U.S. patent application number 17/215218 was filed with the patent office on 2021-07-15 for method and apparatus for an enhanced data pipeline.
This patent application is currently assigned to AT&T Intellectual Property I, L.P.. The applicant listed for this patent is AT&T Intellectual Property I, L.P.. Invention is credited to Shyam Parekh, Mostafa Tofighbakhsh.
Application Number | 20210219004 17/215218 |
Document ID | / |
Family ID | 1000005481777 |
Filed Date | 2021-07-15 |
United States Patent
Application |
20210219004 |
Kind Code |
A1 |
Parekh; Shyam ; et
al. |
July 15, 2021 |
METHOD AND APPARATUS FOR AN ENHANCED DATA PIPELINE
Abstract
Aspects of the subject disclosure may include, for example,
monitoring a plurality of data packets in a network by analyzing
header information to detect an indicator associated with a first
data object type, identifying a first data object in a first set of
data packets according to the monitoring and according to a model
relating to the first data object type, instantiating an
intelligent router at the network, switching the first set of data
packets to the intelligent router to cause the intelligent router
to generate an extracted first data object from the first set of
data packets, transmitting the extracted first data object to a
client device via the data pipeline of the network responsive to a
request from the client device for the first data object, and
decommissioning the intelligent router after the transmitting the
extracted first data object. Other embodiments are disclosed.
Inventors: |
Parekh; Shyam; (Orinda,
CA) ; Tofighbakhsh; Mostafa; (Cupertino, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
AT&T Intellectual Property I, L.P. |
Atlanta |
GA |
US |
|
|
Assignee: |
AT&T Intellectual Property I,
L.P.
Atlanta
GA
|
Family ID: |
1000005481777 |
Appl. No.: |
17/215218 |
Filed: |
March 29, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
16511654 |
Jul 15, 2019 |
10992968 |
|
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17215218 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 3/08 20130101; G06K
9/6256 20130101; H04N 21/2353 20130101; H04N 21/237 20130101; H04L
69/22 20130101; H04N 21/23418 20130101 |
International
Class: |
H04N 21/234 20060101
H04N021/234; H04L 29/06 20060101 H04L029/06; G06N 3/08 20060101
G06N003/08; H04N 21/237 20060101 H04N021/237; G06K 9/62 20060101
G06K009/62; H04N 21/235 20060101 H04N021/235 |
Claims
1. A device, comprising: a processing system including a processor;
and a memory that stores executable instructions that, when
executed by the processing system, facilitate performance of
operations, the operations comprising: monitoring a plurality of
data packets in a data pipeline of a network, wherein the
monitoring includes analyzing header information associated with
the plurality of data packets to detect an indicator associated
with a first data object type; identifying a first data object in a
first set of data packets of the plurality of data packets
according to the monitoring of the plurality of data packets and
according to a model relating to the first data object type,
wherein the model is trained based on associations between data
objects of the first data object type and analyzed header
information associated with other data packets; instantiating an
intelligent router at the data pipeline of the network responsive
to the identifying the first data object, wherein the intelligent
router includes a packet monitoring function; switching the first
set of data packets to the intelligent router to cause the
intelligent router to generate an extracted first data object from
the first set of data packets, responsive to the instantiating the
intelligent router; transmitting the extracted first data object to
a client device via the data pipeline of the network responsive to
a request from the client device for the first data object; and
decommissioning the intelligent router after the transmitting the
extracted first data object.
2. The device of claim 1, wherein the model relating to the first
data object type includes an association of the first data object
type with a sequence of header information, and wherein the model
relating to the first data object type is trained via a machine
learning algorithm.
3. The device of claim 1, wherein the monitoring the plurality of
data packets includes sampling one or more data packets of the
plurality of data packets.
4. The device of claim 1, wherein the indicator associated with the
first data object type includes metadata.
5. The device of claim 1, wherein the identifying the first data
object comprises identifying, in header information associated with
the first set of data packets, metadata associated with the first
data object type.
6. The device of claim 1, wherein the intelligent router further
includes a solid-state memory device and a graphical processing
unit.
7. The device of claim 6, wherein the solid-state memory device
stores the extracted first data object, organizes the extracted
first data object, schedules delivery of the extracted first data
object, or any combination thereof.
8. The device of claim 6, wherein the graphical processing unit
performs graphical functions associated with the extracted first
data object.
9. The device of claim 1, wherein the packet monitoring function is
configured to analyze at least a portion of payload information
associated with the first data object, identifies metadata
information associated with the first data object, determines the
first data object type associated with the first data object, or
any combination thereof.
10. The device of claim 1, wherein the transmitting the extracted
first data object comprises transmitting the extracted first data
object from the intelligent router to the client device.
11. A method, comprising: monitoring, by a processing system
including a processor, a plurality of data packets a network,
wherein the monitoring includes analyzing header information
associated with the plurality of data packets to detect an
indicator associated with a first data object type; detecting, by
the processing system, a first data object in a first set of data
packets of the plurality of data packets according to the
monitoring of the plurality of data packets and according to a
machine learning model relating to the first data object type,
wherein the machine learning model is trained based on associations
between data objects of the first data object type and analyzed
header information of other data packets; instantiating, by the
processing system, an intelligent router in the network responsive
to the detecting the first data object, wherein the intelligent
router includes a packet monitoring function; providing, by the
processing system, the first set of data packets to the intelligent
router to cause the intelligent router to generate an extracted
first data object from the first set of data packets, responsive to
the instantiating the intelligent router; transmitting, by the
processing system, the extracted first data object to a client
device via the network responsive to a request from the client
device for the first data object; and decommissioning, by the
processing system, the intelligent router after the transmitting
the extracted first data object.
12. The method of claim 11, wherein the machine learning model
relating to the first data object type includes an association of
the first data object type with a sequence of header
information.
13. The method of claim 11, wherein the intelligent router stores
the extracted first data object as a cached first data object.
14. The method of claim 11, wherein the transmitting the extracted
first data object comprises transmitting the extracted first data
object from the intelligent router to the client device.
15. The method of claim 11, wherein the intelligent router further
includes a solid-state memory device and a graphical processing
unit.
16. A non-transitory machine-readable medium, comprising executable
instructions that, when executed by a processing system including a
processor, facilitate performance of operations, the operations
comprising: analyzing header information of a plurality of data
packets in a data pipeline of a network to detect an indicator
associated with a first data object type; identifying a first data
object in a first set of data packets of the plurality of data
packets according to the analyzing of the header information and
according to a model, wherein the model is trained based on
associations between data objects of the first data object type and
analyzed header information of other data packets; instantiating a
router at the data pipeline of the network responsive to the
identifying the first data object, wherein the router includes a
packet monitoring function; switching the first set of data packets
to the router to enable the router to derive an extracted first
data object from the first set of data packets after the
instantiating the router; causing the extracted first data object
to be provided to a client device responsive to receiving a request
from the client device for the first data object; and
decommissioning the router responsive to the causing the extracted
first data object to be provided to the client device.
17. The non-transitory machine-readable medium of claim 16, wherein
the model includes an association of the first data object type
with a sequence of header information, and wherein the model is
trained via a machine learning algorithm.
18. The non-transitory machine-readable medium of claim 16, wherein
the router stores the extracted first data object as a cached first
data object.
19. The non-transitory machine-readable medium of claim 16, wherein
the causing the extracted first data object to be provided to the
client device comprises causing the extracted first data object to
be provided from the router to the client device.
20. The non-transitory machine-readable medium of claim 16, wherein
the router further includes a solid-state memory device and a
graphical processing unit.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] This application is a continuation of and claims priority to
U.S. application Ser. No. 16/511,654, filed Jul. 15, 2019. The
contents of the foregoing are hereby incorporated by reference into
this application as if set forth herein in full.
FIELD OF THE DISCLOSURE
[0002] The subject disclosure relates to a method and apparatus for
media data pipeline.
BACKGROUND
[0003] Modern telecommunications systems provide consumers with
telephony capabilities while accessing a large variety of content.
Consumers are no longer bound to specific locations when
communicating with others or when enjoying multimedia content or
accessing the varied resources available via the Internet. Network
capabilities have expanded and have created additional
interconnections and new opportunities for using mobile
communication devices in a variety of situations. Intelligent
devices offer new means for experiencing network interactions in
ways that anticipate consumer desires and provide solutions to
problems.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] Reference will now be made to the accompanying drawings,
which are not necessarily drawn to scale, and wherein:
[0005] FIG. 1 is a block diagram illustrating an exemplary,
non-limiting embodiment of a communications network in accordance
with various aspects described herein.
[0006] FIG. 2A-2C is a block diagram illustrating an example,
non-limiting embodiment of a system functioning within the
communication network of FIG. 1 in accordance with various aspects
described herein.
[0007] FIG. 2D depicts an illustrative embodiment of a method in
accordance with various aspects described herein.
[0008] FIG. 3 is a block diagram illustrating an example,
non-limiting embodiment of a virtualized communication network in
accordance with various aspects described herein.
[0009] FIG. 4 is a block diagram of an example, non-limiting
embodiment of a computing environment in accordance with various
aspects described herein.
[0010] FIG. 5 is a block diagram of an example, non-limiting
embodiment of a mobile network platform in accordance with various
aspects described herein.
[0011] FIG. 6 is a block diagram of an example, non-limiting
embodiment of a communication device in accordance with various
aspects described herein.
DETAILED DESCRIPTION
[0012] The subject disclosure describes, among other things,
illustrative embodiments for processing data in a data pipeline. In
one or more embodiments, a system can monitor a data pipeline to
identify data objects in data packets carried by the data pipeline.
The system can instantiate intelligent routers at the data
pipeline, including services such as solid-state memory devices,
graphical processing units, and/or packet monitor functions. The
system can switch data packets associated with the data objects to
the intelligent routers, where the data objects are extracted from
the data packets and stored in cache memory. Upon receiving
requests from client devices, the intelligent routers can transmit
the extracted data objects from the cache memory to the client
devices. The intelligent routers can, in turn, be decommissioned
subsequent to transmission of the extracted data objects.
