U.S. patent application number 17/205785 was filed with the patent office on 2022-09-22 for systems and methods for optimizing provision of high latency content by a network.
This patent application is currently assigned to Verizon Patent and Licensing Inc.. The applicant listed for this patent is Verizon Patent and Licensing Inc.. Invention is credited to Arda AKSU, Patricia R. CHANG, Suzann HUA, Lalit R. KOTECHA, Maria G. LAM, Donna L. POLEHN, Vishwanath RAMAMURTHI, Jin YANG.
Application Number | 20220303349 17/205785 |
Document ID | / |
Family ID | 1000006575880 |
Filed Date | 2022-09-22 |
United States Patent
Application |
20220303349 |
Kind Code |
A1 |
POLEHN; Donna L. ; et
al. |
September 22, 2022 |
SYSTEMS AND METHODS FOR OPTIMIZING PROVISION OF HIGH LATENCY
CONTENT BY A NETWORK
Abstract
A multi-access edge computing device may receive historical
content data associated with a content application of a user
equipment and may process the historical content data, with a
machine learning model, to identify content to cache for the user
equipment. The multi-access edge computing device may provide, to a
content provider device, a request for the content to cache and may
receive, from the content provider device, the content to cache
based on the request for the content to cache. The multi-access
edge computing device may process the content to cache, with a
document object model and a browser object model, to generate
intermediary content that corresponds to the content to cache. The
multi-access edge computing device may store the intermediary
content in a data structure associated with the multi-access edge
computing device.
Inventors: |
POLEHN; Donna L.; (Mercer
Island, WA) ; LAM; Maria G.; (Oakland, CA) ;
YANG; Jin; (Orinda, CA) ; KOTECHA; Lalit R.;
(San Ramon, CA) ; RAMAMURTHI; Vishwanath; (San
Ramon, CA) ; CHANG; Patricia R.; (San Ramon, CA)
; HUA; Suzann; (Beverly Hills, CA) ; AKSU;
Arda; (Lafayette, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Verizon Patent and Licensing Inc. |
Basking Ridge |
NJ |
US |
|
|
Assignee: |
Verizon Patent and Licensing
Inc.
Basking Ridge
NJ
|
Family ID: |
1000006575880 |
Appl. No.: |
17/205785 |
Filed: |
March 18, 2021 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 20/00 20190101;
H04L 67/568 20220501; H04L 67/306 20130101; H04L 43/0852
20130101 |
International
Class: |
H04L 29/08 20060101
H04L029/08; H04L 12/26 20060101 H04L012/26; G06N 20/00 20060101
G06N020/00 |
Claims
1. A method, comprising: receiving, by a multi-access edge
computing device, historical content data associated with a content
application of a user equipment; processing, by the multi-access
edge computing device, the historical content data, with a machine
learning model, to identify content to cache for the user
equipment; providing, by the multi-access edge computing device and
to a content provider device, a request for the content to cache;
receiving, by the multi-access edge computing device and from the
content provider device, the content to cache based on the request
for the content to cache, wherein the content to cache includes one
or more of: content accessed by the content application of the user
equipment and that fails to satisfy a latency threshold, or content
associated with latencies satisfying one or more criteria;
generating, by the multi-access edge computing device, intermediary
content corresponding to the content to cache based on receiving
the content to cache; and storing, by the multi-access edge
computing device, the intermediary content in a data structure
associated with the multi-access edge computing device.
2. The method of claim 1, further comprising: receiving, from the
user equipment, a request for particular content included in the
content to cache; identifying, in the data structure and based on
the request for the particular content, particular intermediary
content that corresponds to the particular content; and providing
the particular intermediary content to the user equipment.
3. The method of claim 1, wherein receiving the historical content
data associated with the content application of the user equipment
comprises one or more of: receiving the historical content data
from the user equipment; or receiving the historical content data
from the content provider device.
4. The method of claim 1, wherein generating the intermediary
content includes: processing the content to cache with a document
object model and a browser object model to generate the
intermediary content.
5. The method of claim 1, further comprising: receiving network
data identifying a bandwidth associated with an access network of
the user equipment and a latency associated with the access
network, wherein processing the historical content data, with the
machine learning model, to identify the content to cache comprises:
processing the historical content data and the network data, with
the machine learning model, to identify the content to cache.
6. The method of claim 1, wherein the content to cache further
includes one or more of: a percentage of content accessed by the
content application of the user equipment, based on latencies
associated with the content, or content accessed by the content
application of the user equipment and that is identical to content
accessed by other user equipment.
7. The method of claim 1, wherein receiving the content to cache
comprises: receiving the content to cache during a time period
identified by the content provider device.
