf of the computer.
Further, for large services, applications, and/or functions, cloud
computing may be performed by multiple cloud computing resources in
a distributed manner to improve the response time for completion of
the service, application, and/or function. For example, Hadoop is
an open source software framework that supports distributed
applications enabling application execution by thousands of
computers.
[0009] In addition to cloud computing, a computer may use "cloud
storage" as part of its memory system. As is known, cloud storage
enables a user, via its computer, to store files, applications,
etc. on 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.
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
dispersed storage network memory in accordance with the present
invention;
[0019] FIG. 10 is a schematic block diagram of an embodiment of a
slice name in accordance with the present invention;
[0020] FIG. 11 is a logic flow diagram of an embodiment of an
example of creating an audit object in accordance with the present
invention;
[0021] FIG. 12A is a diagram illustrating an example of an audit
object file structure in accordance with the present invention;
[0022] FIG. 12B is a diagram illustrating an example of an audit
record file structure in accordance with the present invention;
[0023] FIG. 12C is a diagram illustrating an example of integrity
information structure in accordance with the present invention;
and
[0024] FIG. 13 is logic flow diagram of an example of auditing a
transaction in accordance with the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0025] 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).
[0026] 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.
[0027] Each of the computing devices 12-16, the managing unit 18,
and the integrity processing unit 20 include a computing core 26,
which includes network interfaces 30-33. Computing devices 12-16
may each be a portable computing device and/or a fixed computing
device. A portable computing device may be a social networking
device, a gaming device, a cell phone, a smart phone, a digital
assistant, a digital music player, a digital video player, a laptop
computer, a handheld computer, a tablet, a video game controller,
and/or any other portable device that includes a computing core. A
fixed computing device may be a computer (PC), a computer server, a
cable set-top box, a satellite receiver, a television set, a
printer, a fax machine, home entertainment equipment, a video game
console, and/or any type of home or office computing equipment.
Note that each of the managing unit 18 and the integrity processing
unit 20 may be separate computing devices, may be a common
computing device, and/or may be integrated into one or more of the
computing devices 12-16 and/or into one or more of the storage
units 36.
[0028] Each interface 30, 32, and 33 includes software and hardware
to support one or more communication links via the network 24
indirectly and/or directly. For example, interface 30 supports a
communication link (e.g., wired, wireless, direct, via a LAN, via
the network 24, etc.) between computing devices 14 and 16. As
another example, interface 32 supports communication links (e.g., a
wired connection, a wireless connection, a LAN connection, and/or
any other type of connection to/from the network 24) between
computing devices 12 and 16 and the DSN memory 22. As yet another
example, interface 33 supports a communication link for each of the
managing unit 18 and the integrity processing unit 20 to the
network 24.
[0029] Computing devices 12 and 16 include a dispersed storage (DS)
client module 34, which enables the computing device to dispersed
storage error encode and decode data (e.g., data 40) as
subsequently described with reference to one or more of FIGS. 3-8.
In this example embodiment, computing device 16 functions as a
dispersed storage processing agent for computing device 14. In this
role, computing device 16 dispersed storage error encodes and
decodes data on behalf of computing device 14. With the use of
dispersed storage error encoding and decoding, the DSN 10 is
tolerant of a significant number of storage unit failures (the
number of failures is based on parameters of the dispersed storage
error encoding function) without loss of data and without the need
for a redundant or backup copies of the data. Further, the DSN 10
stores data for an indefinite period of time without data loss and
in a secure manner (e.g., the system is very resistant to
unauthorized attempts at accessing the data).
[0030] In operation, the managing unit 18 performs DS management
services. For example, the managing unit 18 establishes distributed
data storage parameters (e.g., vault creation, distributed storage
parameters, security parameters, billing information, user profile
information, etc.) for computing devices 12-14 individually or as
part of a group of user devices. As a specific example, the
managing unit 18 coordinates creation of a vault (e.g., a virtual
memory block associated with a portion of an overall namespace of
the DSN) within the DSN memory 22 for a user device, a group of
devices, or for public access and establishes per vault dispersed
storage (DS) error encoding parameters for a vault. The managing
unit 18 facilitates storage of DS error encoding parameters for
each vault by updating registry information of the DSN 10, where
the registry information may be stored in the DSN memory 22, a
computing device 12-16, the managing unit 18, and/or the integrity
processing unit 20.
