U.S. patent application number 15/386544 was filed with the patent office on 2018-06-21 for asynchronous semi-inline deduplication.
The applicant listed for this patent is NetApp Inc.. Invention is credited to Girish Hebbale Venkata Subbaiah, Kartik Rathnakar, Alok Sharma, Mukul Sharma, Venkateswarlu Tella.
Application Number | 20180173449 15/386544 |
Document ID | / |
Family ID | 62554636 |
Filed Date | 2018-06-21 |
United States Patent
Application |
20180173449 |
Kind Code |
A1 |
Sharma; Alok ; et
al. |
June 21, 2018 |
ASYNCHRONOUS SEMI-INLINE DEDUPLICATION
Abstract
Techniques are provided for asynchronous semi-inline
deduplication. A multi-tiered storage arrangement comprises a first
storage tier, a second storage tier, etc. An in-memory change log
of data recently written to the first storage tier is evaluate to
identify a fingerprint of a data block recently written to the
first storage tier. A donor data store, comprising fingerprints of
data blocks already stored within the first storage tier, is
queried using the fingerprint. If the fingerprint is found, then
deduplication is performed for the data block to create
deduplicated data based upon a potential donor data block within
the first storage tier. The deduplicated data is moved from the
first storage tier to the second storage tier, such as in response
to a determination that the deduplicated data has not been recently
accessed. The deduplication is performed before cold data is moved
from first storage tier to second storage tier.
Inventors: |
Sharma; Alok; (Bangalore,
IN) ; Hebbale Venkata Subbaiah; Girish; (Bangalore,
IN) ; Rathnakar; Kartik; (Bengaluru, IN) ;
Tella; Venkateswarlu; (Bangalore, IN) ; Sharma;
Mukul; (Raebarel, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NetApp Inc. |
Sunnyvale |
CA |
US |
|
|
Family ID: |
62554636 |
Appl. No.: |
15/386544 |
Filed: |
December 21, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 3/0641 20130101;
G06F 3/067 20130101; G06F 3/0683 20130101; G06F 3/0685 20130101;
G06F 3/0608 20130101 |
International
Class: |
G06F 3/06 20060101
G06F003/06 |
Claims
1. A method comprising: processing an in-memory change log that
tracks newly written data blocks to a first storage tier of a
multi-tiered storage arrangement, comprising the first storage tier
and a second storage tier, to identify a fingerprint of a data
block written to the first storage tier; querying a donor data
store, comprising fingerprint entries of fingerprints of data
blocks stored within the first storage tier, using the fingerprint
to determine whether the fingerprint is found within the donor data
store; when the fingerprint is not found within the donor data
store: storing the fingerprint within the donor data store as a new
fingerprint entry; and maintaining the data block within the first
storage tier; when the fingerprint is found within a target
fingerprint entry for a potential donor data block stored within
the first storage tier: implementing deduplication for the data
block based upon the potential donor data block to create
deduplicated data within the first storage tier; and moving the
deduplicated data from the first storage tier to the second storage
tier based upon a data access metric.
2. The method of claim 1, wherein the data access metric specifies
that data of the first storage tier is to be moved from the first
storage tier to the second storage tier based upon a frequency of
access to the data falling below a threshold.
3. The method of claim 1, wherein the implementing deduplication
comprises: sending deduplication information of the data block and
the potential donor data block to a sharing engine for performing
deduplication.
4. The method of claim 1, wherein the implementing deduplication
comprises: deallocating redundant data of the data block and
incrementing a reference count of the potential donor data
block.
5. The method of claim 1, wherein the donor data store comprises a
B+tree data structure populated with the fingerprint entries.
6. The method of claim 1, comprising: allocating storage space
within the first storage tier for storing the donor data store.
7. The method of claim 1, comprising: evaluating the in-memory
change log to identify a set of data blocks written to the first
storage tier; querying the donor data store using fingerprints of
the set of data blocks to identify a subset of the set of data
blocks having fingerprints matching a set of target fingerprint
entries within the donor data store; accumulating, within a sharing
message, the subset of the set of data blocks and potential donor
data blocks referenced by the set of target fingerprint entries;
and sending the sharing message to a sharing engine for batch
deduplication of the subset of the set of data blocks.
8. The method of claim 1, comprising: performing deduplication
before cold data is moved from the first storage tier to the second
storage tier.
9. The method of claim 1, comprising: performing deduplication for
the second storage tier, comprising: evaluating a second in-memory
change log of data written to the second storage tier to identify a
set of fingerprints of data blocks written to the second storage
tier; querying a second donor data store, comprising fingerprints
entries of fingerprints of data stored within the second storage
tier, using the set of fingerprints to identify a set of potential
donor data blocks referenced by fingerprint entries within the
second donor data store matching the set of fingerprints; and
implementing deduplication for the set of data blocks within the
second storage tier based upon the set of potential donor data
blocks within the second storage tier to create second deduplicated
data within the second storage tier; and moving the second
deduplicated data from the second storage tier to a third storage
tier based upon a second data access metric.
10. The method of claim 1, comprising: collecting deduplication
statistics from a sharing engine used to perform the
deduplication.
11. The method of claim 1, wherein the in-memory change log is used
to track newly written data blocks in the first storage tier and
the in-memory change log is continually processed for querying the
donor data store for performing deduplication.
12. The method of claim 1, comprising: prefetching data block
information of the potential donor data block; and providing the
data block information to a sharing engine used to implement
deduplication.
13. The method of claim 1, wherein the deduplicated data comprises
the potential donor data block and a reference count for the
potential donor data block.
14. The method of claim 13, wherein the reference count comprises a
number of times the potential donor data block is shared due to
deduplication.
15. The method of claim 1, wherein the moving deduplicated data
comprises: sending the potential donor data block and a reference
count for the potential donor data block to the second storage
tier.
16. A non-transitory machine readable medium having stored thereon
instructions, for performing a method, which when executed by a
machine, causes the machine to: process an in-memory change log of
that tracks newly written data blocks to a first storage tier of a
multi-tiered storage arrangement, comprising the first storage tier
and a second storage tier, to identify fingerprints of data blocks
written to the first storage tier; query a donor data store,
comprising fingerprint entries of fingerprints of data blocks
stored within the first storage tier, using the fingerprints of the
data blocks to identify a subset of data blocks having fingerprints
matching target fingerprint entries within the donor data store;
accumulate, within a sharing message, the subset of data blocks and
potential donor data blocks referenced by the target fingerprint
entries; send the sharing message to a sharing engine for batch
deduplication of the subset of data blocks to create deduplicated
data within the first storage tier; and move the deduplicated data
from the first storage tier to the second storage tier based upon a
data access metric.