8. A multi-access edge computing device, comprising: one or more
processors configured to: receive historical content data
associated with a content application of a user equipment; receive
network data identifying a bandwidth associated with an access
network associated with the user equipment and a latency associated
with the access network; process the historical content data and
the network data, with a machine learning model, to identify
content to cache for the user equipment; provide, to a content
provider device, a request for the content to cache; receive, from
the content provider device, the content to cache based on the
request for the content to cache, wherein the content to cache
includes one or more of: content accessed by the content
application of the user equipment and that fails to satisfy a
latency threshold, or content associated with latencies satisfying
one or more criteria; process the content to cache, with a document
object model and a browser object model, to generate intermediary
content that corresponds to the content to cache; and store the
intermediary content in a data structure associated with the
multi-access edge computing device.
9. The multi-access edge computing device of claim 8, wherein the
one or more processors, when processing the historical content
data, with the machine learning model, to identify the content to
cache, are configured to: create a latency table that identifies
the content accessed by the content application of the user
equipment and latencies associated with the content; and identify,
as the content to cache, a percentage of the content identified in
the latency table, based on the latencies associated with the
content.
10. The multi-access edge computing device of claim 8, wherein the
intermediary content includes a format that requires less
processing by the content application of the user equipment
relative to a format of the content to cache.
11. The multi-access edge computing device of claim 8, wherein the
multi-access edge computing device is located in an access network
associated with the user equipment and the multi-access edge
computing device is one of a plurality of multi-access edge
computing devices that is geographically closest to the user
equipment.
12. The multi-access edge computing device of claim 8, wherein the
user equipment has access to a plurality of multi-access edge
computing devices and the multi-access edge computing device is one
of the plurality of multi-access edge computing devices that is
geographically closest to the user equipment.
13. The multi-access edge computing device of claim 8, wherein the
one or more processors are further configured to: receive
additional historical content data associated with a plurality of
other user equipment; and wherein the one or more processors, when
processing the historical content data, with the machine learning
model, to identify the content to cache for the user equipment, are
configured to: process the historical content data and the
additional historical content data, with the machine learning
model, to identify the content to cache for the user equipment.
14. The multi-access edge computing device of claim 8, wherein the
content application includes a browser application.
15. A non-transitory computer-readable medium storing a set of
instructions, the set of instructions comprising: one or more
instructions that, when executed by one or more processors of a
multi-access edge computing device, cause the multi-access edge
computing device to: receive historical content data associated
with a content application of a user equipment; process the
historical content data, with a machine learning model, to identify
content to cache for the user equipment; provide, to a content
provider device, a request for the content to cache; receive, from
the content provider device, the content to cache based on the
request for the content to cache, wherein the content to cache
includes one or more of: content accessed by the content
application of the user equipment and that fails to satisfy a
latency threshold, or content associated with latencies satisfying
one or more criteria; process the content to cache, with a document
object model and a browser object model, to generate intermediary
content that corresponds to the content to cache; store the
intermediary content in a data structure associated with the
multi-access edge computing device; receive, from the user
equipment, a request for particular content included in the content
to cache; identify, in the data structure and based on the request
for the particular content, particular intermediary content that
corresponds to the particular content; and provide the particular
intermediary content to the user equipment.
16. The non-transitory computer-readable medium of claim 15,
wherein the historical content data is received from one or more of
the user equipment or the content provider device.
17. The non-transitory computer-readable medium of claim 15,
wherein the one or more instructions further cause the multi-access
edge computing device to: receive network data identifying
bandwidth associated with an access network and a latency
associated with the access network; and wherein the one or more
instructions, that cause the multi-access edge computing device to
process the historical content data, with the machine learning
model, to identify the content to cache, cause the multi-access
edge computing device to: process the historical content data and
the network data, with the machine learning model, to identify the
content to cache.
18. The non-transitory computer-readable medium of claim 15,
wherein the one or more instructions, that cause the multi-access
edge computing device to process the historical content data, with
the machine learning model, to identify the content to cache, cause
the multi-access edge computing device to: create a latency table
that identifies the content accessed by the content application of
the user equipment and latencies associated with the content; and
identify, as the content to cache, a percentage of the content
identified in the latency table, based on the latencies associated
with the content.
19. The non-transitory computer-readable medium of claim 15,
wherein the intermediary content includes a format that requires
less processing by the content application of the user equipment
relative to a format of the content to cache.
20. The non-transitory computer-readable medium of claim 15,
wherein the one or more instructions further cause the multi-access
edge computing device to: receive additional historical content
data associated with a plurality of other user equipment; and
wherein the one or more instructions, that cause the multi-access
edge computing device to process the historical content data, with
the machine learning model, to identify the content to cache for
the user equipment, cause the multi-access edge computing device
to: process the historical content data and the additional
historical content data, with the machine learning model, to
identify the content to cache for the user equipment.