[0031] The managing unit 18 creates and stores user profile
information (e.g., an access control list (ACL)) in local memory
and/or within memory of the DSN memory 22. The user profile
information includes authentication information, permissions,
and/or the security parameters. The security parameters may include
encryption/decryption scheme, one or more encryption keys, key
generation scheme, and/or data encoding/decoding scheme.
[0032] The managing unit 18 creates billing information for a
particular user, a user group, a vault access, public vault access,
etc. For instance, the managing unit 18 tracks the number of times
a user accesses a non-public vault and/or public vaults, which can
be used to generate 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.
[0033] As another example, the managing unit 18 performs network
operations, network administration, and/or network maintenance.
Network operations includes authenticating user data allocation
requests (e.g., read and/or write requests), managing creation of
vaults, establishing authentication credentials for user devices,
adding/deleting components (e.g., user devices, storage units,
and/or computing devices with a DS client module 34) to/from the
DSN 10, and/or establishing authentication credentials for the
storage units 36. Network administration includes monitoring
devices and/or units for failures, maintaining vault information,
determining device and/or unit activation status, determining
device and/or unit loading, and/or determining any other system
level operation that affects the performance level of the DSN 10.
Network maintenance includes facilitating replacing, upgrading,
repairing, and/or expanding a device and/or unit of the DSN 10.
[0034] 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.
[0035] FIG. 2 is a schematic block diagram of an embodiment of a
computing core 26 that includes a processing module 50, a memory
controller 52, main memory 54, a video graphics processing unit 55,
an input/output (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.
[0036] The DSN interface module 76 functions to mimic a
conventional operating system (OS) file system interface (e.g.,
network file system (NFS), flash file system (FFS), disk file
system (DFS), file transfer protocol (FTP), web-based distributed
authoring and versioning (WebDAV), etc.) and/or a block memory
interface (e.g., small computer system interface (SCSI), internet
small computer system interface (iSCSI), etc.). The DSN interface
module 76 and/or the network interface module 70 may function as
one or more of the interface 30-33 of FIG. 1. Note that the 10
device interface module 62 and/or the memory interface modules
66-76 may be collectively or individually referred to as 10
ports.
[0037] FIG. 3 is a schematic block diagram of an example of
dispersed storage error encoding of data. When a computing device
12 or 16 has data to store it disperse storage error encodes the
data in accordance with a dispersed storage error encoding process
based on dispersed storage error encoding parameters. The dispersed
storage error encoding parameters include an encoding function
(e.g., information dispersal algorithm, Reed-Solomon, Cauchy
Reed-Solomon, systematic encoding, non-systematic encoding, on-line
codes, etc.), a data segmenting protocol (e.g., data segment size,
fixed, variable, etc.), and per data segment encoding values. The
per data segment encoding values include a total, or pillar width,
number (T) of encoded data slices per encoding of a data segment
(i.e., in a set of encoded data slices); a decode threshold number
(D) of encoded data slices of a set of encoded data slices that are
needed to recover the data segment; a read threshold number (R) of
encoded data slices to indicate a number of encoded data slices per
set to be read from storage for decoding of the data segment;
and/or a write threshold number (W) to indicate a number of encoded
data slices per set that must be accurately stored before the
encoded data segment is deemed to have been properly stored. The
dispersed storage error encoding parameters may further include
slicing information (e.g., the number of encoded data slices that
will be created for each data segment) and/or slice security
information (e.g., per encoded data slice encryption, compression,
integrity checksum, etc.).
[0038] In the present example, Cauchy Reed-Solomon has been
selected as the encoding function (a generic example is shown in
FIG. 4 and a specific example is shown in FIG. 5); the data
segmenting protocol is to divide the data object into fixed sized
data segments; and the per data segment encoding values include: a
pillar width of 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.