17. The non-transitory machine readable medium of claim 16, wherein
the instructions cause the machine to: perform deduplication for
the first storage tier according to a quality of service
policy.
18. The non-transitory machine readable medium of claim 16, wherein
the instructions cause the machine to: send a donor data block and
a reference count for the donor data block to the second storage
tier.
19. A computing device comprising: a memory containing machine
readable medium comprising machine executable code having stored
thereon instructions for performing a method; and a processor
coupled to the memory, the processor configured to execute the
machine executable code to cause the processor to: process an
in-memory change log that tracks newly written data blocks to a
first storage tier of a multi-tiered storage arrangement,
comprising the first storage tier and a second storage tier, to
identify a fingerprint of a data block written to the first storage
tier; query a donor data store, comprising fingerprint entries of
fingerprints of data blocks stored within the first storage tier,
using the fingerprint to determine that the fingerprint matches a
target fingerprint entry for a potential donor data block stored
within the first storage tier; implement deduplication for the data
block based upon the potential donor data block to create
deduplicated data within the first storage tier; and move the
deduplicated data from the first storage tier to the second storage
tier based upon a data access metric.
20. The computing device of claim 19, wherein the machine
executable code causes the processor to: perform deduplication
before cold data is moved from the first storage tier to the second
storage tier.
Description
BACKGROUND
[0001] Many storage environments may implement functionality to
improve storage efficiency. Deduplication is a technique for
storage efficiency and can be in the form of post process
deduplication and inline deduplication. For example, a storage
controller may store data within a storage device. Post-process
deduplication may be performed to remove redundant data within the
storage device after the data has already been stored within the
storage device. In particular, a deduplication scanner detects and
eliminates redundant data by scanning all recent data within the
storage device. Post-process deduplication has less of a latency
impact on a write path of the storage controller because
deduplication is not performed inline within the write path over
which the storage controller receives and processes write requests
from clients. However, additional storage must be provided within
the storage device to initially store the redundant data. Also,
undesirable on-disk fragmentation occurs as redundant data is freed
from the storage device, which can impact I/O on disk based storage
devices. Post-process deduplication requires additional system
resources to identify redundant data, such as resources used to
perform sorting, maintaining hashing tables, etc. While
post-process deduplication is being performed, regular I/O to the
storage device can be impacted, thus clients may experience
unpredictable performance degradation.
[0002] Inline deduplication can be performed in the write path in
order to remove redundant data before the redundant data reaches
the storage device. Inline deduplication does not require the
additional overhead and resources used by post-process
deduplication because redundant data is eliminated in the write
path before reaching the storage device. Inline deduplication also
eliminates redundant data without creating on-disk fragmentation
since merely unique data is written to the storage device. However,
inline deduplication can impact latency of write requests by
clients because deduplication is performed within the write path.
Thus, write requests can be completed and responded back to clients
by the storage controller in a delayed manner.
DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 is a component block diagram illustrating an example
clustered network in accordance with one or more of the provisions
set forth herein.
[0004] FIG. 2 is a component block diagram illustrating an example
data storage system in accordance with one or more of the
provisions set forth herein.
[0005] FIG. 3 is a flow chart illustrating an exemplary method of
asynchronous semi-inline deduplication.
[0006] FIG. 4A is a component block diagram illustrating an
exemplary computing device for asynchronous semi-inline
deduplication, where an in-memory change log residing in random
access memory (RAM) is evaluated.
[0007] FIG. 4B is a component block diagram illustrating an
exemplary computing device for asynchronous semi-inline
deduplication, where a donor data structure is queried.
[0008] FIG. 4C is a component block diagram illustrating an
exemplary computing device for asynchronous semi-inline
deduplication, where a donor data store is updated.
[0009] FIG. 4D is a component block diagram illustrating an
exemplary computing device for asynchronous semi-inline
deduplication, where a donor data store is queried.
[0010] FIG. 4E is a component block diagram illustrating an
exemplary computing device for asynchronous semi-inline
deduplication, where deduplication is implemented.
[0011] FIG. 4F is a component block diagram illustrating an
exemplary computing device for asynchronous semi-inline
deduplication, where cold data is moved from a first storage tier
to a second storage tier.
[0012] FIG. 5 is an example of a computer readable medium in
accordance with one or more of the provisions set forth herein.
DETAILED DESCRIPTION
[0013] Some examples of the claimed subject matter are now
described with reference to the drawings, where like reference
numerals are generally used to refer to like elements throughout.
In the following description, for purposes of explanation, numerous
specific details are set forth in order to provide an understanding
of the claimed subject matter. It may be evident, however, that the
claimed subject matter may be practiced without these specific
details. Nothing in this detailed description is admitted as prior
art.
[0014] One or more techniques and/or computing devices for
asynchronous semi-inline deduplication are provided herein. For
example, a storage environment may comprise multiple storage tiers,
such as a first storage tier (e.g., a relatively lower latency
storage tier, such as a solid state storage tier or a random access
memory (RAM) or other in-core memory storage tier), a second
storage tier (e.g., a relatively higher latency storage tier, such
as a hard disk drive storage tier), and/or other tiers such as a
third storage tier (e.g., a distributed network storage environment
such as a cloud storage network), etc. Data may be moved from one
storage tier to another storage tier when the data becomes "cold",
such as when the data is infrequently accessed or has not been
accessed for a threshold amount of time. For example, data may be
initially stored within a solid state storage tier because the data
can be quickly accessed with low latency. However, the solid state
storage tier may have limited storage capacity due to cost. Thus,
data that is frequently accessed may remain within the solid state
storage tier, while data that is infrequently accessed is moved to
cheaper storage such as a hard disk drive storage tier. It would be
advantageous to improve storage and operating efficiency of the
storage environment by performing deduplication at one storage tier
before moving data to a different storage tier, thus resulting in
reduced data transfer bandwidth by not transferring redundant data,
minimal impact upon on-disk fragmentation otherwise occurring if
deduplication is performed at a hard disk drive storage tier,
minimal impact on I/O latency otherwise occurring if deduplication
is performed inline with a write path, etc.