Description
BACKGROUND
[0001] Multi-access edge computing (MEC) is a technology that
provides computing resources at an edge of a network. A MEC device
may support computing requirements of user equipment (UEs) that are
within an area of service of the MEC device.
BRIEF DESCRIPTION OF THE DRAWINGS
[0002] FIGS. 1A-1F are diagrams of an example associated with
optimizing provision of high latency content by a network.
[0003] FIG. 2 is a diagram illustrating an example of training and
using a machine learning model in connection with optimizing
provision of high latency content by a network.
[0004] FIG. 3 is a diagram of an example environment in which
systems and/or methods described herein may be implemented.
[0005] FIG. 4 is a diagram of example components of one or more
devices of FIG. 3.
[0006] FIG. 5 is a flowchart of an example process for optimizing
provision of high latency content by a network.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0007] The following detailed description of example
implementations refers to the accompanying drawings. The same
reference numbers in different drawings may identify the same or
similar elements.
[0008] MEC devices may be provisioned at multiple locations within
a network, such as a cellular network. For example, a MEC device
may be provisioned within a core network, a radio access network
(RAN), and/or a base station, among other examples. Because MEC
devices are provisioned within a network, rather than a remote data
center where cloud computing resources may be provisioned, a
measure of latency between a MEC device and a UE may be lower than
a measure of latency between a cloud computing resource and the UE.
Low latency may be required for certain applications of the UE,
such as augmented reality applications, virtual reality
applications, vehicle control applications, and/or machine control
applications, among other examples. MEC devices may, therefore, be
more suitable to provide computing resources for such
applications.
[0009] Some content (e.g., websites) provided to a UE may have high
latency regardless of how fast an access network is for the UE. For
example, a first website may have low latency and be quickly
displayed on the UE, whereas a second website may have high latency
and be slowly displayed on the UE. A content application (e.g., a
web browser) utilized by the UE to view the content is inherently
slow and download limited. Thus, utilizing MEC devices simply to
provide access to the second website may not improve the high
latency associated with the second website.
[0010] Some implementations described herein provide a network
device (e.g., a MEC device) that optimizes provision of high
latency content by a network. For example, the MEC device may
receive historical content data associated with a content
application of a user equipment and may process the historical
content data, with a machine learning model, to identify content to
cache for the user equipment. The MEC device may provide, to a
content provider device, a request for the content to cache and may
receive, from the content provider device, the content to cache
based on the request for the content to cache. The MEC device may
process the content to cache, with a document object model and a
browser object model, to generate intermediary content that
corresponds to the content to cache and may store the intermediary
content in a data structure associated with the MEC device.
[0011] In this way, the MEC device may optimize provision of high
latency content by a network. For example, the MEC device may
retrieve high latency content that is historically utilized by the
UE and may process the high latency content, with a document object
model and a browser object model, to generate and cache
intermediary content format that may be displayed by the UE. When
the UE accesses content corresponding to the intermediary content,
the MEC device may quickly stream the intermediary content to the
UE. Thus, the MEC device may conserve computing resources (e.g.,
processing resources, memory resources, communication resources,
and/or the like), networking resources, and other resources that
would have otherwise been consumed by providing the high latency
content to the UE, positioning network resources to attempt to
improve provision of the high latency content, unsuccessfully
diverting network resources from low latency content to service the
high latency content, and/or the like.
[0012] FIGS. 1A-1F are diagrams of an example 100 associated with
optimizing provision of high latency content by a network. As shown
in FIGS. 1A-1F, example 100 includes a base station 105 associated
with a UE 110, a MEC device 115, a core network 120, a data network
125, and a content provider device 130. Further details of the base
station 105, the UE 110, the MEC device 115, the core network 120,
the data network 125, and the content provider device 130 are
provided below.
[0013] As shown in FIG. 1A, and by reference number 135, the MEC
device 115 receives, from a UE 110, historical content data
associated with a content application of the UE 110. The MEC device
115 may be included in a MEC environment and/or an access network
associated with the UE 110. In a MEC environment, computing is
enabled by a network architecture that provides computing
capabilities to a connected device (e.g., UE 110) via computing
platforms at or near an edge of a network (e.g., a wireless
communication network). Accordingly, because a MEC environment may
provide computing at or near the edge of the network, increased
performance may be achieved over networks in which computing is
performed topologically and/or physically further from a connected
device. For example, the MEC environment may offer improved
performance due to less traffic and/or congestion between the
connected device and the computing node(s), less latency (due to
closer proximity to the connected device), increased flexibility
(due to a greater amount of computing node(s)), and/or the
like.