[0013] One or more aspects of the subject disclosure include a
device, including a processing system including a processor, and a
memory that stores executable instructions that, when executed by
the processing system, facilitate performance of operations. The
operations can include monitoring a plurality of data packets in a
data pipeline of a network and, in turn, identifying a first data
object in the plurality of data packets according to the monitoring
of the plurality of data packets, where the monitoring can include
analyzing header information associated with the plurality of data
packets. The operations can also include instantiating an
intelligent router at the data pipeline of the network responsive
to the identifying the first data object, where the intelligent
router can include a solid-state memory device, a graphical
processing unit, and packet monitoring functions. The operations
can further include switching a first set of data packets of the
plurality of data packets associated with the first data object to
the intelligent router for extracting the first data object from
the first set of data packets to generate an extracted first data
object responsive to the identifying the first data object in the
plurality of data packets. Operation of the intelligent router can
be directed according to extended tables associated with a
domain-specific programming language. For example, the P4
programming language is specific to the domain of routing and the
control of routing operations. The operations can include receiving
a request from a client device for the first data object and, in
turn, transmitting the extracted first data object to the client
device via the data pipeline of the network responsive to the
request for the first data object. The operations can also include
decommissioning the intelligent router responsive to the
transmitting the extracted first data object.
[0014] One or more aspects of the subject disclosure include a
machine-readable medium, comprising executable instructions that,
when executed by a processing system including a processor,
facilitate performance of operations. The operations can include
monitoring a plurality of data packets in a data pipeline of a
network and identifying a first data object in the plurality of
data packets according to the monitoring of the plurality of data
packets. The identifying the first data object in the plurality of
data packets can further comprise identifying metadata associated
with the first data object. The monitoring can include analyzing
header information associated with the plurality of data packets.
The operations can also include instantiating an intelligent router
at the data pipeline of the network responsive to the identifying
the first data object. The operations can further include switching
a first set of data packets of the plurality of data packets
associated with the first data object to the intelligent router for
extracting the first data object from the first set of data packets
to generate an extracted first data object responsive to the
identifying the first data object in the plurality of data packets.
The operations can include transmitting the extracted first data
object to a client device via the data pipeline of the network.
[0015] One or more aspects of the subject disclosure include a
method, performing, by a processing system including a processor,
steps including monitoring a plurality of data packets in a data
pipeline of a network, where the monitoring can include analyzing
header information associated with the plurality of data packets.
The method can include identifying a first data object in the
plurality of data packets according to the monitoring of the
plurality of data packets, where the identifying the first data
object in the plurality of data packets can further comprise
identifying metadata associated with the first data object. The
method can also include switching a first set of data packets of
the plurality of data packets associated with the first data object
to an intelligent router for extracting the first data object from
the first set of data packets to generate an extracted first data
object responsive to the identifying the first data object in the
plurality of data packets. The method can further include
transmitting the extracted first data object to a client device via
the data pipeline of the network.
[0016] Referring now to FIG. 1, a block diagram is shown
illustrating an example, non-limiting embodiment of a
communications network 100 in accordance with various aspects
described herein. For example, communications network 100 can
facilitate in whole or in part processing data in a data pipeline.
A system can monitor a data pipeline to identify data objects in
data packets carried by the data pipeline. The system can
instantiate intelligent routers at the data pipeline, including
services such as solid-state memory devices, graphical processing
units, and/or a packet monitoring functions. The system can switch
data packets associated with the data objects to the intelligent
routers, where the data objects are extracted from the data packets
and stored in cache memory. Upon receiving requests from client
devices, the intelligent routers can transmit the extracted data
objects from the cache memory to the client devices. The
intelligent routers can, in turn, be decommissioned subsequent to
transmission of the extracted data objects.
[0017] In particular, a communications network 125 is presented for
providing broadband access 110 to a plurality of data terminals 114
via access terminal 112, wireless access 120 to a plurality of
mobile devices 124 and vehicle 126 via base station or access point
122, voice access 130 to a plurality of telephony devices 134, via
switching device 132 and/or media access 140 to a plurality of
audio/video display devices 144 via media terminal 142. In
particular, a communications network 125 is presented for providing
broadband access 110 to a plurality of data terminals 114 via
access terminal 112, wireless access 120 to a plurality of mobile
devices 124 and vehicle 126 via base station or access point 122,
voice access 130 to a plurality of telephony devices 134, via
switching device 132 and/or media access 140 to a plurality of
audio/video display devices 144 via media terminal 142. In
addition, communication network 125 is coupled to one or more
content sources 175 of audio, video, graphics, text and/or other
media. While broadband access 110, wireless access 120, voice
access 130 and media access 140 are shown separately, one or more
of these forms of access can be combined to provide multiple access
services to a single client device (e.g., mobile devices 124 can
receive media content via media terminal 142, data terminal 114 can
be provided voice access via switching device 132, and so on).
[0018] The communications network 125 includes a plurality of
network elements (NE) 150, 152, 154, 156, etc. for facilitating the
broadband access 110, wireless access 120, voice access 130, media
access 140 and/or the distribution of content from content sources
175. The communications network 125 can include a circuit switched
or packet switched network, a voice over Internet protocol (VoIP)
network, Internet protocol (IP) network, a cable network, a passive
or active optical network, a 4G, 5G, or higher generation wireless
access network, WIMAX network, UltraWideband network, personal area
network or other wireless access network, a broadcast satellite
network and/or other communications network.
[0019] In various embodiments, the access terminal 112 can include
a digital subscriber line access multiplexer (DSLAM), cable modem
termination system (CMTS), optical line terminal (OLT) and/or other
access terminal. The data terminals 114 can include personal
computers, laptop computers, netbook computers, tablets or other
computing devices along with digital subscriber line (DSL) modems,
data over coax service interface specification (DOCSIS) modems or
other cable modems, a wireless modem such as a 4G, 5G, or higher
generation modem, an optical modem and/or other access devices.
[0020] In various embodiments, the base station or access point 122
can include a 4G, 5G, or higher generation base station, an access
point that operates via an 802.11 standard such as 802.11n,
802.11ac or other wireless access terminal. The mobile devices 124
can include mobile phones, e-readers, tablets, phablets, wireless
modems, and/or other mobile computing devices.
[0021] In various embodiments, the switching device 132 can include
a private branch exchange or central office switch, a media
services gateway, VoIP gateway or other gateway device and/or other
switching device. The telephony devices 134 can include traditional
telephones (with or without a terminal adapter), VoIP telephones
and/or other telephony devices.
[0022] In various embodiments, the media terminal 142 can include a
cable head-end or other TV head-end, a satellite receiver, gateway
or other media terminal 142. The display devices 144 can include
televisions with or without a set top box, personal computers
and/or other display devices.
[0023] In various embodiments, the content sources 175 include
broadcast television and radio sources, video on demand platforms
and streaming video and audio services platforms, one or more
content data networks, data servers, web servers and other content
servers, and/or other sources of media.
[0024] In various embodiments, the communications network 125 can
include wired, optical and/or wireless links and the network
elements 150, 152, 154, 156, etc. can include service switching
points, signal transfer points, service control points, network
gateways, media distribution hubs, servers, firewalls, routers,
edge devices, switches and other network nodes for routing and
controlling communications traffic over wired, optical and wireless
links as part of the Internet and other public networks as well as
one or more private networks, for managing subscriber access, for
billing and network management and for supporting other network
functions.
[0025] FIGS. 2A-2C is a block diagram illustrating an example,
non-limiting embodiment of a system functioning within the
communication network of FIG. 1 in accordance with various aspects
described herein. FIG. 2A depicts an illustrative embodiment of a
system 200 for facilitating, in whole or in part, processing data
in a data pipeline. A system can monitor a data pipeline to
identify data objects in data packets carried by the data pipeline.
The system can instantiate intelligent routers at the data
pipeline, including services such as solid-state memory devices,
graphical processing units, and/or a packet monitoring functions.
The system can switch data packets associated with the data objects
to the intelligent routers, where the data objects are extracted
from the data packets and stored in cache memory. Upon receiving
requests from client devices and/or other devices and/or network
elements, the intelligent routers can transmit the extracted data
objects from the cache memory to the client devices. The
intelligent routers can, in turn, be decommissioned subsequent to
transmission of the extracted data objects.
[0026] In one or more embodiments, the system 200 can include a
data pipeline 202. The data pipeline 202 can include routers for
transmitting data packets. The data pipeline 202 can also include
transmission media, such as fiber optic lines and/or copper wire.
In one or more embodiments, the data pipeline 202 can further
include an analytical router 204. The analytical router 204 can
process packet data streams in the data pipeline in real-time. In
particular, analytical router 204 can analyze headers from data
packets to identify data objects 210 and 210' that are associated
with the data packets flowing in the data pipeline 202. For
example, the analytical router 204 can monitor data packets flowing
in the data pipeline 202 to determine if those data packets are
part of a data object 210, such as a video stream. In one or more
embodiments, the analytical router 204 can analyze header
information from the data packets. The analytical router 204 can
determine if the header information indicates that the data packets
are associated with a particular type of data object 210. For
example, header information, such as metadata, title data, data
type information, can be gleaned from the data packet header, and
can be associated with one or more types of data objects 210.
[0027] In one or more embodiments, the data pipeline 202 may be a
User Plane Function (UPF) included as a part of a 5G Core
infrastructure architecture. The UPF may implement a Control and
User Plane Separation (CUPS) strategy, where Control Plane (CP) and
User Plane (UP) functions are decoupled, such that functions
associated with data forwarding are decentralized from CP
functions. As a result, packet processing and traffic aggregation
may be performed in close proximity to a network edge. By moving
the functions of the data pipeline 202 close to the network edge,
the UPF can increase bandwidth efficiencies.