[0039] The computing device 12 or 16 then disperse storage error
encodes a data segment using the selected encoding function (e.g.,
Cauchy Reed-Solomon) to produce a set of encoded data slices. FIG.
4 illustrates a generic Cauchy Reed-Solomon encoding function,
which includes an encoding matrix (EM), a data matrix (DM), and a
coded matrix (CM). The size of the encoding matrix (EM) is
dependent on the pillar width number (T) and the decode threshold
number (D) of selected per data segment encoding values. To produce
the data matrix (DM), the data segment is divided into a plurality
of data blocks and the data blocks are arranged into D number of
rows with Z data blocks per row. Note that Z is a function of the
number of data blocks created from the data segment and the decode
threshold number (D). The coded matrix is produced by matrix
multiplying the data matrix by the encoding matrix.
[0040] FIG. 5 illustrates a specific example of Cauchy Reed-Solomon
encoding with a pillar number (T) of five and decode threshold
number of three. In this example, a first data segment is divided
into twelve data blocks (D1-D12). The coded matrix includes five
rows of coded data blocks, where the first row of X11-X14
corresponds to a first encoded data slice (EDS 1_1), the second row
of X21-X24 corresponds to a second encoded data slice (EDS 2_1),
the third row of X31-X34 corresponds to a third encoded data slice
(EDS 3_1), the fourth row of X41-X44 corresponds to a fourth
encoded data slice (EDS 4_1), and the fifth row of X51-X54
corresponds to a fifth encoded data slice (EDS 5_1). Note that the
second number of the EDS designation corresponds to the data
segment number.
[0041] Returning to the discussion of FIG. 3, the computing device
also creates a slice name (SN) for each encoded data slice (EDS) in
the set of encoded data slices. A typical format for a slice name
80 is shown in FIG. 6. As shown, the slice name (SN) 80 includes a
pillar number of the encoded data slice (e.g., one of 1-T), a data
segment number (e.g., one of 1-Y), a vault identifier (ID), a data
object identifier (ID), and may further include revision level
information of the encoded data slices. The slice name functions
as, at least part of, a DSN address for the encoded data slice for
storage and retrieval from the DSN memory 22.
[0042] As a result of encoding, the computing device 12 or 16
produces a plurality of sets of encoded data slices, which are
provided with their respective slice names to the storage units for
storage. As shown, the first set of encoded data slices includes
EDS 1_1 through EDS 5_1 and the first set of slice names includes
SN 1_1 through SN 5_1 and the last set of encoded data slices
includes EDS 1_Y through EDS 5_Y and the last set of slice names
includes SN 1_Y through SN 5_Y.
[0043] FIG. 7 is a schematic block diagram of an example of
dispersed storage error decoding of a data object that was
dispersed storage error encoded and stored in the example of FIG.
4. In this example, the computing device 12 or 16 retrieves from
the storage units at least the decode threshold number of encoded
data slices per data segment. As a specific example, the computing
device retrieves a read threshold number of encoded data
slices.
[0044] To recover a data segment from a decode threshold number of
encoded data slices, the computing device uses a decoding function
as shown in FIG. 8. As shown, the decoding function is essentially
an inverse of the encoding function of FIG. 4. The coded matrix
includes a decode threshold number of rows (e.g., three in this
example) and the decoding matrix in an inversion of the encoding
matrix that includes the corresponding rows of the coded matrix.
For example, if the coded matrix includes rows 1, 2, and 4, the
encoding matrix is reduced to rows 1, 2, and 4, and then inverted
to produce the decoding matrix.
[0045] FIG. 9 is a schematic block diagram of an example of a DSN
memory. In this example, the DSN memory 22 includes a client vault
#1 through client vault #n, a DSN vault and an audit object vault.
The client vaults #1-n include user memory for data that generally
allows for data to be one or more of written, read, modified and
deleted. The DSN vault includes system memory for storing data, in
which access permissions (e.g., from an access control list) may be
needed to perform certain data access requests. The audit object
vault contains sets of audit objects related to transactions
occurring in vaults of a dispersed storage network. The data in the
audit object vault may only be written or read. For example, a user
may not delete or modify any of the data in the audit object
vault.