[0014] In some implementations, the UE 110 has access to a
plurality of MEC devices included in the MEC environment. The UE
110 may determine that the MEC device 115 is the geographically
closest MEC device and/or a logically closest (e.g., having a
fewest number of devices between the MEC device 115 and the UE 110
along a route which the historical content data is transmitted))
MEC device relative to the other MEC devices of the plurality of
MEC devices. The UE 110 may provide the historical content to the
MEC device 115 based on the MEC device 115 being the geographically
and/or logically closest MEC device.
[0015] The content application may include a browser application
associated with a web browser, a streaming media application
associated with a media player and/or another type of application
configured to receive media (e.g., audio, video, and/or the like)
via a network and provide the media to a user of the UE 110, and/or
another type of application configured to receive content (e.g.,
web pages, content associated with an online application, content
associated with online gaming, audio, video, documents, and/or the
like) via a network and provide the content to a user of the UE 110
(e.g., via an output device associated with the UE 110, such as a
display and/or a speaker, among other examples). In some
implementations, the content application includes a browser
extension associated with a web browser of the UE 110.
Alternatively, and/or additionally, the content application may
include an application configured to determine where and/or how to
display content based on limitations of an application (e.g., a web
browser and/or a media player, among other examples) with which the
content application is associated.
[0016] The historical content data may include information
identifying content accessed by and/or provided to a user of the UE
110 over a time period (e.g., one day, one week, one month, one
year, and/or the like). In some implementations, the content
identified by the historical content data includes content most
frequently accessed by and/or provided to the user during the time
period relative to other content accessed by and/or provided to the
user during the time period, content identified by the user (e.g.,
content included in a favorites list of a web browser), content
associated with an application (e.g., an online application, an
online gaming application, and/or the like) utilized by the user
during the time period, and/or the like.
[0017] In some implementations, the content application, the MEC
device 115, and/or the content provider device 130 may monitor a
latency associated with receiving and/or providing content during
the time period and the historical content data may identify
received and/or provided content associated with a higher latency
relative to latencies associated with other content received and/or
provided during the time period. The latency may include a network
latency, a latency associated with loading and/or rendering the
content by the UE 110, and/or the like.
[0018] In some implementations, the content application determines
the latency associated with loading and/or rending the content
based on an amount of time required to load and/or render the
content. The content application may determine that the content is
associated with a high latency when the amount of time satisfies
one or more criteria (e.g., satisfies a time threshold, crosses a
time threshold, and/or the like). In some implementations, the MEC
device 115 determines a latency associated with rendering and/or
providing content based on a document object model and/or a browser
object model associated with the content. The MEC device 115 may
estimate a complexity associated with the document object model
and/or the browser object model. The MEC device 115 may determine
that an amount of time required to render content increases based
on a complexity associated with a document object model and/or a
browser object associated with the content and, therefore, the MEC
device 115 may determine that content is associated with a high
latency when the estimated complexity satisfies one or more
criteria (e.g., satisfies one or more thresholds, crosses one or
more thresholds, and/or the like).
[0019] Alternatively, and/or additionally, the historical content
data may include content associated with latency that satisfies one
or more criteria (e.g., greater than a threshold, equal to a
threshold, less than a threshold, crossing a threshold, and/or the
like). The UE 110 (e.g., the content application) may provide the
historical content to the MEC device 115 periodically (e.g., daily,
weekly, monthly, and/or the like), based on a receiving a request
for the historical content data from the MEC device 115, and/or
based on an occurrence of an event (e.g., expiration of the time
period, content being downloaded a particular quantity of times,
and/or a latency associated with content satisfying a latency
threshold, among other examples).
[0020] Alternatively, and/or additionally, the MEC device 115 may
receive historical content data from content provider device 130.
The content provider device 130 may monitor a quantity of times
content provided by the content provider device 130 is downloaded
via the MEC environment, via the MEC device 115, by the UE 110, by
UEs operating in a geographical area associated with the MEC device
115, and/or the like. The content provider device 130 may provide
historical content data provided by the content provider device 130
during a time period and/or satisfying one or more criteria (e.g.,
content downloaded a threshold quantity of times, content
downloaded more frequently relative to other content provided by
the content provider device 130, content associated with a latency
that satisfies a latency threshold, and/or the like). The content
provider device 130 may provide the historical content to the MEC
device 115 periodically (e.g., daily, weekly, monthly, and/or the
like), based on receiving a request for the historical content data
from the MEC device 115, and/or based on an occurrence of an event
(e.g., expiration of the time period, content being downloaded a
particular quantity of times, and/or a latency associated with
content satisfying a latency threshold, among other examples).
[0021] As shown by reference number 140, the MEC device 115
processes the historical content data, with a machine learning
model, to identify content to cache for the UE 110. The machine
learning model may include a heuristic prediction model. The
machine learning model may receive the historical content data as
an input and may generate an output indicating content to cache for
the UE 110 and a confidence score that reflects a measure of
confidence that the content to cache is accurately identified.