[0015] Performing asynchronous semi-inline deduplication for the
first storage tier, when compared to post-process deduplication,
helps reduce the latency of write operations for the first storage
tier. Performing asynchronous semi-inline deduplication reduces a
number of incoming write operations to the second storage tier
since deduplication occurs in the first storage tier. Performing
asynchronous semi-inline deduplication reduces a transport cost
between the first storage tier and the second storage tier since an
amount of data being transported is reduced. Performing
asynchronous semi-inline deduplication reduces on-disk
fragmentation for the second storage tier, and also reduces an
amount of data processed by post-process deduplication.
[0016] Accordingly, as provided herein, asynchronous semi-inline
deduplication may be performed at a first storage tier (e.g., a
solid state storage tier) before "cold" data (e.g., infrequently
accessed data) is moved from the first storage tier to a second
storage tier (e.g., a hard disk drive storage tier). The
asynchronous semi-inline deduplication may be performed outside of
a write path for the first storage tier, thus having minimal impact
on latency of the write path compared to inline deduplication. The
asynchronous semi-inline deduplication can be performed at the
first storage tier, such as the solid state storage tier, before
data is moved for the second storage tier, such as the hard disk
drive storage tier, which reduces on-disk fragmentation that would
otherwise occur if post-process deduplication was performed within
the hard disk drive storage tier. Storage allocation at the second
storage tier is conserved because there is no need for additional
allocation of storage to hold redundant data since the redundant
data is eliminated at the first storage tier. The asynchronous
semi-inline deduplication is less resource intensive than
post-procession deduplication. Data transfer bandwidth is reduced
because redundant data is not transferred to the second storage
tier.
[0017] To provide asynchronous semi-inline deduplication, FIG. 1
illustrates an embodiment of a clustered network environment 100 or
a network storage environment. It may be appreciated, however, that
the techniques, etc. described herein may be implemented within the
clustered network environment 100, a non-cluster network
environment, and/or a variety of other computing environments, such
as a desktop computing environment. That is, the instant
disclosure, including the scope of the appended claims, is not
meant to be limited to the examples provided herein. It will be
appreciated that where the same or similar components, elements,
features, items, modules, etc. are illustrated in later figures but
were previously discussed with regard to prior figures, that a
similar (e.g., redundant) discussion of the same may be omitted
when describing the subsequent figures (e.g., for purposes of
simplicity and ease of understanding).
[0018] FIG. 1 is a block diagram illustrating the clustered network
environment 100 that may implement at least some embodiments of the
techniques and/or systems described herein. The clustered network
environment 100 comprises data storage systems 102 and 104 that are
coupled over a cluster fabric 106, such as a computing network
embodied as a private Infiniband, Fibre Channel (FC), or Ethernet
network facilitating communication between the data storage systems
102 and 104 (and one or more modules, component, etc. therein, such
as, nodes 116 and 118, for example). It will be appreciated that
while two data storage systems 102 and 104 and two nodes 116 and
118 are illustrated in FIG. 1, that any suitable number of such
components is contemplated. In an example, nodes 116, 118 comprise
storage controllers (e.g., node 116 may comprise a primary or local
storage controller and node 118 may comprise a secondary or remote
storage controller) that provide client devices, such as host
devices 108, 110, with access to data stored within data storage
devices 128, 130. Similarly, unless specifically provided otherwise
herein, the same is true for other modules, elements, features,
items, etc. referenced herein and/or illustrated in the
accompanying drawings. That is, a particular number of components,
modules, elements, features, items, etc. disclosed herein is not
meant to be interpreted in a limiting manner.
[0019] It will be further appreciated that clustered networks are
not limited to any particular geographic areas and can be clustered
locally and/or remotely. Thus, in one embodiment a clustered
network can be distributed over a plurality of storage systems
and/or nodes located in a plurality of geographic locations; while
in another embodiment a clustered network can include data storage
systems (e.g., 102, 104) residing in a same geographic location
(e.g., in a single onsite rack of data storage devices).
[0020] In the illustrated example, one or more host devices 108,
110 which may comprise, for example, client devices, personal
computers (PCs), computing devices used for storage (e.g., storage
servers), and other computers or peripheral devices (e.g.,
printers), are coupled to the respective data storage systems 102,
104 by storage network connections 112, 114. Network connection may
comprise a local area network (LAN) or wide area network (WAN), for
example, that utilizes Network Attached Storage (NAS) protocols,
such as a Common Internet File System (CIFS) protocol or a Network
File System (NFS) protocol to exchange data packets, a Storage Area
Network (SAN) protocol, such as Small Computer System Interface
(SCSI) or Fiber Channel Protocol (FCP), an object protocol, such as
S3, etc. Illustratively, the host devices 108, 110 may be
general-purpose computers running applications, and may interact
with the data storage systems 102, 104 using a client/server model
for exchange of information. That is, the host device may request
data from the data storage system (e.g., data on a storage device
managed by a network storage control configured to process I/O
commands issued by the host device for the storage device), and the
data storage system may return results of the request to the host
device via one or more storage network connections 112, 114.
[0021] The nodes 116, 118 on clustered data storage systems 102,
104 can comprise network or host nodes that are interconnected as a
cluster to provide data storage and management services, such as to
an enterprise having remote locations, cloud storage (e.g., a
storage endpoint may be stored within a data cloud), etc., for
example. Such a node in the clustered network environment 100 can
be a device attached to the network as a connection point,
redistribution point or communication endpoint, for example. A node
may be capable of sending, receiving, and/or forwarding information
over a network communications channel, and could comprise any
device that meets any or all of these criteria. One example of a
node may be a data storage and management server attached to a
network, where the server can comprise a general purpose computer
or a computing device particularly configured to operate as a
server in a data storage and management system.
[0022] In an example, a first cluster of nodes such as the nodes
116, 118 (e.g., a first set of storage controllers configured to
provide access to a first storage aggregate comprising a first
logical grouping of one or more storage devices) may be located on
a first storage site. A second cluster of nodes, not illustrated,
may be located at a second storage site (e.g., a second set of
storage controllers configured to provide access to a second
storage aggregate comprising a second logical grouping of one or
more storage devices). The first cluster of nodes and the second
cluster of nodes may be configured according to a disaster recovery
configuration where a surviving cluster of nodes provides
switchover access to storage devices of a disaster cluster of nodes
in the event a disaster occurs at a disaster storage site
comprising the disaster cluster of nodes (e.g., the first cluster
of nodes provides client devices with switchover data access to
storage devices of the second storage aggregate in the event a
disaster occurs at the second storage site).