[0046] FIG. 10 is a schematic block diagram of a slice name 80. The
slice name 80 is similar to the slice name 80 of FIG. 6, however
the data object ID is replaced by an audit object ID field. As
shown, the slice name 80 includes a pillar number field, a data
segment number field, an audit vault ID field, an audit object ID
field and a revision information field. The audit object ID field
includes a device ID field, a timestamp information field, a target
ID field, a source ID field, a transaction type ID field, and a
sequence number field. The timestamp info field includes a source
timestamp field and a received timestamp field.
[0047] The audit object ID field of the slice name 80 serves to
identify information relating to transactions occurring in a DSN.
For example, the audit object ID serves to identify a device in the
DSN, a type of transaction and timestamps. As another example, the
audit object ID serves to identify a target and a source of a
transaction. Each field may be used to search for audit objects
based on desired search options. For example, to determine a
device's use of the DSN, the device ID field may be used to only
return audit objects for a certain device. As another example, to
audit a particular timeframe, the timestamp info field can be
searched to only return audit objects for transactions occurring
during the particular timeframe.
[0048] FIG. 11 is a flowchart illustrating an example of generating
an audit object. For example, a device of a dispersed storage
network (DSN) generates the audit object. Note the audit object
includes at least one record regarding the device's use of the DSN.
The method begins at step 100, where a computing device receives an
audit information message. The audit information message indicates
activity within a dispersed storage network (DSN) and includes one
or more of a type code (e.g., read, write, delete, list, etc.), a
short message indicator, a long message indicator, a user
identifier (ID), an activity timestamp (e.g., a date and time of
execution of the activity), an activity indicator, and a source ID.
The audit information message may be received from one or more of a
user device, a DS client module, a computing device, an integrity
processing unit, a managing unit, and a storage unit.
[0049] The method continues at step 102, where the computing device
appends a received timestamp (e.g., current date and time), a
sequence number (e.g., a monotonically and consecutively increasing
number), and a source ID (e.g., identifier of machine sending audit
information message) to the audit information message to produce an
audit record. A structure of the audit record is discussed in
greater detail with reference to FIG. 12B. The method continues at
step 104, where the computing device caches the audit record in a
local memory and/or stores the audit record as encoded data slices
in a DSN memory.
[0050] The method continues at step 106, where the computing device
determines whether to process cached audit records. The
determination may be based on one or more of a number of audit
records, size of the audit records, and an elapsed time since a
last processing. For example, the computing device determines to
process cached audit records when the number of audit records is
greater than an audit record threshold. The method repeats back to
step 100 when the computing device determines not to process the
cached audit records. The method continues to step 108 when the
computing device determines to process the cached audit
records.
[0051] The method continues at step 108, where the computing device
transforms one or more cached audit records to generate an audit
object. The transforming includes determining a number of audit
records of the one or more cached audit records to include in the
audit object to produce a number of audit records entry for a
number of records field within the audit object, aggregating the
number of audit records into the audit object, generating integrity
information, aggregating the one or more cached audit records, a
number of audit records indicator, and the integrity information
into the audit object in accordance with an audit object structure.
The audit object structure is discussed in greater detail with
reference to FIG. 12A. Note the computing device may also generate
the audit object to include one or more of a certificate chain and
a digital signature.
[0052] In a specific example of generating an audit object, the
computing device generates a set of audit objects that is regarding
a write transaction to storage units of the DSN. The write
transaction includes a write sequence number, a write request
phase, a write commit phase, and a write final phase. The computing
device generates records of the set of audit objects for at least
some of: write requests of the write request phase sent from a user
device to the storage units, write responses to the write requests
sent from at least some of the storage units to the user device,
write commit requests of the write commit phase sent from the user
device to the storage units, write commit responses to the write
commit requests sent from the at least some of the storage units to
the user device, write finalize requests of the write finalize
phase sent from the user device to the storage units, and write
finalize responses to the write finalize requests sent from at
least some of the storage units to the user device.