[0028] In one or more embodiments, the analytical router 204 can
access data models 206 that associate various types and/or
sequences of header data information with various types of data
objects, such as video streams. The analytical router 204 can
compare the header data information acquired from data packets it
has sampled from the data pipeline with header data from the data
models 206. If the analytical router 204 matches or nearly matches
the data model header information and the data packet header
information, then the analytical router 204 can conclude that the
sampled data packets from the data pipeline 202 are associated with
a particular type of data object 210.
[0029] In one or more embodiments, the data models 206 can be
generated via a video stream training function 208. As header
information of packet data is analyzed, target object metadata of
the header information and particular objects 210 can be mutually
associated and logged as tagged objects in a database 212. The
video stream training function 208 can use the mutual association
between metadata in the header information and data objects 210 to
train the data models 206 that are used by the analytical router
204 for identifying new data objects in real-time.
[0030] FIG. 2B further depicts an illustrative embodiment of a
system 220 for facilitating, in whole or in part, processing data
in a data pipeline. In one or more embodiments, the system 220 can
perform real-time tagging of data objects in the data packets of
the data pipeline 202. In one embodiment, a series of analytical
routers 204, 204', and 204'' can analyze packet data at the data
pipeline 202. The analytical router 204 can use the trained data
models 206 to determine if the data packets at the data pipeline
204 are associated data objects. The data packets are thereby
tagged as belonging to data objects, such as video objects.
[0031] In one or more embodiments, upon determining, based on
header information, that a group of data packets in the data
pipeline 202 is carrying payload data associated with a data
object, the analytic router 204 can direct the data packets to an
intelligent router 228, 228', or 228''. The intelligent router 228
can include one or more higher level devices for processing the
data packets at the data object level. For example, the intelligent
router 228 can include a packet monitoring device 232 for
extracting data object data from the data packet streams, a
solid-state memory device 234 for storing data object data, and/or
a graphical processing unit 236 for performing graphical and/or
mathematical functions, such as image pattern recognition, on the
data object data. In one or more embodiments, the generating,
obtaining and/or monitoring of this information can be responsive
to an authorization provided by the user. In one or more
embodiments, an analysis of data can be subject to authorization
from user(s) associated with the data, such as an opt-in, an
opt-out, acknowledgement requirements, notifications, selective
authorization based on types of data, and so forth.
[0032] In one or more embodiments, once the analytical router 204
identifies the data packets that include data object data, the
intelligent router 228 can extract the data object information from
the data packets, so that the system 220 can perform further
processing and/or transmission of the information at the data
object level. A significant amount of data pipeline
resources--bandwidth, capacity, quality of service--can be freed up
from processing massive data objects, such as video streams, while
intelligent routers 228, 228', and 228'' perform processing and
re-transmission with high efficiency. FIG. 2C further depicts an
illustrative embodiment of a system 240 for facilitating, in whole
or in part, processing data in a data pipeline. In particular, the
system 240 can dynamically allocate resources for processing
intensive activities of recognizing data objects and tags and
enhancing/redirecting/coding/manipulating data objects that have
been recognized. The system 240 utilizes controllers 242, 244, 246,
248 for providing a tagging model, policies, learning, and
triggering on external events. These front end controllers can be
of a relatively static set of resources for use by the system 240
providing pipelining services. By comparison, the backend
controllers 250 and 252 for providing
enhancing/redirecting/coding/manipulating of data objects that have
been identified by the system 240 can be relatively dynamically
added/subtracted by processes of instantiation, initiation, and/or
decommissioning of virtual network functions as processing needs
and quality of service demands change over time.
[0033] In one or more embodiments, long-term increases in data
traffic and increases network demand have been found to keep
service providers of Internet and communication data pipelining
busy with network centric & packet forwarding pipeline
activities. By comparison, over-the-top (OTT) services, such as
web-based streaming services, like those provided by Google.TM.,
Netflix.TM., Apple.TM., and Facebook.TM. have leveraged application
services and cloud computing businesses utilizing centralized cloud
solutions by adding intelligence beyond simple routing of data
packets at computing layers. In addition, 5G application demands
can force service providers to move toward smarter forwarding
pipeline that goes beyond traditional pipelining layers 1-4. In one
or more embodiments, the system 220 provides a multi-Layer network
delivery system that combines a basic forwarding pipeline 202 with
data object specific intelligent routers 228 operating at hardware
speeds. The data object capabilities of the system 220 can provide
a service provider with enhanced delivery speeds and data object
focus that can allow the service provider to move "up the stack"
into the application level while still providing lower level data
pipeline services.
[0034] In one or more embodiments, a system 220, based on
intelligent routers, can provide a multi-level layer environment
that can effectively bypass and/or significantly limit the usage of
software stacks. Intelligent router 228 can include functions, such
as but not limited to, integrated GPU (Graphic Processing Unit)
236, DPU (Date Processing Unit) 232, memory/queuing at a SSD
(Solid-State Drive) 234. These functions of the intelligent router
228 can be efficiently performed at very high hardware speed. In
addition, the intelligent router 228 can include a wide range of
programmability options consistent with media cruiser forwarding
& analytics pipelines in a multi-level layer.
[0035] In one or more embodiments, a video stream, such as a soccer
game, can be detected by the analytical router 204. The analytical
router 204 can identify the objects of the game (ball, players,
field, etc.,) in the data packets. The analytical router 204 can
capture metadata related to the game from headers of the data
packets. The analytical router 204 can simply cache this metadata
and can stream this metadata instead of streaming the entire set of
data packets. The data packets can be captured by the intelligent
router 228, which can use its processing resources 232-236 to
process the data packets at the data object level. The result is an
object-switching pipeline 202. The object-switching pipeline 202,
can be more efficient than a packet-switching pipeline. The ability
to switch at higher level primitives, such as data objects,
metadata, and/or video clips can result in better utilization of
wireless communication spectrums, network bandwidth, memory,
storage, and/or related resources. The additional computing devices
and nodes for the intelligent routers 228 can be integrated into
network resources. Object-level switching can enable the lower
layers (L1-L4) of an OSI network model to be collapsed into
intelligent router 228 sub-appliances, such as DPI devices 232, SSD
memory devices 234, and/or GPU devices 236.
[0036] In one or more embodiments, the system 220 can utilized
protocols, such as RDMA (Remote Direct Memory Access) or
enhanced-versions of RDMA, to work across wide area networks with
capability to extract data objects at the boundaries of these data
objects (e.g., files, Meta data, and/or video). By using
extensively-integrated appliances, such as DPI 232, SSD memory 234,
and GPU 236, at the intelligent routers 228, all of the needed
object-level processing can be automated and centralized. This
concentration of resources can be leveraged to offer
personalization of services and to gain maximum efficiency for the
service provider networks while eliminating or reducing the need
for the operating system (OS) and other, related, traditional
memory stacks.
[0037] In one or more embodiments, a switching "fabric" of
traditional routers can be overlaid with intelligent routers 228,
including for DPI 232, SSD memory 234, and/or GPU 236. These
intelligent routers 228 can be partitioned into independent
resource functions available as a pool to the network. As a result,
networking and service resources management schedules can be
coordinated using high-level event queues.
[0038] In one or more embodiments, the analytical router 204 can
capture a set of data packets and can use a DPI function, such as
the DPI 232 of the intelligent router 228, to determine the general
type of content that is being carried by the data packers. The
analytical router 204 can identify and categorize the type of the
data object (if such a data object exists). For example, the
analytical router 204 can determine if the payloads of the data
packets contain information related to a picture, a video, and/or
metadata. Once the analytical router 204 has determined the type of
data object, that analytical router 204 and/or the intelligent
router 228 can decide how to process the data packets, via the
intelligent router 228, in order to efficiently extract the data
object.
[0039] In one or more embodiments, the object-level pipeline 202
provides integration of a spine and leaf programmable switching
fabric with shared intelligent router 228 appliances. The
object-level pipeline 202 enables the service provider network to
take control of containerized (object level) functions, which are
compatible with edge and core cloud-centric platforms. The
object-level pipeline 202 can take advantages of shared intelligent
router 228 appliances that are attached to the switching fabric,
via the domain-specific programming language, such as the P4
language, where the domain-specific programming includes a
collection of specific processing flows for the intelligent routers
based on a data modeling signatures for the specific data objects.
In one or more embodiments, the switching fabric can utilize the
attached intelligent router 228 appliances, such as DPI 232, SSD
memory 234, and/or GPU 236, to characterize metadata and/or data
objects. The switching fabric can utilizes the domain-specific
programming language, such as the P4 language or a "P4-like"
programming language, to perform "look up" operations and/or access
"look up" tables, which can be used to instantiate and/or spin off
network and service functions in an adaptive fashion. The look up
tables can be based on categorizations signatures, such as data
objects or metadata. As a result, the object-level pipeline 202 can
provide enhanced computing, data storage, and/or address space
capabilities. These enhanced capabilities can potentially eliminate
and/or reduce the need for data packet switching while enabling
object-level and/or metadata-level switching.
[0040] For example, a group of data packet streams can represent a
video clip, an image of a person, and/or some other intelligent
value. The object-level pipelines 202 can carry groups of data
packets that can represent one or more intelligent events. The
inclusion of intelligent routers in the object-level pipelines can
significantly improve data latency and network utilization, while
the control of service functions can be pushed down to the service
provider networks. The extension and application of the
domain-specific programming language to control of the analytical
routers 204 and the intelligent routers 228 can enable the
identification of data object targets and the use of plug-ins that
can allow the object-level pipeline to perform faster, more
efficiently, and/or more intelligently, while reducing network
utilization by use of coding techniques. In one or more
embodiments, the system 220 does not need to send an entire video
stream of data packets to a client device. Rather, the system 220
can utilized the additional processing power of the intelligent
routers 228 at the switch fabric level to efficiently process the
packet data and to transmit the content at an object-level and/or
metadata-level.