[0023] As illustrated in the clustered network environment 100,
nodes 116, 118 can comprise various functional components that
coordinate to provide distributed storage architecture for the
cluster. For example, the nodes can comprise network modules 120,
122 and data modules 124, 126. Network modules 120, 122 can be
configured to allow the nodes 116, 118 (e.g., network storage
controllers) to connect with host devices 108, 110 over the storage
network connections 112, 114, for example, allowing the host
devices 108, 110 to access data stored in the distributed storage
system. Further, the network modules 120, 122 can provide
connections with one or more other components through the cluster
fabric 106. For example, in FIG. 1, the network module 120 of node
116 can access a second data storage device by sending a request
through the data module 126 of node 118.
[0024] Data modules 124, 126 can be configured to connect one or
more data storage devices 128, 130, such as disks or arrays of
disks, flash memory, or some other form of data storage, to the
nodes 116, 118. The nodes 116, 118 can be interconnected by the
cluster fabric 106, for example, allowing respective nodes in the
cluster to access data on data storage devices 128, 130 connected
to different nodes in the cluster. Often, data modules 124, 126
communicate with the data storage devices 128, 130 according to the
SAN protocol, such as SCSI or FCP, for example. Thus, as seen from
an operating system on nodes 116, 118, the data storage devices
128, 130 can appear as locally attached to the operating system. In
this manner, different nodes 116, 118, etc. may access data blocks
through the operating system, rather than expressly requesting
abstract files.
[0025] It should be appreciated that, while the clustered network
environment 100 illustrates an equal number of network and data
modules, other embodiments may comprise a differing number of these
modules. For example, there may be a plurality of network and data
modules interconnected in a cluster that does not have a one-to-one
correspondence between the network and data modules. That is,
different nodes can have a different number of network and data
modules, and the same node can have a different number of network
modules than data modules.
[0026] Further, a host device 108, 110 can be networked with the
nodes 116, 118 in the cluster, over the storage networking
connections 112, 114. As an example, respective host devices 108,
110 that are networked to a cluster may request services (e.g.,
exchanging of information in the form of data packets) of nodes
116, 118 in the cluster, and the nodes 116, 118 can return results
of the requested services to the host devices 108, 110. In one
embodiment, the host devices 108, 110 can exchange information with
the network modules 120, 122 residing in the nodes 116, 118 (e.g.,
network hosts) in the data storage systems 102, 104.
[0027] In one embodiment, the data storage devices 128, 130
comprise volumes 132, which is an implementation of storage of
information onto disk drives or disk arrays or other storage (e.g.,
flash) as a file-system for data, for example. In an example, a
disk array can include all traditional hard drives, all flash
drives, or a combination of traditional hard drives and flash
drives. Volumes can span a portion of a disk, a collection of
disks, or portions of disks, for example, and typically define an
overall logical arrangement of file storage on disk space in the
storage system. In one embodiment a volume can comprise stored data
as one or more files that reside in a hierarchical directory
structure within the volume.
[0028] Volumes are typically configured in formats that may be
associated with particular storage systems, and respective volume
formats typically comprise features that provide functionality to
the volumes, such as providing an ability for volumes to form
clusters. For example, where a first storage system may utilize a
first format for their volumes, a second storage system may utilize
a second format for their volumes.
[0029] In the clustered network environment 100, the host devices
108, 110 can utilize the data storage systems 102, 104 to store and
retrieve data from the volumes 132. In this embodiment, for
example, the host device 108 can send data packets to the network
module 120 in the node 116 within data storage system 102. The node
116 can forward the data to the data storage device 128 using the
data module 124, where the data storage device 128 comprises volume
132A. In this way, in this example, the host device can access the
volume 132A, to store and/or retrieve data, using the data storage
system 102 connected by the storage network connection 112.
Further, in this embodiment, the host device 110 can exchange data
with the network module 122 in the node 118 within the data storage
system 104 (e.g., which may be remote from the data storage system
102). The node 118 can forward the data to the data storage device
130 using the data module 126, thereby accessing volume 1328
associated with the data storage device 130.
[0030] It may be appreciated that asynchronous semi-inline
deduplication may be implemented within the clustered network
environment 100. In an example, the data storage device 128 may be
associated with a first storage tier and the data storage device
130 may be associated with a second storage tier. Accordingly,
asynchronous semi-inline deduplication may be performed for the
data storage device 128 before "cold" data is moved from the data
storage device 128 to the data storage device 130. It may be
appreciated that asynchronous semi-inline deduplication may be
implemented for and/or between any type of computing environment,
and may be transferrable between physical devices (e.g., node 116,
node 118, a desktop computer, a tablet, a laptop, a wearable
device, a mobile device, a storage device, a server, etc.) and/or a
cloud computing environment (e.g., remote to the clustered network
environment 100).
[0031] FIG. 2 is an illustrative example of a data storage system
200 (e.g., 102, 104 in FIG. 1), providing further detail of an
embodiment of components that may implement one or more of the
techniques and/or systems described herein. The data storage system
200 comprises a node 202 (e.g., nodes 116, 118 in FIG. 1), and a
data storage device 234 (e.g., data storage devices 128, 130 in
FIG. 1). The node 202 may be a general purpose computer, for
example, or some other computing device particularly configured to
operate as a storage server. A host device 205 (e.g., 108, 110 in
FIG. 1) can be connected to the node 202 over a network 216, for
example, to provide access to files and/or other data stored on the
data storage device 234. In an example, the node 202 comprises a
storage controller that provides client devices, such as the host
device 205, with access to data stored within data storage device
234.
[0032] The data storage device 234 can comprise mass storage
devices, such as disks 224, 226, 228 of a disk array 218, 220, 222.
It will be appreciated that the techniques and systems, described
herein, are not limited by the example embodiment. For example,
disks 224, 226, 228 may comprise any type of mass storage devices,
including but not limited to magnetic disk drives, flash memory,
and any other similar media adapted to store information,
including, for example, data (D) and/or parity (P) information.
[0033] The node 202 comprises one or more processors 204, a memory
206, a network adapter 210, a cluster access adapter 212, and a
storage adapter 214 interconnected by a system bus 242. The data
storage system 200 also includes an operating system 208 installed
in the memory 206 of the node 202 that can, for example, implement
a Redundant Array of Independent (or Inexpensive) Disks (RAID)
optimization technique to optimize a reconstruction process of data
of a failed disk in an array.