[0022] In some implementations, the historical content data
identifies content that is currently being provided to a user via
the UE 110. The MEC device 115 may process the historical data,
with the machine learning model, to determine the content to cache
based on the content currently being provided to the user. For
example, the MEC device 115 may identify particular content
requested by the UE 110 after receiving the content currently being
provided to the user. The MEC device 115 may determine, based on
the historical content data, that a quantity of times the
particular content has been requested satisfies one or more
criteria. The MEC device 115 may include the particular content in
the content to be cached based on the quantity of times the
particular content has been requested satisfying the one or more
criteria.
[0023] In some implementations, the MEC device 115 receives network
data identifying a bandwidth associated with an access network of
the UE, a latency associated with the access network, and/or the
like. The MEC device 115 may process the historical content data
and the network data, with the machine learning model, to identify
the content to cache. The content to cache may include content that
is accessed by the content application during a time period,
content that is accessed by the content application and fails to
satisfy a latency threshold, a percentage of content accessed by
the content application, content associated with latencies
satisfying one or more criteria, content accessed by the content
application that is accessed by a quantity of other UEs during a
time period, and/or the like.
[0024] In some implementations, the MEC device 115 creates a
latency table that identifies the content accessed by the content
application and latencies associated with the accessed content. The
MEC device 115 may identify a percentage of the content identified
in the latency table as the content to cache based on the latencies
associated with the content. For example, the MEC device 115 may
identify a percentage of the content identified in the latency
table associated with latencies satisfying one or more criteria, a
percentage of the content identified in the latency table
associated with latencies greater than latencies associated with
other content identified in the latency table, and/or the like.
[0025] In some implementations, the MEC device 115 may receive
additional historical content data associated with a plurality of
other UEs. The MEC device 115 may process the historical content
data and the additional historical content data, with the machine
learning model, to identify the content to cache for the UE 110.
For example, the MEC device 115 may identify particular content
requested by other UEs after receiving content currently being
provided to the UE 110. The MEC device 115 may determine, based on
the additional historical content data, that a quantity of times
the particular content has been requested satisfies one or more
criteria. The MEC device 115 may include the particular content in
the content to be cached based on the quantity of times the
particular content has been requested satisfying the one or more
criteria.
[0026] In some implementations, the MEC device 115 determines the
content to cache based on user input. For example, a user may input
information indicating particular content to cache (e.g.,
information identifying a particular web site), information
identifying a rule for determining content to cache (e.g.,
always/never cache particular content, always/never cache content
provided by a particular application, always/never cache a
particular type of content (e.g., web pages, streaming audio,
streaming video, and/or the like), and/or cache particular content
during a particular time period, among other examples).
[0027] In some implementations, the MEC device 115 trains the
machine learning model to determine the content to cache based on
historical training data. For example, the MEC device 115 may train
the machine learning model to determine the content to cache in a
manner similar to that described below with respect to FIG. 2.
Alternatively, and/or additionally, the MEC device 115 may receive
a trained machine learning model from another device (e.g., a
server device associated with a network associated with the MEC
device 115, a third-party server device, the content provider
device 130, and/or the like).
[0028] As shown in FIG. 1B, and by reference number 145, the MEC
device 115 provides, to the content provider device 130, a request
for the content to cache. The request for the content to cache may
include information identifying content to be provided by the
content provider device 130 and/or cached by the MEC device 115. In
some implementations, the request for the content to cache may
include information identifying content to be cached by another
device. For example, the request for the content to cache may
identify content to be cached by the UE 110, customer premises
equipment associated with the UE 110, a server device associated
with the UE 110, a data storage device associated with the UE 110
and/or the MEC device 115, and/or the like. The content provider
device 130 may receive the request for the content to cache and may
provide the content to cache to the MEC device 115 (and/or another
device indicated in the request for content to cache). As shown by
reference number 150, the MEC device 115 receives the content to
cache from the content provider device 130 based on the
request.
[0029] Alternatively, and/or additionally, the MEC device 115 may
receive the content to cache during a time period identified by the
content provider device 130. The content provider device 130 may
provide the content to the cache during a time period in which one
or more parameters associated with the content to cache satisfy one
or more criteria. For example, the content provider device 130 may
identify a time period in which a latency associated with the
content to cache satisfies a latency threshold, a quantity of
downloads of the content to cache satisfies a quantity threshold,
and/or the like. The content provider device 130 may identify the
time period based on historical data associated with downloading
the content to cache and/or real-time data associated with
downloading the content to cache. The content provider device 130
may provide the content to cache to the MEC device 115 during the
time period based on the one or more parameters associated with the
content to cache satisfying the one or more criteria.
[0030] As shown in FIG. 1C, and by reference number 155, the MEC
device 115 processes the content to cache, with a document object
model and a browser object model, to generate intermediary content.