[0041] In one or more embodiments, a system 220 of combined
object-level data pipelines can use domain-specific programmability
to control intelligent router 228 appliances, including DPI, SSD
memory, and/or GPU. Data packet flows can enter the system 220, can
be categorized into metadata and/or data objects via or modeling
signatures for the object types. Network-level services can be set
off from the categorized collection of packet flows and can form a
data object and/or a metadata object. In one or more embodiments,
data packets can be processed into data objects, which are
subsequently tagged for object-level switching by deeper networking
switches or cached for load and optimization. In one or more
embodiments, the system is able to spin up containers associated
with the data-object and/or metadata according to the categories,
or types, of objects, which will be used for object-level
switching. In another embedment, machine-learning (ML) algorithms
can be used to perform knowledge-based, object-level switching
based on one or more factors, including external events and
pipeline-accessible metadata.
[0042] In one or more embodiments, the system 220 can use
programmable (or advanced programmable) extension tables. The
extension tables can be designed to embrace integrated switching
with DPI 232, SSD memory 234, and/or GPUs, where the
programmability enables the intelligent router 228 to operate at
hardware speeds, yet with extremely flexible programmability. For
example, a table descriptor can be used to provide multi-layer
instructions to each respective hardware component 232 in the
intelligent router 228. The extension tables can function as "DNA"
instructions for configuring and controlling the hardware
components 232 processing any layer of the data object at hardware
speeds for both the intelligent router 228 and for other switches
and routers. The "DNA" Instructions can describe actions to be
performed by the various hardware in the various scenarios. In
various examples, the table instructions can direct switches to
perform switching function based on IP layers. The table
instructions can direct SSD memories 234 can perform storage
functions, including advantageous hardware acceleration features,
in support of Quality-of-Service (QoS), traffic management, and/or
delivery diversity. The memory instructions can be used to perform
data queue management and/or "glue logic" to minimize the data
copying and/or the number of copies of data across the various
hardware components of the intelligent router 228. The table
instructions can direct DPUs 232 apply packet monitoring to one or
more data packets. The table instructions can direct GPUs 236 to
perform graphical computation and/or parallel graphical
computational processing. In one or more embodiments, the
domain-specific language, which can be used for packet-level
switching, can further be extended to direct operations for
inspecting payloads, controlling memories, and/or overseeing
graphical functions. In one embodiment, data packets can be subject
to encryption. In such a case, the system 220 can use machine
learning to identify patterns and/or signatures that can be used by
the system 220 to overcome the encryption.
[0043] In one example, the table extensions can include
instructions, such as switch control, which can match information
in one or more headers of data packets. The header information can
perform actions for forwarding pipeline data to one or more logical
ports, including SSD memory 234, GPU 236, and/or DPU 232 hardware
functions. The functions at the intelligent routers can be used to
process payloads of data packets (as opposed to dumbly forwarding
the data packets). The functions can be used to match logical
addresses and/or to copy data from a logical port into an
addressable memory. The memory pointers can be used as references
by other components. The functions at the intelligent routers 228
can be used at the SSD 234 to compare and/or match specific SSD
features or actions, such as storing, organizing, scheduling
deliveries, and/or categorizing data and/or objects.
[0044] The functions at the intelligent routers 228 can be used to
direct a GPU 236 to perform graphical functions. The functions at
the intelligent routers 228 can be used to direct the DPU to
perform packet monitoring of one or more data packets in order to
compare and/or match data, including categorization of metadata
and/or tagging. Intelligent router equipment and functions are
described in Table 1, below:
TABLE-US-00001 TABLE 1 Intelligent Router equipment and functions.
Switch Forwarding pipeline Match/action (forwarding) SSD store and
compute pipeline Match/Action (compute and delivery) GPU compute
instruction pipeline Mach/Action (Compute and . . . ) DPI;
instruction pipeline Mach/Action (shift into payload and
action)
[0045] In one or more embodiments, a packet data source at the
pipeline 202 can trigger different types of application containers
based on different types of data objects, metadata, and/or flow
signatures detected by the analytical router 204 at the pipeline
202. In one example, the domain-specific programming language
instructions can direct the system 202 to categorize the data
packets and/or automatically process pre-labeled packets into
objects. In this example, the data objects can be handled according
to level policies and/according to policy definitions. This
architecture can enable shared platforms at a network edge to meet
standards for application performance, latency, scaling,
optimization, user data privacy and/or security.
[0046] In one or more embodiments, edge functionality can be
implemented to exploit new technology trends and/or enhance
opportunities beyond simple "dumb" pipes. In one example,
applications executing at user equipment (UE) and/or
internet-of-things (IOT) devices can utilizes edge functions by
using network protocols, such RDMA or simple rest APIs. Service
providers can complement intelligent routers 228 by adding
applications, such as intelligent street light gateways to create a
data-driven transportation solution.
[0047] In one or more embodiments, the system 220 can facilitate
mesh-forming data collectors that can enable dynamic configuration
of instructions that can match instruction "DNA" sets. These "DNA"
instruction sets can be received by the system 220 every period
from a Software Defined Collector controller. In one or more
embodiments, a ML-centric approach can be used to establish
programmable "super tables," which can be used for multi-layer
networking. Ambient, or background, computing at the intelligent
routers 228 can facilitate applications, which incorporate ML
and/or other forms of artificial intelligence (AI). The ML can be
characterized by features for human-like cognitive, behavioral
capabilities, and/or contextual awareness. A digital environment
can be created, in which companies can integrate technology
seamlessly and/or invisibly into a large variety of devices to
maximize usefulness while minimizing demands for attention or
maintenance.
[0048] In one or more embodiments, the system 220 can support a
number of use cases. For example, the intelligent router 228 can
include an inline DPU pipeline with a P4-based instruction wrapper.
The intelligent router 228 can also include Inline object switching
pipeline with a P4 wrapper, inline ML inference pipeline with an AI
wrapper, and an inline Codec pipeline with IP security
(IPSEC)/Secure Sockets Layer (SSL) and a P4 wrapper. The
intelligent router 228 van also include P4 Interrupt/VProbe
observability with a P4 wrapper and/or P4 Video/Voice/IOT
Compression with a P4 Wrapper. In this example, an extension
"super" table can be used to establish flow categorization of
flows. The intelligent router 228 can perform operations for
dynamically spinning-off or instantiating multilayer hardware level
functions. The hardware level functions can be dynamically provided
on per service and/or per flow basis. The intelligent router can
perform operations for re-programming the extended tables, for
association extended tables to specific probes and/or compute
functions, for accessing a "DNA" data model, key matrices and/or
data probes, for controlling delivery mechanisms, for performing
operations for computing, ML, aggregation of object data, and/or
generating entities.
[0049] FIG. 2D depicts an illustrative embodiment of a method 260
in accordance with various aspects described herein. A system 220
can monitor data packets in a data pipeline, in step 262. In step
264, the system 220 can identify data objects in the data packets.
For example, the system 220 can analyze header information from
data packets to detect indicators for a type of data object, such a
metadata. In step 266, the analytic router 204 can instantiate an
intelligent router 228 at the data pipeline and switch the set of
data packets identified to the data object to the intelligent
router 228 for processing. If the intelligent router 228 is already
extant at the data pipeline, then the system 220 can simply direct
the set of data packets to the intelligent router 228. In step 268,
the intelligent router 228 can extract the data object from the set
of data packets.
[0050] In step 270, the system 220 can receive a request from a
client device for a data object. The system 220 can respond to the
request by transmitting the extracted data object to the client
device, in step 272. In step 274, the system 220 can decommission
the intelligent router 228 at the conclusion of the extraction
and/or transmission of the extracted data object.
[0051] While for purposes of simplicity of explanation, the
respective processes are shown and described as a series of blocks
in FIG. 2D, it is to be understood and appreciated that the claimed
subject matter is not limited by the order of the blocks, as some
blocks may occur in different orders and/or concurrently with other
blocks from what is depicted and described herein. Moreover, not
all illustrated blocks may be required to implement the methods
described herein.
[0052] Referring now to FIG. 3, a block diagram 300 is shown
illustrating an example, non-limiting embodiment of a virtualized
communication network in accordance with various aspects described
herein. In particular a virtualized communication network is
presented that can be used to implement some or all of the
subsystems and functions of communication network 100, the
subsystems and functions of system 200, and method 260 presented in
FIGS. 1, 2A-2D, and 3. For example, communications network 100 can
facilitate in whole or in part processing data in a data pipeline.
A system can monitor a data pipeline to identify data objects in
data packets carried by the data pipeline. The system can
instantiate intelligent routers at the data pipeline, including
services such as solid-state memory devices, graphical processing
units, and/or a packet monitoring functions. The system can switch
data packets associated with the data objects to the intelligent
routers, where the data objects are extracted from the data packets
and stored in cache memory. Upon receiving requests from client
devices, the intelligent routers can transmit the extracted data
objects from the cache memory to the client devices. The
intelligent routers can, in turn, be decommissioned subsequent to
transmission of the extracted data objects.
[0053] In particular, a cloud networking architecture is shown that
leverages cloud technologies and supports rapid innovation and
scalability via a transport layer 350, a virtualized network
function cloud 325 and/or one or more cloud computing environments
375. In various embodiments, this cloud networking architecture is
an open architecture that leverages application programming
interfaces (APIs); reduces complexity from services and operations;
supports more nimble business models; and rapidly and seamlessly
scales to meet evolving customer requirements including traffic
growth, diversity of traffic types, and diversity of performance
and reliability expectations.
[0054] In contrast to traditional network elements--which are
typically integrated to perform a single function, the virtualized
communication network employs virtual network elements (VNEs) 330,
332, 334, etc. that perform some or all of the functions of network
elements 150, 152, 154, 156, etc. For example, the network
architecture can provide a substrate of networking capability,
often called Network Function Virtualization Infrastructure (NFVI)
or simply infrastructure that is capable of being directed with
software and Software Defined Networking (SDN) protocols to perform
a broad variety of network functions and services. This
infrastructure can include several types of substrates. The most
typical type of substrate being servers that support Network
Function Virtualization (NFV), followed by packet forwarding
capabilities based on generic computing resources, with specialized
network technologies brought to bear when general purpose
processors or general purpose integrated circuit devices offered by
merchants (referred to herein as merchant silicon) are not
appropriate. In this case, communication services can be
implemented as cloud-centric workloads.