[0034] The operating system 208 can also manage communications for
the data storage system, and communications between other data
storage systems that may be in a clustered network, such as
attached to a cluster fabric 215 (e.g., 106 in FIG. 1). Thus, the
node 202, such as a network storage controller, can respond to host
device requests to manage data on the data storage device 234
(e.g., or additional clustered devices) in accordance with these
host device requests. The operating system 208 can often establish
one or more file systems on the data storage system 200, where a
file system can include software code and data structures that
implement a persistent hierarchical namespace of files and
directories, for example. As an example, when a new data storage
device (not shown) is added to a clustered network system, the
operating system 208 is informed where, in an existing directory
tree, new files associated with the new data storage device are to
be stored. This is often referred to as "mounting" a file
system.
[0035] In the example data storage system 200, memory 206 can
include storage locations that are addressable by the processors
204 and adapters 210, 212, 214 for storing related software
application code and data structures. The processors 204 and
adapters 210, 212, 214 may, for example, include processing
elements and/or logic circuitry configured to execute the software
code and manipulate the data structures. The operating system 208,
portions of which are typically resident in the memory 206 and
executed by the processing elements, functionally organizes the
storage system by, among other things, invoking storage operations
in support of a file service implemented by the storage system. It
will be apparent to those skilled in the art that other processing
and memory mechanisms, including various computer readable media,
may be used for storing and/or executing application instructions
pertaining to the techniques described herein. For example, the
operating system can also utilize one or more control files (not
shown) to aid in the provisioning of virtual machines.
[0036] The network adapter 210 includes the mechanical, electrical
and signaling circuitry needed to connect the data storage system
200 to a host device 205 over a network 216, which may comprise,
among other things, a point-to-point connection or a shared medium,
such as a local area network. The host device 205 (e.g., 108, 110
of FIG. 1) may be a general-purpose computer configured to execute
applications. As described above, the host device 205 may interact
with the data storage system 200 in accordance with a client/host
model of information delivery.
[0037] The storage adapter 214 cooperates with the operating system
208 executing on the node 202 to access information requested by
the host device 205 (e.g., access data on a storage device managed
by a network storage controller). The information may be stored on
any type of attached array of writeable media such as magnetic disk
drives, flash memory, and/or any other similar media adapted to
store information. In the example data storage system 200, the
information can be stored in data blocks on the disks 224, 226,
228. The storage adapter 214 can include input/output (I/O)
interface circuitry that couples to the disks over an I/O
interconnect arrangement, such as a storage area network (SAN)
protocol (e.g., Small Computer System Interface (SCSI), iSCSI,
hyperSCSI, Fiber Channel Protocol (FCP)). The information is
retrieved by the storage adapter 214 and, if necessary, processed
by the one or more processors 204 (or the storage adapter 214
itself) prior to being forwarded over the system bus 242 to the
network adapter 210 (and/or the cluster access adapter 212 if
sending to another node in the cluster) where the information is
formatted into a data packet and returned to the host device 205
over the network 216 (and/or returned to another node attached to
the cluster over the cluster fabric 215).
[0038] In one embodiment, storage of information on disk arrays
218, 220, 222 can be implemented as one or more storage volumes
230, 232 that are comprised of a cluster of disks 224, 226, 228
defining an overall logical arrangement of disk space. The disks
224, 226, 228 that comprise one or more volumes are typically
organized as one or more groups of RAIDs. As an example, volume 230
comprises an aggregate of disk arrays 218 and 220, which comprise
the cluster of disks 224 and 226.
[0039] In one embodiment, to facilitate access to disks 224, 226,
228, the operating system 208 may implement a file system (e.g.,
write anywhere file system) that logically organizes the
information as a hierarchical structure of directories and files on
the disks. In this embodiment, respective files may be implemented
as a set of disk blocks configured to store information, whereas
directories may be implemented as specially formatted files in
which information about other files and directories are stored.
[0040] Whatever the underlying physical configuration within this
data storage system 200, data can be stored as files within
physical and/or virtual volumes, which can be associated with
respective volume identifiers, such as file system identifiers
(FSIDs), which can be 32-bits in length in one example.
[0041] A physical volume corresponds to at least a portion of
physical storage devices whose address, addressable space,
location, etc. doesn't change, such as at least some of one or more
data storage devices 234 (e.g., a Redundant Array of Independent
(or Inexpensive) Disks (RAID system)). Typically the location of
the physical volume doesn't change in that the (range of)
address(es) used to access it generally remains constant.
[0042] A virtual volume, in contrast, is stored over an aggregate
of disparate portions of different physical storage devices. The
virtual volume may be a collection of different available portions
of different physical storage device locations, such as some
available space from each of the disks 224, 226, and/or 228. It
will be appreciated that since a virtual volume is not "tied" to
any one particular storage device, a virtual volume can be said to
include a layer of abstraction or virtualization, which allows it
to be resized and/or flexible in some regards.
[0043] Further, a virtual volume can include one or more logical
unit numbers (LUNs) 238, directories 236, Qtrees 235, and files
240. Among other things, these features, but more particularly
LUNS, allow the disparate memory locations within which data is
stored to be identified, for example, and grouped as data storage
unit. As such, the LUNs 238 may be characterized as constituting a
virtual disk or drive upon which data within the virtual volume is
stored within the aggregate. For example, LUNs are often referred
to as virtual drives, such that they emulate a hard drive from a
general purpose computer, while they actually comprise data blocks
stored in various parts of a volume.
[0044] In one embodiment, one or more data storage devices 234 can
have one or more physical ports, wherein each physical port can be
assigned a target address (e.g., SCSI target address). To represent
respective volumes stored on a data storage device, a target
address on the data storage device can be used to identify one or
more LUNs 238. Thus, for example, when the node 202 connects to a
volume 230, 232 through the storage adapter 214, a connection
between the node 202 and the one or more LUNs 238 underlying the
volume is created.
[0045] In one embodiment, respective target addresses can identify
multiple LUNs, such that a target address can represent multiple
volumes. The I/O interface, which can be implemented as circuitry
and/or software in the storage adapter 214 or as executable code
residing in memory 206 and executed by the processors 204, for
example, can connect to volume 230 by using one or more addresses
that identify the one or more LUNs 238.
[0046] It may be appreciated that asynchronous semi-inline
deduplication may be implemented for the data storage system 200.
In an example, the one or more data storage devices 234 may be
associated with a first storage tier. Asynchronous semi-inline
deduplication may be performed for the one or more data storage
devices 234 before "cold" data is moved from the one or more data
storage devices 234 to a second storage tier. It may be appreciated
that asynchronous semi-inline deduplication may be implemented for
and/or between any type of computing environment, and may be
transferrable between physical devices (e.g., node 202, host device
205, a desktop computer, a tablet, a laptop, a wearable device, a
mobile device, a storage device, a server, etc.) and/or a cloud
computing environment (e.g., remote to the node 202 and/or the host
device 205).