The intermediary content may include a pre-rending of the content
to cache, by the MEC device 115, based on heuristic prediction
algorithms, the document object model, and/or the browser object
model.
[0031] The MEC device 115 may run an implementation of an
application (e.g., a web browser) and may render a document object
model and a browser object model associated with the content to
cache to an intermediary image format and/or an intermediary
content format, such as a scalable vector graphics (SVG) format, a
bitmap image file (BMP) format, a wave format, and/or the like.
[0032] The document object model may represent content associated
with a web page as objects that can be modified. For example, the
document object model may include a document object that enables
properties of a document (e.g., a web page) to be created, removed,
and/or modified.
[0033] The browser object model may be a browser-specific
convention that includes a set of objects exposed by a web browser.
For example, the browser object model may include a navigator
object configured to provide background information about the web
browser and/or an operating system associated with the UE 110, a
location object configured to read a current uniform resource
locator (URL) and/or to re-direct the web browser to a different
URL, and/or the like.
[0034] As shown by reference number 160, the MEC device 115 stores
the intermediary content in a data structure (e.g., a database, a
table, a list, and/or the like) associated with the MEC device 115.
In some implementations, the data structure is stored in a memory
of the MEC device 115. In some implementations, the data structure
is stored in a memory of a network device associated with the MEC
device 115. In some implementations, the data structure is stored
in a memory of customer premise equipment associated with the MEC
device 115 and/or the UE 110.
[0035] In some implementations, the MEC device 115 provides
information indicating that the intermediary content associated
with the content to be cached is stored in the data structure. The
content application may receive the information and may provide an
indication associated with the content to be cached to a user
associated with the UE 110. For example, content included in the
content to be cached may be identified in a favorites list
associated with a browser application. The content application may
cause an indication to be displayed in association with the content
in the favorites list based on the intermediary content being
stored in the data structure. For example, the content application
may cause a particular symbol, a particular icon, the word
"cached," and/or the like to be displayed next to a name of the
content in the favorites list, the content application may cause
the name of the content to be displayed in a particular color,
and/or the like.
[0036] In some implementations, the content application causes the
UE 110 to provide bypass information to the MEC device 115 based on
receiving the information indicating that the intermediary content
associated with the content to be cached is stored in the data
structure. The bypass information may include information
indicating that the intermediary content associated with the
content to be cached is not to be provided to the UE 110, is not to
be provided to UE 110 during a particular time period, that an
original form (e.g., hypertext markup language (HTML)) of the
particular content is to be obtained by the MEC device 115 and/or
provided to the UE 110, and/or the like. The content application
may cause the UE 110 to provide the bypass information based on a
user input and/or another type of information.
[0037] As shown in FIG. 1D, and by reference number 165, the MEC
device 115 receives, from the UE 110, a request for particular
content included in the content to cache. As shown by reference
number 170, the MEC device 115 identifies, in the data structure,
particular intermediary content that corresponds to the particular
content. The MEC device 115 may identify the particular
intermediary content based on the particular intermediary content
being associated with information identifying the particular
content (e.g., a uniform resource locator (URL) of a web page
associated with the particular content, an identifier associated
with a web page associated with the particular content, and/or the
like) and/or included in the request for the particular
content.
[0038] As shown in FIG. 1E, and by reference number 175, the MEC
device 115 provides the particular intermediary content to the UE
110. The content application may cause the intermediary content to
be provided to a user of the UE 110 based on the intermediary
content being provided to the UE 110.
[0039] In some implementations, the MEC device 115 causes the
particular content to be provided to the UE 110 by another device
(e.g., another UE 110, another MEC device 115, customer premises
equipment associated with the UE 110, and/or the like). The MEC
device 115 may determine that the particular content has been
provided to the other device during a previous time period (e.g.,
within the past five minutes, within the past ten minutes, and/or
the like) and/or is stored on the other device. The MEC device 115
may determine that the other device is able to provide the content
to the UE 110. For example, the MEC device 115 may determine that
the other device and the UE 110 are connected to a same network,
are within a particular distance from each other, are able to
communicate via direct communication, and/or the like. The MEC
device 115 may provide a message to the other device to cause the
other device to provide the particular content to the UE 110. The
message may include information identifying the particular content,
information identifying the UE 110, information indicating that the
other device is to provide the particular content to the UE 110 via
direct communication, and/or the like.
[0040] In some implementations, the MEC device 115 includes a
virtual machine instance. As shown in FIG. 1F, the virtual machine
instance may be associated with a virtual operating system, a
network interface, a machine learning model, and/a cache. The
virtual machine instance may utilize the virtual operating system,
the network interface, the machine learning model, and/or the cache
to receive the historical content data, identify the content to be
cached based on the historical content data, request the content to
be cached, receive the content to be cached, generate the
intermediary content, receive the request for the particular
content, identify the particular intermediary content, and/or
provide the particular intermediary content to the UE 110, in a
manner similar to that described above.