[0055] As an example, a traditional network element 150 (shown in
FIG. 1), such as an edge router can be implemented via a VNE 330
composed of NFV software modules, merchant silicon, and associated
controllers. The software can be written so that increasing
workload consumes incremental resources from a common resource
pool, and moreover so that it's elastic: so the resources are only
consumed when needed. In a similar fashion, other network elements
such as other routers, switches, edge caches, and middle-boxes are
instantiated from the common resource pool. Such sharing of
infrastructure across a broad set of uses makes planning and
growing infrastructure easier to manage.
[0056] In an embodiment, the transport layer 350 includes fiber,
cable, wired and/or wireless transport elements, network elements
and interfaces to provide broadband access 110, wireless access
120, voice access 130, media access 140 and/or access to content
sources 175 for distribution of content to any or all of the access
technologies. In particular, in some cases a network element needs
to be positioned at a specific place, and this allows for less
sharing of common infrastructure. Other times, the network elements
have specific physical layer adapters that cannot be abstracted or
virtualized, and might require special DSP code and analog
front-ends (AFEs) that do not lend themselves to implementation as
VNEs 330, 332 or 334. These network elements can be included in
transport layer 350.
[0057] The virtualized network function cloud 325 interfaces with
the transport layer 350 to provide the VNEs 330, 332, 334, etc. to
provide specific NFVs. In particular, the virtualized network
function cloud 325 leverages cloud operations, applications, and
architectures to support networking workloads. The virtualized
network elements 330, 332 and 334 can employ network function
software that provides either a one-for-one mapping of traditional
network element function or alternately some combination of network
functions designed for cloud computing. For example, VNEs 330, 332
and 334 can include route reflectors, domain name system (DNS)
servers, and dynamic host configuration protocol (DHCP) servers,
system architecture evolution (SAE) and/or mobility management
entity (MME) gateways, broadband network gateways, IP edge routers
for IP-VPN, Ethernet and other services, load balancers,
distributers and other network elements. Because these elements
don't typically need to forward large amounts of traffic, their
workload can be distributed across a number of servers--each of
which adds a portion of the capability, and overall which creates
an elastic function with higher availability than its former
monolithic version. These virtual network elements 330, 332, 334,
etc. can be instantiated and managed using an orchestration
approach similar to those used in cloud compute services.
[0058] The cloud computing environments 375 can interface with the
virtualized network function cloud 325 via APIs that expose
functional capabilities of the VNEs 330, 332, 334, etc. to provide
the flexible and expanded capabilities to the virtualized network
function cloud 325. In particular, network workloads may have
applications distributed across the virtualized network function
cloud 325 and cloud computing environment 375 and in the commercial
cloud, or might simply orchestrate workloads supported entirely in
NFV infrastructure from these third party locations.
[0059] Turning now to FIG. 4, there is illustrated a block diagram
of a computing environment in accordance with various aspects
described herein. In order to provide additional context for
various embodiments of the embodiments described herein, FIG. 4 and
the following discussion are intended to provide a brief, general
description of a suitable computing environment 400 in which the
various embodiments of the subject disclosure can be implemented.
In particular, computing environment 400 can be used in the
implementation of network elements 150, 152, 154, 156, access
terminal 112, base station or access point 122, switching device
132, media terminal 142, and/or VNEs 330, 332, 334, etc. Each of
these devices can be implemented via computer-executable
instructions that can run on one or more computers, and/or in
combination with other program modules and/or as a combination of
hardware and software. For example, communications network 100 can
facilitate in whole or in part processing data in a data pipeline.
A system can monitor a data pipeline to identify data objects in
data packets carried by the data pipeline. The system can
instantiate intelligent routers at the data pipeline, including
services such as solid-state memory devices, graphical processing
units, and/or a packet monitoring functions. The system can switch
data packets associated with the data objects to the intelligent
routers, where the data objects are extracted from the data packets
and stored in cache memory. Upon receiving requests from client
devices, the intelligent routers can transmit the extracted data
objects from the cache memory to the client devices. The
intelligent routers can, in turn, be decommissioned subsequent to
transmission of the extracted data objects.
[0060] Generally, program modules comprise routines, programs,
components, data structures, etc., that perform particular tasks or
implement particular abstract data types. Moreover, those skilled
in the art will appreciate that the methods can be practiced with
other computer system configurations, comprising single-processor
or multiprocessor computer systems, minicomputers, mainframe
computers, as well as personal computers, hand-held computing
devices, microprocessor-based or programmable consumer electronics,
and the like, each of which can be operatively coupled to one or
more associated devices.
[0061] As used herein, a processing circuit includes one or more
processors as well as other application specific circuits such as
an application specific integrated circuit, digital logic circuit,
state machine, programmable gate array or other circuit that
processes input signals or data and that produces output signals or
data in response thereto. It should be noted that while any
functions and features described herein in association with the
operation of a processor could likewise be performed by a
processing circuit.
[0062] The illustrated embodiments of the embodiments herein can be
also practiced in distributed computing environments where certain
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed computing
environment, program modules can be located in both local and
remote memory storage devices.
[0063] Computing devices typically comprise a variety of media,
which can comprise computer-readable storage media and/or
communications media, which two terms are used herein differently
from one another as follows. Computer-readable storage media can be
any available storage media that can be accessed by the computer
and comprises both volatile and nonvolatile media, removable and
non-removable media. By way of example, and not limitation,
computer-readable storage media can be implemented in connection
with any method or technology for storage of information such as
computer-readable instructions, program modules, structured data or
unstructured data.
[0064] Computer-readable storage media can comprise, but are not
limited to, random access memory (RAM), read only memory (ROM),
electrically erasable programmable read only memory (EEPROM), flash
memory or other memory technology, compact disk read only memory
(CD-ROM), digital versatile disk (DVD) or other optical disk
storage, magnetic cassettes, magnetic tape, magnetic disk storage
or other magnetic storage devices or other tangible and/or
non-transitory media which can be used to store desired
information. In this regard, the terms "tangible" or
"non-transitory" herein as applied to storage, memory or
computer-readable media, are to be understood to exclude only
propagating transitory signals per se as modifiers and do not
relinquish rights to all standard storage, memory or
computer-readable media that are not only propagating transitory
signals per se.
[0065] Computer-readable storage media can be accessed by one or
more local or remote computing devices, e.g., via access requests,
queries or other data retrieval protocols, for a variety of
operations with respect to the information stored by the
medium.
[0066] Communications media typically embody computer-readable
instructions, data structures, program modules or other structured
or unstructured data in a data signal such as a modulated data
signal, e.g., a carrier wave or other transport mechanism, and
comprises any information delivery or transport media. The term
"modulated data signal" or signals refers to a signal that has one
or more of its characteristics set or changed in such a manner as
to encode information in one or more signals. By way of example,
and not limitation, communication media comprise wired media, such
as a wired network or direct-wired connection, and wireless media
such as acoustic, RF, infrared and other wireless media.
[0067] With reference again to FIG. 4, the example environment can
comprise a computer 402, the computer 402 comprising a processing
unit 404, a system memory 406 and a system bus 408. The system bus
408 couples system components including, but not limited to, the
system memory 406 to the processing unit 404. The processing unit
404 can be any of various commercially available processors. Dual
microprocessors and other multiprocessor architectures can also be
employed as the processing unit 404.
[0068] The system bus 408 can be any of several types of bus
structure that can further interconnect to a memory bus (with or
without a memory controller), a peripheral bus, and a local bus
using any of a variety of commercially available bus architectures.
The system memory 406 comprises ROM 410 and RAM 412. A basic
input/output system (BIOS) can be stored in a non-volatile memory
such as ROM, erasable programmable read only memory (EPROM),
EEPROM, which BIOS contains the basic routines that help to
transfer information between elements within the computer 402, such
as during startup. The RAM 412 can also comprise a high-speed RAM
such as static RAM for caching data.
[0069] The computer 402 further comprises an internal hard disk
drive (HDD) 414 (e.g., EIDE, SATA), which internal HDD 414 can also
be configured for external use in a suitable chassis (not shown), a
magnetic floppy disk drive (FDD) 416, (e.g., to read from or write
to a removable diskette 418) and an optical disk drive 420, (e.g.,
reading a CD-ROM disk 422 or, to read from or write to other high
capacity optical media such as the DVD). The HDD 414, magnetic FDD
416 and optical disk drive 420 can be connected to the system bus
408 by a hard disk drive interface 424, a magnetic disk drive
interface 426 and an optical drive interface 428, respectively. The
hard disk drive interface 424 for external drive implementations
comprises at least one or both of Universal Serial Bus (USB) and
Institute of Electrical and Electronics Engineers (IEEE) 1394
interface technologies. Other external drive connection
technologies are within contemplation of the embodiments described
herein.
[0070] The drives and their associated computer-readable storage
media provide nonvolatile storage of data, data structures,
computer-executable instructions, and so forth. For the computer
402, the drives and storage media accommodate the storage of any
data in a suitable digital format. Although the description of
computer-readable storage media above refers to a hard disk drive
(HDD), a removable magnetic diskette, and a removable optical media
such as a CD or DVD, it should be appreciated by those skilled in
the art that other types of storage media which are readable by a
computer, such as zip drives, magnetic cassettes, flash memory
cards, cartridges, and the like, can also be used in the example
operating environment, and further, that any such storage media can
contain computer-executable instructions for performing the methods
described herein.
[0071] A number of program modules can be stored in the drives and
RAM 412, comprising an operating system 430, one or more
application programs 432, other program modules 434 and program
data 436. All or portions of the operating system, applications,
modules, and/or data can also be cached in the RAM 412. The systems
and methods described herein can be implemented utilizing various
commercially available operating systems or combinations of
operating systems.