[0053] In this example, when the computing device is a user device,
the computing device generates a first audit object of a set of
audit objects that is regarding transactions between the user
device and a first storage unit of the storage units. The first
audit object includes a first record regarding a first write
request of the write requests sent to the first storage unit, a
second record regarding a first write response of the write
responses received from the first storage unit, a third record
regarding a first write commit request of the write commit requests
sent to the first storage unit, a fourth record regarding a first
write commit response of the write commit responses received from
the first storage unit, a fifth record regarding a first write
finalize request of the write finalize requests sent to the first
storage unit and a sixth record regarding a first write finalize
response of the write finalize responses received from the first
storage unit. The computing device may further generate a second
audit object of the set of audit objects regarding transactions
between the user device and a second storage unit of the storage
units.
[0054] As another example, the computing device generates a first
audit object of a set of audit objects of a read transaction, In
this example, the first audit object is regarding transactions
between a user device and a first storage unit of the storage units
and the first audit object includes a first record regarding a
first read request of a set of read requests sent to the first
storage unit and a second record regarding a first read response of
a set of read responses received from the first storage unit. The
computing device then generates a second audit object of the set of
audit objects. Here, the second audit object is regarding
transactions between the user device and a second storage unit of
the storage units. The second audit object includes a first record
regarding a second read request of the set of read requests sent to
the second storage unit and a second record regarding a second read
response of the set of read responses received from the second
storage unit.
[0055] As yet a further example, when the computing device is a
first storage unit of the storage units, the computing device
generates a first audit object of the set of audit objects
regarding transactions between the user device and the first
storage unit. The first audit object includes a first record
regarding a first write request of the write requests received from
the user device, a second record regarding a first write response
of the write responses sent to the user device, a third record
regarding a first write commit request of the write commit received
from the user device, a fourth record regarding a first write
commit response of the write commit responses sent to the user
device, a fifth record regarding a first write finalize request of
the write finalize requests received from the user device, and a
sixth record regarding a first write finalize response of the write
finalize responses sent to the user device.
[0056] The method continues at step 110, where the computing device
dispersed storage error encodes the audit object to produce one or
more sets of encoded data slices. The method continues at step 112,
where the computing device generates a source name (e.g., common
aspects of the slice name 80) corresponding to the one or more sets
of encoded data slices. For example, the computing device generates
the source name based on at least one of an audit vault ID, an
aggregator internet protocol (IP) address, and a current timestamp.
As another example, the computing device generates a set of slice
names for the set of encoded data slices, wherein each slice name
of the set of slice name includes a pillar number section that
contains a unique pillar number for a corresponding encoded data
slice of the set of slice names and a common section that contains
audit object identifying information, wherein the common section
includes an audit vault identifier section and an audit object
identifier section, wherein the audit object identifier section
includes at least some of: a device identifier section, a timestamp
section, a target identifier section, a source identifier section,
a sequence number section, and a transaction type identifier
section.
[0057] For example, for a write transaction, the computing device
(e.g., the user device) generates a first set of slice names for
the first audit object, wherein the device identifier section
contains a user device identifier of the user device, the timestamp
section contains a timestamp that corresponds to initial time of
the write transaction, the target identifier section contains an
identifier of the first storage units, the source identifier
section contains an identifier of a source of the write transaction
(e.g., may be the user device or a different device), the sequence
number section contains the write sequence number, and the
transaction type identifier section contains a write transaction.
The computing device also generates a second set of slice names for
the second audit object, wherein the device identifier section
contains the user device identifier, the timestamp section contains
the timestamp, the target identifier section contains an identifier
of the second storage unit, the source identifier section contains
the identifier of the source of the write transaction, the sequence
number section contains the write sequence number, and the
transaction type identifier section contains the write
transaction.
[0058] In this example, the computing device (e.g., the first
storage unit) also generates a first set of slice names for the
first audit object wherein the device identifier section contains
an identifier of the first storage unit, the timestamp section
contains a timestamp that corresponds to an initial time of the
write transaction, the target identifier section contains the
identifier of the first storage unit, the source identifier section
contains an identifier of a source of the write transaction, the
sequence number section contains the write sequence number and the
transaction type identifier section contains a write
transaction.