[0041] In this way, the MEC device 115 may optimize provision of
high latency content by a network. For example, the MEC device 115
may retrieve high latency content that is historically utilized by
the UE 110 and may process the high latency content, with a
document object model and a browser object model, to generate and
cache an intermediary content that may be displayed by the UE 110.
When the UE 110 accesses content corresponding to the intermediary
content, the MEC device 115 may quickly stream the intermediary
content to the UE 110. Thus, the MEC device 115 may conserve
computing resources, networking resources, and other resources that
would have otherwise been consumed by providing the high latency
content, positioning network resources to attempt to improve
provision of the high latency content, unsuccessfully diverting
network resources from low latency content to service the high
latency content, and/or the like.
[0042] As indicated above, FIGS. 1A-1F are provided as an example.
Other examples may differ from what is described with regard to
FIGS. 1A-1F. The number and arrangement of devices shown in FIGS.
1A-1F are provided as an example. In practice, there may be
additional devices, fewer devices, different devices, or
differently arranged devices than those shown in FIGS. 1A-1F.
Furthermore, two or more devices shown in FIGS. 1A-1F may be
implemented within a single device, or a single device shown in
FIGS. 1A-1F may be implemented as multiple, distributed devices.
Additionally, or alternatively, a set of devices (e.g., one or more
devices) shown in FIGS. 1A-1F may perform one or more functions
described as being performed by another set of devices shown in
FIGS. 1A-1F.
[0043] FIG. 2 is a diagram illustrating an example 200 of training
and using a machine learning model in connection with optimizing
provision of high latency content by a network. The machine
learning model training and usage described herein may be performed
using a machine learning system. The machine learning system may
include or may be included in a computing device, a server, a cloud
computing environment, and/or the like, such as the MEC device 115
described in more detail elsewhere herein.
[0044] As shown by reference number 205, a machine learning model
may be trained using a set of observations. The set of observations
may be obtained from historical data, such as data gathered during
one or more processes described herein. In some implementations,
the machine learning system may receive the set of observations
(e.g., as input) from the MEC device 115, as described elsewhere
herein.
[0045] As shown by reference number 210, the set of observations
includes a feature set. The feature set may include a set of
variables, and a variable may be referred to as a feature. A
specific observation may include a set of variable values (or
feature values) corresponding to the set of variables. In some
implementations, the machine learning system may determine
variables for a set of observations and/or variable values for a
specific observation based on input received from the MEC device
115. For example, the machine learning system may identify a
feature set (e.g., one or more features and/or feature values) by
extracting the feature set from structured data, by performing
natural language processing to extract the feature set from
unstructured data, by receiving input from an operator, and/or the
like.
[0046] As an example, a feature set for a set of observations may
include a first feature of content, a second feature of content
type, a third feature of latency, and so on. As shown, for a first
observation, the first feature may have a value of content 1, the
second feature may have a value of content type 1, the third
feature may have a value of latency 1, and so on. These features
and feature values are provided as examples and may differ in other
examples.
[0047] As shown by reference number 215, the set of observations
may be associated with a target variable. The target variable may
represent a variable having a numeric value, may represent a
variable having a numeric value that falls within a range of values
or has some discrete possible values, may represent a variable that
is selectable from one of multiple options (e.g., one of multiple
classes, classifications, labels, and/or the like), may represent a
variable having a Boolean value, and/or the like. A target variable
may be associated with a target variable value, and a target
variable value may be specific to an observation. In example 200,
the target variable is content to cache, which has a value of
content to cache 1 for the first observation.
[0048] The target variable may represent a value that a machine
learning model is being trained to predict, and the feature set may
represent the variables that are input to a trained machine
learning model to predict a value for the target variable. The set
of observations may include target variable values so that the
machine learning model can be trained to recognize patterns in the
feature set that lead to a target variable value. A machine
learning model that is trained to predict a target variable value
may be referred to as a supervised learning model.
[0049] In some implementations, the machine learning model may be
trained on a set of observations that do not include a target
variable. This may be referred to as an unsupervised learning
model. In this case, the machine learning model may learn patterns
from the set of observations without labeling or supervision, and
may provide output that indicates such patterns, such as by using
clustering and/or association to identify related groups of items
within the set of observations.
[0050] As shown by reference number 220, the machine learning
system may train a machine learning model using the set of
observations and using one or more machine learning algorithms,
such as a regression algorithm, a decision tree algorithm, a neural
network algorithm, a k-nearest neighbor algorithm, a support vector
machine algorithm, and/or the like. After training, the machine
learning system may store the machine learning model as a trained
machine learning model 225 to be used to analyze new
observations.