[0047] One embodiment of asynchronous semi-inline deduplication is
illustrated by an exemplary method 300 of FIG. 3. In an example, a
storage environment (e.g., a multi-tiered storage arrangement) may
comprise multiple tiers of storage, such as tiers of different
types of storage (e.g., a lower latency tier, a higher latency
tier, etc.). For example, the storage environment comprises a first
storage tier (e.g., a solid state storage tier), a second storage
tier (e.g., a hard disk drive storage tier), a third storage tier,
etc. Data may be initially stored within the first storage tier.
When the data becomes "cold" due to infrequent access, the "cold"
data may be moved from the first storage tier to the second storage
tier. In this way, frequently accessed "hot" data may remain within
the first storage tier for quick access. An in-memory change log,
residing in random access memory (RAM), is maintained to keep track
of fingerprints of the new data blocks written to the first storage
tier. In-memory fingerprints are used to find redundant data from
the donor database and perform deduplication. The whole operation
of deduplication for the new writes in first storage tier will be
performed before "cold" data is identified and moved to the second
storage tier. The asynchronous semi inline deduplication uses the
in-memory change log, which is in contrast to post process
deduplication that maintains a persistent change log and will be
processed through scheduled or manual operation. The asynchronous
semi-inline will process the in-memory change log continuously as
long as there is incoming new writes to first storage tier.
[0048] At 302, the in-memory change log, which tracks the new data
written to the first storage tier, is processed. The in-memory
change log contains fingerprint information of a new data block
that may include block information in a file system and checksum
computed on the data of the new data block. In an example, a set of
data blocks written to the first storage tier may be identified
from the in-memory change log, such as for batch deduplication
processing. For example, a data block (1), a data block (2), and a
data block (3) may have been written to the first storage tier. In
this way, the in-memory change log may identify data blocks
recently written to the first storage tier.
[0049] Storage space within the first storage tier may be allocated
to store a donor data store. In an example, the donor data store
may comprise a B+ tree or any other data structure. The donor data
store may comprise fingerprint entries used to store fingerprints
of data blocks already stored within the first storage tier (e.g.,
fingerprints of all unique data blocks of the first storage tier).
In this way, the donor data store may be used to identify redundant
data within the first storage tier by matching fingerprints of
newly written data blocks, identified from the in-memory change
log, to fingerprints of already stored data blocks within the first
storage tier.
[0050] At 304, the donor data store may be queried using the
fingerprint of in-memory change-logged blocks to determine whether
a match is found within the donor data store. If a match is found,
then a donor data block, comprising the same data as the data
block, already exists within the first storage tier and thus the
data block comprises duplicate/redundant. Otherwise if the
fingerprint is not found within the donor data store, then the data
block comprises unique/non-redundant data not already stored within
the first storage tier. The donor data store may comprise
fingerprint entries of fingerprints of data blocks already stored
within the first storage tier before entries within the in-memory
change log were recorded. For example, newly written data blocks,
written to the first storage tier, may be recorded within the
in-memory change log, and the donor data store may comprise
fingerprint entries of data blocks (potential donor data blocks).
In this way, the in-memory change log and the donor data store may
be compared to determine whether newly written data blocks comprise
the same data as potential donor data blocks already existing
within the first storage tier.
[0051] In an example where a set of data blocks were identified
from the in-memory change log for batch deduplication, the donor
data store may be queried using fingerprints of the set of data
blocks to identify a subset of the set of data blocks having
fingerprints matching a set of target fingerprint entries within
the donor data store. For example, fingerprints of the data block
(1), the data block (2), and the data block (3) may be used to
query the donor data store. The fingerprints of the data block (1)
and the data block (3) may be found within the donor data store.
However, a fingerprint of the data block (2) may not be found
within the donor data store. Accordingly, the subset of the set of
data blocks may comprise the data block (1) and the data block (3)
having fingerprints that match a set of target fingerprint entries
within the donor data store. Thus, the data block (2) may comprise
unique data not already stored within the first storage tier, while
the data block (1) and the data block (3) may comprise
duplicate/redundant data already stored within the first storage
tier by potential donor data blocks.
[0052] At 306, when the fingerprint is not found within the donor
data store (e.g., the fingerprint of data block (2) does not match
any fingerprint entries within the donor data store), the
fingerprint is added to the donor data store as a new fingerprint
entry. The new fingerprint entry provides a new indication that the
data block (2) is now stored within the first storage tier and is
available as a potential donor block for subsequent identification
of duplicate data. The data block (2) may be maintained within the
first storage tier since the data block (2) comprises unique
data.
[0053] At 308, when the fingerprint is found within a target
fingerprint entry for a potential donor data block stored within
the first storage tier (e.g., the fingerprint of data block (1)
matches a target fingerprint entry for a data block already stored
within the first storage tier, thus indicating that the data block
(1) comprises the same/redundant data as the data block),
deduplication may be implemented for the data block based upon the
potential donor data block to create deduplicated data within the
first storage tier. In an example, deduplication information of the
data block (1) and the potential donor data block (e.g., a
deduplication pair) may be sent to a sharing engine for performing
deduplication (e.g., a block sharing state machine may perform
deduplication so that the data block (1) and the potential donor
data block share data as opposed to storing two separate and
redundant instances of the same data). In an example, data block
information of the potential donor data block may be pre-fetched
and provided to the sharing engine for use in implementing
deduplication. In an example, data of the data block (1) may be
deallocated from the first storage tier (e.g., an instance of the
data stored by the data block (1) may be deallocated and merely the
original instance of the potential donor data block may be
retained, and thus the data block (1) may merely reference/share
the data of the potential donor data block). In an example, the
data block (1) and the potential donor data block may now share the
same data as a result of deduplication. In an example, a result of
the deduplication may be referred to as deduplicated data, which
comprises the donor data block and a reference count for the donor
data block that specifies a number of times the donor data block is
shared due to deduplication. The deduplicated data does not
comprise redundant data that was deallocated from
deduplication.
[0054] By performing asynchronous semi-inline deduplication, less
data may be processed by any subsequent offline deduplication
functionality for a later storage tier (e.g., the second storage
tier) in order to avoid wasting resources on redundant
deduplication. In an example, the deduplication may be performed
according to a quality of service policy (e.g., deduplication may
be prioritized and/or performed in a manner that does not increase
latency of client I/O operations above a threshold amount specified
by the quality of service policy). In an example, deduplication
statistics may be collected for the sharing engine (e.g., an amount
of time to perform deduplication, an amount of detected duplicate
data, an impact on client I/O latency, storage savings from
deduplication, etc.).