[0072] A user can enter commands and information into the computer
402 through one or more wired/wireless input devices, e.g., a
keyboard 438 and a pointing device, such as a mouse 440. Other
input devices (not shown) can comprise a microphone, an infrared
(IR) remote control, a joystick, a game pad, a stylus pen, touch
screen or the like. These and other input devices are often
connected to the processing unit 404 through an input device
interface 442 that can be coupled to the system bus 408, but can be
connected by other interfaces, such as a parallel port, an IEEE
1394 serial port, a game port, a universal serial bus (USB) port,
an IR interface, etc.
[0073] A monitor 444 or other type of display device can be also
connected to the system bus 408 via an interface, such as a video
adapter 446. It will also be appreciated that in alternative
embodiments, a monitor 444 can also be any display device (e.g.,
another computer having a display, a smart phone, a tablet
computer, etc.) for receiving display information associated with
computer 402 via any communication means, including via the
Internet and cloud-based networks. In addition to the monitor 444,
a computer typically comprises other peripheral output devices (not
shown), such as speakers, printers, etc.
[0074] The computer 402 can operate in a networked environment
using logical connections via wired and/or wireless communications
to one or more remote computers, such as a remote computer(s) 448.
The remote computer(s) 448 can be a workstation, a server computer,
a router, a personal computer, portable computer,
microprocessor-based entertainment appliance, a peer device or
other common network node, and typically comprises many or all of
the elements described relative to the computer 402, although, for
purposes of brevity, only a remote memory/storage device 450 is
illustrated. The logical connections depicted comprise
wired/wireless connectivity to a local area network (LAN) 452
and/or larger networks, e.g., a wide area network (WAN) 454. Such
LAN and WAN networking environments are commonplace in offices and
companies, and facilitate enterprise-wide computer networks, such
as intranets, all of which can connect to a global communications
network, e.g., the Internet.
[0075] When used in a LAN networking environment, the computer 402
can be connected to the LAN 452 through a wired and/or wireless
communication network interface or adapter 456. The adapter 456 can
facilitate wired or wireless communication to the LAN 452, which
can also comprise a wireless AP disposed thereon for communicating
with the adapter 456.
[0076] When used in a WAN networking environment, the computer 402
can comprise a modem 458 or can be connected to a communications
server on the WAN 454 or has other means for establishing
communications over the WAN 454, such as by way of the Internet.
The modem 458, which can be internal or external and a wired or
wireless device, can be connected to the system bus 408 via the
input device interface 442. In a networked environment, program
modules depicted relative to the computer 402 or portions thereof,
can be stored in the remote memory/storage device 450. It will be
appreciated that the network connections shown are example and
other means of establishing a communications link between the
computers can be used.
[0077] The computer 402 can be operable to communicate with any
wireless devices or entities operatively disposed in wireless
communication, e.g., a printer, scanner, desktop and/or portable
computer, portable data assistant, communications satellite, any
piece of equipment or location associated with a wirelessly
detectable tag (e.g., a kiosk, news stand, restroom), and
telephone. This can comprise Wireless Fidelity (Wi-Fi) and
BLUETOOTH.RTM. wireless technologies. Thus, the communication can
be a predefined structure as with a conventional network or simply
an ad hoc communication between at least two devices.
[0078] Wi-Fi can allow connection to the Internet from a couch at
home, a bed in a hotel room or a conference room at work, without
wires. Wi-Fi is a wireless technology similar to that used in a
cell phone that enables such devices, e.g., computers, to send and
receive data indoors and out; anywhere within the range of a base
station. Wi-Fi networks use radio technologies called IEEE 802.11
(a, b, g, n, ac, ag, etc.,) to provide secure, reliable, fast
wireless connectivity. A Wi-Fi network can be used to connect
computers to each other, to the Internet, and to wired networks
(which can use IEEE 802.3 or Ethernet). Wi-Fi networks operate in
the unlicensed 2.4 and 5 GHz radio bands for example or with
products that contain both bands (dual band), so the networks can
provide real-world performance similar to the basic 10BaseT wired
Ethernet networks used in many offices.
[0079] Turning now to FIG. 5, an embodiment 500 of a mobile network
platform 510 is shown that is an example of network elements 150,
152, 154, 156, and/or VNEs 330, 332, 334, etc. For example,
platform 510 can facilitate, in whole or in part, processing data
in a data pipeline. A system can monitor a data pipeline to
identify data objects in data packets carried by the data pipeline.
The system can instantiate intelligent routers at the data
pipeline, including services such as solid-state memory devices,
graphical processing units, and/or a packet monitoring functions.
The system can switch data packets associated with the data objects
to the intelligent routers, where the data objects are extracted
from the data packets and stored in cache memory. Upon receiving
requests from client devices, the intelligent routers can transmit
the extracted data objects from the cache memory to the client
devices. The intelligent routers can, in turn, be decommissioned
subsequent to transmission of the extracted data objects.
[0080] In one or more embodiments, the mobile network platform 510
can generate and receive signals transmitted and received by base
stations or access points such as base station or access point 122.
Generally, mobile network platform 510 can comprise components,
e.g., nodes, gateways, interfaces, servers, or disparate platforms,
that facilitate both packet-switched (PS) (e.g., internet protocol
(IP), frame relay, asynchronous transfer mode (ATM)) and
circuit-switched (CS) traffic (e.g., voice and data), as well as
control generation for networked wireless telecommunication. As a
non-limiting example, mobile network platform 510 can be included
in telecommunications carrier networks, and can be considered
carrier-side components as discussed elsewhere herein. Mobile
network platform 510 comprises CS gateway node(s) 512 which can
interface CS traffic received from legacy networks like telephony
network(s) 540 (e.g., public switched telephone network (PSTN), or
public land mobile network (PLMN)) or a signaling system #7 (SS7)
network 560. CS gateway node(s) 512 can authorize and authenticate
traffic (e.g., voice) arising from such networks. Additionally, CS
gateway node(s) 512 can access mobility, or roaming, data generated
through SS7 network 560; for instance, mobility data stored in a
visited location register (VLR), which can reside in memory 530.
Moreover, CS gateway node(s) 512 interfaces CS-based traffic and
signaling and PS gateway node(s) 518. As an example, in a 3GPP UMTS
network, CS gateway node(s) 512 can be realized at least in part in
gateway GPRS support node(s) (GGSN). It should be appreciated that
functionality and specific operation of CS gateway node(s) 512, PS
gateway node(s) 518, and serving node(s) 516, is provided and
dictated by radio technology(ies) utilized by mobile network
platform 510 for telecommunication over a radio access network 520
with other devices, such as a radiotelephone 575.
[0081] The embodiment 500 may support and/or operate according to a
5G system (5GS). A 5GS can include Next Generation (NG) functions,
such as a NG-RAN and a NG-CORE. For example, the NG-RAN may
consists of gNBs, also known as New Radio (NR) base stations,
and/or NG-eNBs. The NG-eNBs may include LTE base stations capable
of supporting a 5G Core Network. The NG-RAN may be capable of
supporting network slicing and/or aspects related to QoS flow
management and/or mapping to radio bearers. The NG-CORE may provide
capabilities for full separation between the Control Plane (CP) and
the User Plane (UP). For example, a Session Management Function
(SMF) may include session management functions and/or UE IP Address
function, which may be provided in an LTE system via MME and/or PGW
functions. An Access and Mobility Management Function (AMF) may
include mobility management and network access functions,
registration functions, and/or security functions. A User Plane
Function (UPF) may include network functions performing purely
packet processing and transmission operations for the data plane.
The NG-RAN and NG-CORE can interface via an NG interface. The NG
interface may be either of two versions, NG-C or NG-U, which may be
connected to to the AMF and the UPF.
[0082] In addition to receiving and processing CS-switched traffic
and signaling, PS gateway node(s) 518 can authorize and
authenticate PS-based data sessions with served mobile devices.
Data sessions can comprise traffic, or content(s), exchanged with
networks external to the mobile network platform 510, like wide
area network(s) (WANs) 550, enterprise network(s) 570, and service
network(s) 580, which can be embodied in local area network(s)
(LANs), can also be interfaced with mobile network platform 510
through PS gateway node(s) 518. It is to be noted that WANs 550 and
enterprise network(s) 570 can embody, at least in part, a service
network(s) like IP multimedia subsystem (IMS). Based on radio
technology layer(s) available in technology resource(s) or radio
access network 520, PS gateway node(s) 518 can generate packet data
protocol contexts when a data session is established; other data
structures that facilitate routing of packetized data also can be
generated. To that end, in an aspect, PS gateway node(s) 518 can
comprise a tunnel interface (e.g., tunnel termination gateway (TTG)
in 3GPP UMTS network(s) (not shown)) which can facilitate
packetized communication with disparate wireless network(s), such
as Wi-Fi networks.
[0083] In embodiment 500, mobile network platform 510 also
comprises serving node(s) 516 that, based upon available radio
technology layer(s) within technology resource(s) in the radio
access network 520, convey the various packetized flows of data
streams received through PS gateway node(s) 518. It is to be noted
that for technology resource(s) that rely primarily on CS
communication, server node(s) can deliver traffic without reliance
on PS gateway node(s) 518; for example, server node(s) can embody
at least in part a mobile switching center. As an example, in a
3GPP UMTS network, serving node(s) 516 can be embodied in serving
GPRS support node(s) (SGSN).
[0084] For radio technologies that exploit packetized
communication, server(s) 514 in mobile network platform 510 can
execute numerous applications that can generate multiple disparate
packetized data streams or flows, and manage (e.g., schedule,
queue, format . . . ) such flows. Such application(s) can comprise
add-on features to standard services (for example, provisioning,
billing, customer support . . . ) provided by mobile network
platform 510. Data streams (e.g., content(s) that are part of a
voice call or data session) can be conveyed to PS gateway node(s)
518 for authorization/authentication and initiation of a data
session, and to serving node(s) 516 for communication thereafter.