[0059] As another example, for a read transaction, the computing
device generates a first set of slice names for the first audit
object, wherein the device identifier section contains a user
device identifier of the user device, the timestamp section
contains a timestamp that corresponds to initial time of the read
transaction, the target identifier section contains an identifier
of the first storage units, the source identifier section contains
an identifier of a source of the read transaction, the sequence
number section contains the read sequence number, and the
transaction type identifier section contains a read
transaction.
[0060] The method continues at step 114, where the computing device
outputs the one or more sets of encoded data slices to a DSN memory
utilizing the source name. For example, the computing device sends
the set of encoded data slices in accordance with the set of slice
names to a set of storage units of the DSN, wherein the set of
slice names corresponds to logical DSN addresses for the set of
encoded data slices.
[0061] FIG. 12A is a diagram illustrating an example of an audit
object file structure. The structure includes an audit object data
file 120, wherein the audit object data file 120 may be stored in a
dispersed storage network (DSN) as one or more sets of encoded data
slices, and wherein the audit object data file 120 is accessible at
a logical DSN address when stored as the one or more sets of
encoded data slices.
[0062] The audit object data file 120 includes a number of records
field 122, a set of size indicator fields size 1-R, a set of audit
record fields 1-R, and an integrity information field 124. The
number of records field 122 includes a number of records entry
indicating a number of audit records R included in the audit data
object file 120. Each such size indicator field includes a size
indicator corresponding to an audit record within the set of audit
records 1-R. For example, a size 1 field includes a size 1 entry of
300 when a size of an audit record entry of audit record field 1 is
300 bytes. The integrity information field 124 includes an
integrity information entry, wherein the integrity information
entry includes integrity information corresponding to the audit
object data file. The integrity information is described in greater
detail with reference to FIG. 12C.
[0063] FIG. 12B is a diagram illustrating an example of an audit
record file structure. The structure includes an audit record data
file 126, wherein the audit record data file 126 may be aggregated
into an audit object data file 120 for storage in a dispersed
storage network (DSN) as one or more sets of encoded data slices.
The audit record data file 126 includes a sourced timestamp field
130, a received timestamp field 132, an object timestamp field 134,
a sequence number field 136, a type code field 138, a source
identifier (ID) field 140, a user ID field 142, and a further type
information field 144. The sourced timestamp field 130 includes a
sourced timestamp entry including a date and time of when a
corresponding audit message was generated. The received timestamp
field 132 includes a received timestamp entry including a date and
time of when a corresponding audit record was generated. The object
timestamp field 134 includes an object timestamp entry including a
date and time of when a corresponding audit object data file was
generated. The sequence number field 136 includes a sequence number
entry including a monotonically and consecutively increasing
number. The type code field 138 includes a type code entry
including a type of DSN activity (e.g., a read indicator, a write
indicator, a delete indicator, a valid transaction indicator, an
invalid transaction indicator). The source ID field 140 includes a
source ID entry indicating an identifier associated with a module
or unit (e.g., machine) that sent the corresponding audit
information message. The user ID field 142 includes a user ID entry
indicating a user ID associated with the audit information message.
The further type information field 144 includes a further type
information entry including one or more of a function of a type
code (e.g., a valid slice name, an invalid user ID, a valid user
ID, an invalid slice name, etc.).
[0064] FIG. 12C is a diagram illustrating an example of integrity
information structure. The structure includes integrity information
124, wherein the integrity information 124 may be aggregated into
an audit object data file 120 for storage in a dispersed storage
network (DSN) as one or more sets of encoded data slices. The
integrity information 124 includes an aggregator identifier (ID)
field 150, a certificate chain field 152, a signature algorithm
field 154, and a signature field 156. The aggregator ID field 150
includes an aggregator ID of a module or unit that generated the
corresponding audit object file. The certificate chain field 152
includes a certificate chain entry including one or more signed
certificates of a chain structure. The chain structure includes one
or more of a signed certificate associated with the aggregator ID,
an intermediate signed certificate, and a root signed certificate.