[0055] In an example where the subset of the set of data blocks
having fingerprints matching target fingerprint entries was
identified (e.g., data block (1) and data block (3)) for batch
deduplication, the subset of the set of data blocks and the
potential donor data blocks referenced by the set of target
fingerprints (e.g., deduplication pairs) may be accumulated into a
sharing message. Based upon a trigger (e.g., a threshold amount of
deduplication pairs being stored within the sharing message), the
sharing message may be sent to the sharing engine for batch
deduplication of the subset of the set of data blocks. In this way,
deduplicated data is produced.
[0056] At 310, the deduplicated data (e.g., the donor data block
and a reference count of the donor data block, but not redundant
data of data blocks that share the data of the donor data block,
which were deallocated due to deduplication) may be moved from the
first storage tier to the second storage tier based upon a data
access metric or any other metric (e.g., an indication that the
deduplicated data, such as the data shared by the data block (1)
and the potential donor data block for the data block (1), is being
infrequently accessed). For example, the data access metric may
specify that data of the first storage tier is to be moved from the
first storage tier to the second storage tier based upon a
frequency of access to the data falling below a threshold or based
upon a threshold amount of time occurring since a last access to
the data. Here the semi inline deduplication is performed before
data is moved to the second storage tier, which can reduce on-disk
fragmentation otherwise resulting from performing post process
deduplication at the second storage tier such as a hard disk drive
storage tier. Also, performing such semi inline deduplication
reduces data transfer bandwidth otherwise wasted in transmitting
redundant data between the storage tiers.
[0057] It may be appreciated that asynchronous semi-inline
deduplication may be performed for multiple tiers of the
multi-tiered storage arrangement (e.g., within a RAM/memory storage
tier, within a solid state drive storage tier, within a hard disk
drive storage tier, within a cloud storage tier, etc.) before data
is moved between tiers. For example, a second in-memory change log
of data written to the second storage tier may be evaluated to
identify a set of fingerprints of data blocks written to the second
storage tier. The second in-memory change log may be stored within
the second storage tier. A second donor data store, comprising
fingerprint entries of fingerprints of data stored within the
second storage tier, may be queried using the set of fingerprints
to identify a set of potential donor data blocks, within the second
storage tier, that are referenced by fingerprint entries within the
second donor data store matching the set of fingerprints.
Deduplication may be implemented for the set of data blocks within
the second storage tier based upon the set of potential donor data
blocks within the second storage tier (e.g., the set of data blocks
and the set of potential donor data blocks may now share the same
data) to create second deduplicated data within the second storage
tier. The second deduplicated data may be moved from the second
storage tier to a third storage tier based upon a second data
access metric.
[0058] FIGS. 4A-4F illustrate examples of a system for asynchronous
semi-inline deduplication. FIG. 4A illustrates a multi-tiered
storage arrangement comprising a first storage tier 404, a second
storage tier 410, and/or other storage tiers not illustrated. The
first storage tier 404 may be configured to store data written to
the first storage tier as stored data 406. For example, the first
storage tier 404 may store a data block (A), a data block (B), a
data block (C), a data block (X), a data block (Z), and/or other
data blocks at a first point in time (e.g., since a last time an
in-memory change log 402 was processed and a donor data store 408
was updated). The donor data store 408 is used to store
fingerprints of data blocks (e.g., potential donor data blocks)
already stored within the first storage tier 404 (e.g., data blocks
stored before the last time the in-memory change log 402 was
processed and the donor data store 408 was updated).
[0059] As new data blocks are written to the first storage tier
404, the in-memory change log 402 is maintained to track the newly
written data blocks to the first storage tier 404. For example, a
new data block (C), a new data block (D), and/or other new data
blocks may be written to the first storage tier 404. In this way,
the in-memory change log 402 may be evaluated (e.g., at a second
point in time, which may be triggered based upon an amount of time
elapsing, a threshold amount of new data being written to the first
storage tier 404, the in-memory change log 402 becoming full, etc.)
to identify the newly written data, such as the new data block (C)
and the new data block (D), which may be evaluated for asynchronous
semi-inline deduplication.
[0060] FIG. 4B illustrates the donor data store 408 being queried
412 using a fingerprint (D) of the new data block (D) to determine
whether any fingerprint entries within the donor data store 408
match the fingerprint (D). For example, the fingerprint (D) may not
be found within the donor data store 408. Accordingly, the new data
block (D) may comprise unique data not already stored within the
first storage tier 404.
[0061] FIG. 4C illustrates the donor data store 408 being updated
420 based upon the new data block (D) comprising unique data not
already stored within the first storage tier 404. In particular, a
fingerprint entry (D) 422 is inserted into the donor data store
408. The fingerprint entry (D) 422 comprises the fingerprint (D) of
the new data block (D). In this way, subsequent deduplication may
take into account the new data block (D) for determining whether
subsequently written data matches data of the new data block (D),
and thus should be deduplicated. The data of new data block (D) may
be retained within the first storage tier 404 because the data of
new data block (D) is unique.
[0062] FIG. 4D illustrates a second query 430 being performed upon
the donor data store 408. In particular, the donor data store 408
is queried using a fingerprint (C) of the new data block (C) 436 to
determine whether any fingerprint entries within the donor data
store 408 match the fingerprint (C). For example, the fingerprint
(C) may match a fingerprint entry (C) 432 within the donor data
store 408, which indicates that data of the new data block (C) 436
was already stored within the first storage tier 404 as data block
(C) 434 (e.g., a potential donor data block for the new data block
(C) 436) to which the fingerprint (C) refers. In this way, the new
data block (C) 436 may be identified as comprising
redundant/duplicate data already stored within the first storage
tier 404 by the data block (C) 434.
[0063] FIG. 4E illustrates deduplication 440 being implemented upon
the stored data 406 of the first storage tier 404. For example,
data of the new data block (C) 436 may be deallocated by a sharing
engine that implements the deduplication 440. The sharing engine
may perform the deduplication 440 such that the data block (C) 434
and the new data block (C) 436 reference the same data instead of
referencing separate instances of the same data. In this way, the
first storage tier 404 now comprises deduplicated data as a result
of the deduplication 440.