In addition to application server, server(s) 514 can comprise
utility server(s), a utility server can comprise a provisioning
server, an operations and maintenance server, a security server
that can implement at least in part a certificate authority and
firewalls as well as other security mechanisms, and the like. In an
aspect, security server(s) secure communication served through
mobile network platform 510 to ensure network's operation and data
integrity in addition to authorization and authentication
procedures that CS gateway node(s) 512 and PS gateway node(s) 518
can enact. Moreover, provisioning server(s) can provision services
from external network(s) like networks operated by a disparate
service provider; for instance, WAN 550 or Global Positioning
System (GPS) network(s) (not shown). Provisioning server(s) can
also provision coverage through networks associated to mobile
network platform 510 (e.g., deployed and operated by the same
service provider), such as the distributed antennas networks shown
in FIG. 1(s) that enhance wireless service coverage by providing
more network coverage.
[0085] It is to be noted that server(s) 514 can comprise one or
more processors configured to confer at least in part the
functionality of mobile network platform 510. To that end, the one
or more processor can execute code instructions stored in memory
530, for example. It is should be appreciated that server(s) 514
can comprise a content manager, which operates in substantially the
same manner as described hereinbefore.
[0086] In example embodiment 500, memory 530 can store information
related to operation of mobile network platform 510. Other
operational information can comprise provisioning information of
mobile devices served through mobile network platform 510,
subscriber databases; application intelligence, pricing schemes,
e.g., promotional rates, flat-rate programs, couponing campaigns;
technical specification(s) consistent with telecommunication
protocols for operation of disparate radio, or wireless, technology
layers; and so forth. Memory 530 can also store information from at
least one of telephony network(s) 540, WAN 550, SS7 network 560, or
enterprise network(s) 570. In an aspect, memory 530 can be, for
example, accessed as part of a data store component or as a
remotely connected memory store.
[0087] In order to provide a context for the various aspects of the
disclosed subject matter, FIG. 5, and the following discussion, are
intended to provide a brief, general description of a suitable
environment in which the various aspects of the disclosed subject
matter can be implemented. While the subject matter has been
described above in the general context of computer-executable
instructions of a computer program that runs on a computer and/or
computers, those skilled in the art will recognize that the
disclosed subject matter also can be implemented in combination
with other program modules. Generally, program modules comprise
routines, programs, components, data structures, etc. that perform
particular tasks and/or implement particular abstract data
types.
[0088] Turning now to FIG. 6, an illustrative embodiment of a
communication device 600 is shown. The communication device 600 can
serve as an illustrative embodiment of devices such as data
terminals 114, mobile devices 124, vehicle 126, display devices 144
or other client devices for communication via either communications
network 125. For example, computing device 600 can facilitate, in
whole or in part, equipment for processing data in a data pipeline.
A system can monitor a data pipeline to identify data objects in
data packets carried by the data pipeline. The system can
instantiate intelligent routers at the data pipeline, including
services such as solid-state memory devices, graphical processing
units, and/or a packet monitoring functions. The system can switch
data packets associated with the data objects to the intelligent
routers, where the data objects are extracted from the data packets
and stored in cache memory. Upon receiving requests from client
devices, the intelligent routers can transmit the extracted data
objects from the cache memory to the client devices. The
intelligent routers can, in turn, be decommissioned subsequent to
transmission of the extracted data objects.
[0089] The communication device 600 can comprise a wireline and/or
wireless transceiver 602 (herein transceiver 602), a user interface
(UI) 604, a power supply 614, a location receiver 616, a motion
sensor 618, an orientation sensor 620, and a controller 606 for
managing operations thereof. The transceiver 602 can support
short-range or long-range wireless access technologies such as
Bluetooth.RTM., ZigBee.RTM., WiFi, DECT, or cellular communication
technologies, just to mention a few (Bluetooth.RTM. and ZigBee.RTM.
are trademarks registered by the Bluetooth.RTM. Special Interest
Group and the ZigBee.RTM. Alliance, respectively). Cellular
technologies can include, for example, CDMA-1X, UMTS/HSDPA,
GSM/GPRS, TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, as well as other next
generation wireless communication technologies as they arise. The
transceiver 602 can also be adapted to support circuit-switched
wireline access technologies (such as PSTN), packet-switched
wireline access technologies (such as TCP/IP, VoIP, etc.), and
combinations thereof.
[0090] The UI 604 can include a depressible or touch-sensitive
keypad 608 with a navigation mechanism such as a roller ball, a
joystick, a mouse, or a navigation disk for manipulating operations
of the communication device 600. The keypad 608 can be an integral
part of a housing assembly of the communication device 600 or an
independent device operably coupled thereto by a tethered wireline
interface (such as a USB cable) or a wireless interface supporting
for example Bluetooth.RTM.. The keypad 608 can represent a numeric
keypad commonly used by phones, and/or a QWERTY keypad with
alphanumeric keys. The UI 604 can further include a display 610
such as monochrome or color LCD (Liquid Crystal Display), OLED
(Organic Light Emitting Diode) or other suitable display technology
for conveying images to an end user of the communication device
600. In an embodiment where the display 610 is touch-sensitive, a
portion or all of the keypad 608 can be presented by way of the
display 610 with navigation features.
[0091] The display 610 can use touch screen technology to also
serve as a user interface for detecting user input. As a touch
screen display, the communication device 600 can be adapted to
present a user interface having graphical user interface (GUI)
elements that can be selected by a user with a touch of a finger.
The display 610 can be equipped with capacitive, resistive or other
forms of sensing technology to detect how much surface area of a
user's finger has been placed on a portion of the touch screen
display. This sensing information can be used to control the
manipulation of the GUI elements or other functions of the user
interface. The display 610 can be an integral part of the housing
assembly of the communication device 600 or an independent device
communicatively coupled thereto by a tethered wireline interface
(such as a cable) or a wireless interface.
[0092] The UI 604 can also include an audio system 612 that
utilizes audio technology for conveying low volume audio (such as
audio heard in proximity of a human ear) and high volume audio
(such as speakerphone for hands free operation). The audio system
612 can further include a microphone for receiving audible signals
of an end user. The audio system 612 can also be used for voice
recognition applications. The UI 604 can further include an image
sensor 613 such as a charged coupled device (CCD) camera for
capturing still or moving images.
[0093] The power supply 614 can utilize common power management
technologies such as replaceable and rechargeable batteries, supply
regulation technologies, and/or charging system technologies for
supplying energy to the components of the communication device 600
to facilitate long-range or short-range portable communications.
Alternatively, or in combination, the charging system can utilize
external power sources such as DC power supplied over a physical
interface such as a USB port or other suitable tethering
technologies.
[0094] The location receiver 616 can utilize location technology
such as a global positioning system (GPS) receiver capable of
assisted GPS for identifying a location of the communication device
600 based on signals generated by a constellation of GPS
satellites, which can be used for facilitating location services
such as navigation. The motion sensor 618 can utilize motion
sensing technology such as an accelerometer, a gyroscope, or other
suitable motion sensing technology to detect motion of the
communication device 600 in three-dimensional space. The
orientation sensor 620 can utilize orientation sensing technology
such as a magnetometer to detect the orientation of the
communication device 600 (north, south, west, and east, as well as
combined orientations in degrees, minutes, or other suitable
orientation metrics).
[0095] The communication device 600 can use the transceiver 602 to
also determine a proximity to a cellular, WiFi, Bluetooth.RTM., or
other wireless access points by sensing techniques such as
utilizing a received signal strength indicator (RSSI) and/or signal
time of arrival (TOA) or time of flight (TOF) measurements. The
controller 606 can utilize computing technologies such as a
microprocessor, a digital signal processor (DSP), programmable gate
arrays, application specific integrated circuits, and/or a video
processor with associated storage memory such as Flash, ROM, RAM,
SRAM, DRAM or other storage technologies for executing computer
instructions, controlling, and processing data supplied by the
aforementioned components of the communication device 600.
[0096] Other components not shown in FIG. 6 can be used in one or
more embodiments of the subject disclosure. For instance, the
communication device 600 can include a slot for adding or removing
an identity module such as a Subscriber Identity Module (SIM) card
or Universal Integrated Circuit Card (UICC). SIM or UICC cards can
be used for identifying subscriber services, executing programs,
storing subscriber data, and so on.
[0097] The terms "first," "second," "third," and so forth, as used
in the claims, unless otherwise clear by context, is for clarity
only and doesn't otherwise indicate or imply any order in time. For
instance, "a first determination," "a second determination," and "a
third determination," does not indicate or imply that the first
determination is to be made before the second determination, or
vice versa, etc.
[0098] In the subject specification, terms such as "store,"
"storage," "data store," data storage," "database," and
substantially any other information storage component relevant to
operation and functionality of a component, refer to "memory
components," or entities embodied in a "memory" or components
comprising the memory. It will be appreciated that the memory
components described herein can be either volatile memory or
nonvolatile memory, or can comprise both volatile and nonvolatile
memory, by way of illustration, and not limitation, volatile
memory, non-volatile memory, disk storage, and memory storage.
Further, nonvolatile memory can be included in read only memory
(ROM), programmable ROM (PROM), electrically programmable ROM
(EPROM), electrically erasable ROM (EEPROM), or flash memory.
Volatile memory can comprise random access memory (RAM), which acts
as external cache memory. By way of illustration and not
limitation, RAM is available in many forms such as synchronous RAM
(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data
rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM
(SLDRAM), and direct Rambus RAM (DRRAM). Additionally, the
disclosed memory components of systems or methods herein are
intended to comprise, without being limited to comprising, these
and any other suitable types of memory.
[0099] Moreover, it will be noted that the disclosed subject matter
can be practiced with other computer system configurations,
comprising single-processor or multiprocessor computer systems,
mini-computing devices, mainframe computers, as well as personal
computers, hand-held computing devices (e.g., PDA, phone,
smartphone, watch, tablet computers, netbook computers, etc.),
microprocessor-based or programmable consumer or industrial
electronics, and the like. The illustrated aspects can also be
practiced in distributed computing environments where tasks are
performed by remote processing devices that are linked through a
communications network; however, some if not all aspects of the
subject disclosure can be practiced on stand-alone computers. In a
distributed computing environment, program modules can be located
in both local and remote memory storage devices.