The signature algorithm field 154 includes a signature algorithm
entry indicating one or more of an encryption algorithm identifier
associated with generating a signature, a public key, and a private
key. The signature field 156 includes a signature entry indicating
a signature over the entire audit object data file in accordance
with a signature algorithm of the signature algorithm entry and a
public and/or private key associated with the aggregator ID.
[0065] FIG. 13 is a logic flow diagram of an example of auditing
transactions in a dispersed storage network (DSN). The method
begins at step 160, where the computing device for a transaction of
the transactions identifies a set of audit objects. Note an audit
object of the set of audit objects includes at least one record
regarding the transaction and the set of audit objects is dispersed
storage error encoded to produce sets of encoded data slices. As a
specific example, the computing device selects the transaction
based on one or more of an identity of a device involved in the
transaction (e.g., looks at transactions involving a particular
storage unit), a transaction type (e.g., a write, a rebuild, etc.),
and a pseudo random selection process. As another example, when the
transaction is regarding a data access request (e.g., a write
operation, a read operation, a list request, a status report,
etc.), the computing device identifies the set of audit objects
based on encoding parameters associated with data of the data
access request. Note the set of audit objects includes a first
subset of audit objects from a device of the transaction to storage
units of the transaction and a second subset of audit objects from
at least some of the storage units to the device.
[0066] The method continues at step 162, where the computing device
determines sets of slice names for the set of audit objects based
on information regarding the transaction, wherein each slice name
of the sets of slice name includes a pillar number section and a
common object section, the pillar number section contains a unique
pillar number for a corresponding encoded data slice of the sets of
slice names, the common object section contains audit object
identifying information, wherein the common object section includes
an audit vault identifier section and an audit object identifier
section, wherein the audit object identifier section includes at
least some of: a device identifier section, a timestamp section, a
target identifier section, a source identifier section, a sequence
number section, and a transaction type identifier section.
[0067] The method continues at step 164, where the computing device
generates sets of read requests regarding the sets of encoded data
slices based on the sets of slice names. The method continues at
step 166, where the computing device sends the sets of read
requests to a set of storage units of the DSN. The method continues
at step 168, where the computing device receives and decodes a
decode threshold number of encoded data slices of each of the sets
of encoded data slices to recover the set of audit objects.
[0068] The method continues with step 170, where the computing
device analyzes the set of audit objects for DSN operational
compliance. For example, the computing device analyzes the set of
audit objects by determining that the first subset of audit objects
includes a first appropriate number of audit objects and by
determining that the second subset of audit objects includes a
second appropriate number of audit objects. When the first and
second subsets of audit objects include the first and second
appropriate numbers, respectively, the computing device determines
whether records of audit objects of the first subset of audit
objects correlate with records of audit objects of the second
subset of audit objects. When the records of audit objects of the
first subset of audit objects correlate with the records of audit
objects of the second subset of audit objects, the computing device
indicates the transaction passed the audit. When one of the records
of audit objects of the first subset of audit objects does not
correlate with a corresponding record of the records of audit
objects of the second subset of audit objects, the computing device
indicates that the transaction failed the audit.
[0069] Alternatively, when the records of audit objects of the
first subset of audit objects do not correlate with the records of
audit objects of the second subset of audit objects, the computing
device determines whether non-correlation is due to obtaining less
than the second appropriate number of audit objects of the second
subset of audit objects. When the non-correlation is due to
obtaining less than the second appropriate number of audit objects
of the second subset of audit objects, the computing device
determines whether a follow-up audit object exists, wherein the
follow-up audit object includes one or more records regarding one
or more of rebuilding, storage unit service report, storage unit
off-line report, and storage unit failure report. When the
follow-up audit object exists, the computing device indicates that
the transaction passed the audit. When the follow-up audit object
does not exist, the computing device indicates that the transaction
failed the audit.
[0070] 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`).
[0071] 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.
[0072] 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.
[0073] 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.
[0074] 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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.
[0081] 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.
* * * * *