[0064] FIG. 4F illustrates a data access metric 450 being used to
identify "cold" data to be moved 452 from the first storage tier
404 to the second storage tier 410. The data access metric 450 may
specify that if data has not been recently accessed for more than a
specific amount of time and/or a frequency of access to the data
has fallen below a threshold, then the data is identified as "cold"
data that is to be moved 452 to the second storage tier 410. In an
example, the deduplicated data shared by the data block (C) 434 and
the new data block (C) 436 is identified as "cold" deduplicated
data that is moved 452 to the second storage tier 410. When the
deduplicated data (e.g., a donor data block and a reference count
of the donor data block) is moved 452, merely a single instance of
data block (C) 434 is moved to the second storage tier 410 along
with a reference count of the number of times the data of data
block (C) 434 is shared from deduplication.
[0065] Still another embodiment involves a computer-readable medium
comprising processor-executable instructions configured to
implement one or more of the techniques presented herein. An
example embodiment of a computer-readable medium or a
computer-readable device that is devised in these ways is
illustrated in FIG. 5, wherein the implementation 500 comprises a
computer-readable medium 508, such as a compact disc-recordable
(CD-R), a digital versatile disc-recordable (DVD-R), flash drive, a
platter of a hard disk drive, etc., on which is encoded
computer-readable data 506. This computer-readable data 506, such
as binary data comprising at least one of a zero or a one, in turn
comprises a processor-executable computer instructions 504
configured to operate according to one or more of the principles
set forth herein. In some embodiments, the processor-executable
computer instructions 504 are configured to perform a method 502,
such as at least some of the exemplary method 300 of FIG. 3, for
example. In some embodiments, the processor-executable computer
instructions 504 are configured to implement a system, such as at
least some of the exemplary system 400 of FIGS. 4A-4F, for example.
Many such computer-readable media are contemplated to operate in
accordance with the techniques presented herein.
[0066] It will be appreciated that processes, architectures and/or
procedures described herein can be implemented in hardware,
firmware and/or software. It will also be appreciated that the
provisions set forth herein may apply to any type of
special-purpose computer (e.g., file host, storage server and/or
storage serving appliance) and/or general-purpose computer,
including a standalone computer or portion thereof, embodied as or
including a storage system. Moreover, the teachings herein can be
configured to a variety of storage system architectures including,
but not limited to, a network-attached storage environment and/or a
storage area network and disk assembly directly attached to a
client or host computer. Storage system should therefore be taken
broadly to include such arrangements in addition to any subsystems
configured to perform a storage function and associated with other
equipment or systems.
[0067] In some embodiments, methods described and/or illustrated in
this disclosure may be realized in whole or in part on
computer-readable media. Computer readable media can include
processor-executable instructions configured to implement one or
more of the methods presented herein, and may include any mechanism
for storing this data that can be thereafter read by a computer
system. Examples of computer readable media include (hard) drives
(e.g., accessible via network attached storage (NAS)), Storage Area
Networks (SAN), volatile and non-volatile memory, such as read-only
memory (ROM), random-access memory (RAM), electrically erasable
programmable read-only memory (EEPROM) and/or flash memory, compact
disk read only memory (CD-ROM)s, CD-Rs, compact disk re-writeable
(CD-RW)s, DVDs, cassettes, magnetic tape, magnetic disk storage,
optical or non-optical data storage devices and/or any other medium
which can be used to store data.
[0068] Although the subject matter has been described in language
specific to structural features or methodological acts, it is to be
understood that the subject matter defined in the appended claims
is not necessarily limited to the specific features or acts
described above. Rather, the specific features and acts described
above are disclosed as example forms of implementing at least some
of the claims.
[0069] Various operations of embodiments are provided herein. The
order in which some or all of the operations are described should
not be construed to imply that these operations are necessarily
order dependent. Alternative ordering will be appreciated given the
benefit of this description. Further, it will be understood that
not all operations are necessarily present in each embodiment
provided herein. Also, it will be understood that not all
operations are necessary in some embodiments.
[0070] Furthermore, the claimed subject matter is implemented as a
method, apparatus, or article of manufacture using standard
application or engineering techniques to produce software,
firmware, hardware, or any combination thereof to control a
computer to implement the disclosed subject matter. The term
"article of manufacture" as used herein is intended to encompass a
computer application accessible from any computer-readable device,
carrier, or media. Of course, many modifications may be made to
this configuration without departing from the scope or spirit of
the claimed subject matter.
[0071] As used in this application, the terms "component",
"module," "system", "interface", and the like are generally
intended to refer to a computer-related entity, either hardware, a
combination of hardware and software, software, or software in
execution. For example, a component includes a process running on a
processor, a processor, an object, an executable, a thread of
execution, an application, or a computer. By way of illustration,
both an application running on a controller and the controller can
be a component. One or more components residing within a process or
thread of execution and a component may be localized on one
computer or distributed between two or more computers.
[0072] Moreover, "exemplary" is used herein to mean serving as an
example, instance, illustration, etc., and not necessarily as
advantageous. As used in this application, "or" is intended to mean
an inclusive "or" rather than an exclusive "or". In addition, "a"
and "an" as used in this application are generally be construed to
mean "one or more" unless specified otherwise or clear from context
to be directed to a singular form. Also, at least one of A and B
and/or the like generally means A or B and/or both A and B.
Furthermore, to the extent that "includes", "having", "has",
"with", or variants thereof are used, such terms are intended to be
inclusive in a manner similar to the term "comprising".
[0073] Many modifications may be made to the instant disclosure
without departing from the scope or spirit of the claimed subject
matter. Unless specified otherwise, "first," "second," or the like
are not intended to imply a temporal aspect, a spatial aspect, an
ordering, etc. Rather, such terms are merely used as identifiers,
names, etc. for features, elements, items, etc. For example, a
first set of information and a second set of information generally
correspond to set of information A and set of information B or two
different or two identical sets of information or the same set of
information.
[0074] Also, although the disclosure has been shown and described
with respect to one or more implementations, equivalent alterations
and modifications will occur to others skilled in the art based
upon a reading and understanding of this specification and the
annexed drawings. The disclosure includes all such modifications
and alterations and is limited only by the scope of the following
claims. In particular regard to the various functions performed by
the above described components (e.g., elements, resources, etc.),
the terms used to describe such components are intended to
correspond, unless otherwise indicated, to any component which
performs the specified function of the described component (e.g.,
that is functionally equivalent), even though not structurally
equivalent to the disclosed structure. In addition, while a
particular feature of the disclosure may have been disclosed with
respect to only one of several implementations, such feature may be
combined with one or more other features of the other
implementations as may be desired and advantageous for any given or
particular application.
* * * * *