[0100] In one or more embodiments, information regarding use of
services can be generated including services being accessed, media
consumption history, user preferences, and so forth. This
information can be obtained by various methods including user
input, detecting types of communications (e.g., video content vs.
audio content), analysis of content streams, sampling, and so
forth. The generating, obtaining and/or monitoring of this
information can be responsive to an authorization provided by the
user. In one or more embodiments, an analysis of data can be
subject to authorization from user(s) associated with the data,
such as an opt-in, an opt-out, acknowledgement requirements,
notifications, selective authorization based on types of data, and
so forth.
[0101] Some of the embodiments described herein can also employ
artificial intelligence (AI) to facilitate automating one or more
features described herein. The embodiments (e.g., in connection
with automatically identifying acquired cell sites that provide a
maximum value/benefit after addition to an existing communication
network) can employ various AI-based schemes for carrying out
various embodiments thereof. Moreover, the classifier can be
employed to determine a ranking or priority of each cell site of
the acquired network. A classifier is a function that maps an input
attribute vector, x=(x1, x2, x3, x4, . . . , xn), to a confidence
that the input belongs to a class, that is, f(x)=confidence
(class). Such classification can employ a probabilistic and/or
statistical-based analysis (e.g., factoring into the analysis
utilities and costs) to determine or infer an action that a user
desires to be automatically performed. A support vector machine
(SVM) is an example of a classifier that can be employed. The SVM
operates by finding a hypersurface in the space of possible inputs,
which the hypersurface attempts to split the triggering criteria
from the non-triggering events. Intuitively, this makes the
classification correct for testing data that is near, but not
identical to training data. Other directed and undirected model
classification approaches comprise, e.g., naive Bayes, Bayesian
networks, decision trees, neural networks, fuzzy logic models, and
probabilistic classification models providing different patterns of
independence can be employed. Classification as used herein also is
inclusive of statistical regression that is utilized to develop
models of priority.
[0102] As will be readily appreciated, one or more of the
embodiments can employ classifiers that are explicitly trained
(e.g., via a generic training data) as well as implicitly trained
(e.g., via observing UE behavior, operator preferences, historical
information, receiving extrinsic information). For example, SVMs
can be configured via a learning or training phase within a
classifier constructor and feature selection module. Thus, the
classifier(s) can be used to automatically learn and perform a
number of functions, including but not limited to determining
according to predetermined criteria which of the acquired cell
sites will benefit a maximum number of subscribers and/or which of
the acquired cell sites will add minimum value to the existing
communication network coverage, etc.
[0103] As used in some contexts in this application, in some
embodiments, the terms "component," "system" and the like are
intended to refer to, or comprise, a computer-related entity or an
entity related to an operational apparatus with one or more
specific functionalities, wherein the entity can be either
hardware, a combination of hardware and software, software, or
software in execution. As an example, a component may be, but is
not limited to being, a process running on a processor, a
processor, an object, an executable, a thread of execution,
computer-executable instructions, a program, and/or a computer. By
way of illustration and not limitation, both an application running
on a server and the server can be a component. One or more
components may reside within a process and/or thread of execution
and a component may be localized on one computer and/or distributed
between two or more computers. In addition, these components can
execute from various computer readable media having various data
structures stored thereon. The components may communicate via local
and/or remote processes such as in accordance with a signal having
one or more data packets (e.g., data from one component interacting
with another component in a local system, distributed system,
and/or across a network such as the Internet with other systems via
the signal). As another example, a component can be an apparatus
with specific functionality provided by mechanical parts operated
by electric or electronic circuitry, which is operated by a
software or firmware application executed by a processor, wherein
the processor can be internal or external to the apparatus and
executes at least a part of the software or firmware application.
As yet another example, a component can be an apparatus that
provides specific functionality through electronic components
without mechanical parts, the electronic components can comprise a
processor therein to execute software or firmware that confers at
least in part the functionality of the electronic components. While
various components have been illustrated as separate components, it
will be appreciated that multiple components can be implemented as
a single component, or a single component can be implemented as
multiple components, without departing from example
embodiments.
[0104] Further, the various embodiments can be implemented as a
method, apparatus or article of manufacture using standard
programming and/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
program accessible from any computer-readable device or
computer-readable storage/communications media. For example,
computer readable storage media can include, but are not limited
to, magnetic storage devices (e.g., hard disk, floppy disk,
magnetic strips), optical disks (e.g., compact disk (CD), digital
versatile disk (DVD)), smart cards, and flash memory devices (e.g.,
card, stick, key drive). Of course, those skilled in the art will
recognize many modifications can be made to this configuration
without departing from the scope or spirit of the various
embodiments.
[0105] In addition, the words "example" and "exemplary" are used
herein to mean serving as an instance or illustration. Any
embodiment or design described herein as "example" or "exemplary"
is not necessarily to be construed as preferred or advantageous
over other embodiments or designs. Rather, use of the word example
or exemplary is intended to present concepts in a concrete fashion.
As used in this application, the term "or" is intended to mean an
inclusive "or" rather than an exclusive "or". That is, unless
specified otherwise or clear from context, "X employs A or B" is
intended to mean any of the natural inclusive permutations. That
is, if X employs A; X employs B; or X employs both A and B, then "X
employs A or B" is satisfied under any of the foregoing instances.
In addition, the articles "a" and "an" as used in this application
and the appended claims should generally be construed to mean "one
or more" unless specified otherwise or clear from context to be
directed to a singular form.
[0106] Moreover, terms such as "user equipment," "mobile station,"
"mobile," subscriber station," "access terminal," "terminal,"
"handset," "mobile device" (and/or terms representing similar
terminology) can refer to a wireless device utilized by a
subscriber or user of a wireless communication service to receive
or convey data, control, voice, video, sound, gaming or
substantially any data-stream or signaling-stream. The foregoing
terms are utilized interchangeably herein and with reference to the
related drawings.
[0107] Furthermore, the terms "user," "subscriber," "customer,"
"consumer" and the like are employed interchangeably throughout,
unless context warrants particular distinctions among the terms. It
should be appreciated that such terms can refer to human entities
or automated components supported through artificial intelligence
(e.g., a capacity to make inference based, at least, on complex
mathematical formalisms), which can provide simulated vision, sound
recognition and so forth.
[0108] As employed herein, the term "processor" can refer to
substantially any computing processing unit or device comprising,
but not limited to comprising, single-core processors;
single-processors with software multithread execution capability;
multi-core processors; multi-core processors with software
multithread execution capability; multi-core processors with
hardware multithread technology; parallel platforms; and parallel
platforms with distributed shared memory. Additionally, a processor
can refer to an integrated circuit, an application specific
integrated circuit (ASIC), a digital signal processor (DSP), a
field programmable gate array (FPGA), a programmable logic
controller (PLC), a complex programmable logic device (CPLD), a
discrete gate or transistor logic, discrete hardware components or
any combination thereof designed to perform the functions described
herein. Processors can exploit nano-scale architectures such as,
but not limited to, molecular and quantum-dot based transistors,
switches and gates, in order to optimize space usage or enhance
performance of user equipment. A processor can also be implemented
as a combination of computing processing units.
[0109] As used herein, terms such as "data storage," data storage,"
"database," and substantially any other information storage
component relevant to operation and functionality of a component,
refer to "memory components," or entities embodied in a "memory" or
components comprising the memory. It will be appreciated that the
memory components or computer-readable storage media, described
herein can be either volatile memory or nonvolatile memory or can
include both volatile and nonvolatile memory.
[0110] What has been described above includes mere examples of
various embodiments. It is, of course, not possible to describe
every conceivable combination of components or methodologies for
purposes of describing these examples, but one of ordinary skill in
the art can recognize that many further combinations and
permutations of the present embodiments are possible. Accordingly,
the embodiments disclosed and/or claimed herein are intended to
embrace all such alterations, modifications and variations that
fall within the spirit and scope of the appended claims.
Furthermore, to the extent that the term "includes" is used in
either the detailed description or the claims, such term is
intended to be inclusive in a manner similar to the term
"comprising" as "comprising" is interpreted when employed as a
transitional word in a claim.
[0111] 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.
[0112] As may also be used herein, the term(s) "operably coupled
to", "coupled to", and/or "coupling" includes direct coupling
between items and/or indirect coupling between items via one or
more intervening items. Such items and intervening items include,
but are not limited to, junctions, communication paths, components,
circuit elements, circuits, functional blocks, and/or devices. As
an example of indirect coupling, a signal conveyed from a first
item to a second item may be modified by one or more intervening
items by modifying the form, nature or format of information in a
signal, while one or more elements of the information in the signal
are nevertheless conveyed in a manner than can be recognized by the
second item. In a further example of indirect coupling, an action
in a first item can cause a reaction on the second item, as a
result of actions and/or reactions in one or more intervening
items.
[0113] Although specific embodiments have been illustrated and
described herein, it should be appreciated that any arrangement
which achieves the same or similar purpose may be substituted for
the embodiments described or shown by the subject disclosure. The
subject disclosure is intended to cover any and all adaptations or
variations of various embodiments. Combinations of the above
embodiments, and other embodiments not specifically described
herein, can be used in the subject disclosure. For instance, one or
more features from one or more embodiments can be combined with one
or more features of one or more other embodiments. In one or more
embodiments, features that are positively recited can also be
negatively recited and excluded from the embodiment with or without
replacement by another structural and/or functional feature. The
steps or functions described with respect to the embodiments of the
subject disclosure can be performed in any order. The steps or
functions described with respect to the embodiments of the subject
disclosure can be performed alone or in combination with other
steps or functions of the subject disclosure, as well as from other
embodiments or from other steps that have not been described in the
subject disclosure. Further, more than or less than all of the
features described with respect to an embodiment can also be
utilized.
* * * * *