[0051] As shown by reference number 230, the machine learning
system may apply the trained machine learning model 225 to a new
observation, such as by receiving a new observation and inputting
the new observation to the trained machine learning model 225. As
shown, the new observation may include a first feature of content
X, a second feature of content type Y, a third feature of latency
Z, and so on, as an example. The machine learning system may apply
the trained machine learning model 225 to the new observation to
generate an output (e.g., a result). The type of output may depend
on the type of machine learning model and/or the type of machine
learning task being performed. For example, the output may include
a predicted value of a target variable, such as when supervised
learning is employed. Additionally, or alternatively, the output
may include information that identifies a cluster to which the new
observation belongs, information that indicates a degree of
similarity between the new observation and one or more other
observations, and/or the like, such as when unsupervised learning
is employed.
[0052] As an example, the trained machine learning model 225 may
predict a value of content to cache A for the target variable of
the content to cache for the new observation, as shown by reference
number 235. Based on this prediction, the machine learning system
may provide a first recommendation, may provide output for
determination of a first recommendation, may perform a first
automated action, may cause a first automated action to be
performed (e.g., by instructing another device to perform the
automated action), and/or the like.
[0053] In some implementations, the trained machine learning model
225 may classify (e.g., cluster) the new observation in a cluster,
as shown by reference number 240. The observations within a cluster
may have a threshold degree of similarity. As an example, if the
machine learning system classifies the new observation in a first
cluster (e.g., a content cluster), then the machine learning system
may provide a first recommendation. Additionally, or alternatively,
the machine learning system may perform a first automated action
and/or may cause a first automated action to be performed (e.g., by
instructing another device to perform the automated action) based
on classifying the new observation in the first cluster.
[0054] As another example, if the machine learning system were to
classify the new observation in a second cluster (e.g., a content
type cluster), then the machine learning system may provide a
second (e.g., different) recommendation and/or may perform or cause
performance of a second (e.g., different) automated action.
[0055] In some implementations, the recommendation and/or the
automated action associated with the new observation may be based
on a target variable value having a particular label (e.g.,
classification, categorization, and/or the like), may be based on
whether a target variable value satisfies one or more thresholds
(e.g., whether the target variable value is greater than a
threshold, is less than a threshold, is equal to a threshold, falls
within a range of threshold values, and/or the like), may be based
on a cluster in which the new observation is classified, and/or the
like.
[0056] In this way, the machine learning system may apply a
rigorous and automated process to optimize provision of high
latency content by a network. The machine learning system enables
recognition and/or identification of tens, hundreds, thousands, or
millions of features and/or feature values for tens, hundreds,
thousands, or millions of observations, thereby increasing accuracy
and consistency and reducing delay associated with optimizing
provision of high latency content by a network relative to
requiring computing resources to be allocated for tens, hundreds,
or thousands of operators to manually optimize provision of high
latency content by a network.
[0057] As indicated above, FIG. 2 is provided as an example. Other
examples may differ from what is described in connection with FIG.
2.
[0058] FIG. 3 is a diagram of an example environment 300 in which
systems and/or methods described herein may be implemented. As
shown in FIG. 3, environment 300 may include the base station 105,
the UE 110, the MEC device 115, the core network 120, the data
network 125, and/or the content provider device 130. Devices and/or
elements of environment 300 may interconnect via wired connections
and/or wireless connections.
[0059] The base station 105 includes one or more devices capable of
transferring traffic, such as audio, video, text, and/or other
traffic, destined for and/or received from UE 110. For example, the
base station 105 may include an eNodeB (eNB) associated with an LTE
network that receives traffic from and/or sends traffic to a core
network, a gNodeB (gNB) associated with a RAN of a 5G network, a
base transceiver station, a radio base station, a base station
subsystem, a cellular site, a cellular tower, an access point, a
transmit receive point (TRP), a radio access node, a macrocell base
station, a microcell base station, a picocell base station, a
femtocell base station, and/or another network entity capable of
supporting wireless communication.
[0060] The UE 110 includes one or more devices capable of
receiving, generating, storing, processing, and/or providing
information, as described elsewhere herein. UE 110 may include a
communication device. For example, the UE 110 may include a
wireless communication device, a mobile phone, a laptop computer, a
tablet computer, a gaming console, a set-top box, a wearable
communication device (e.g., a smart wristwatch, a pair of smart
eyeglasses, a head mounted display, or a virtual reality headset),
or a similar type of device.
[0061] The MEC device 115 includes one or more devices capable of
receiving, generating, storing, processing, providing, and/or
routing information, as described elsewhere herein. The MEC device
115 may include a communication device and/or a computing device.
For example, the MEC device 115 may include a device, such as an
application device, a client device, a web device, a database
device, a host device, a proxy device, a virtual device (e.g.,
executing on computing hardware), or a device in a cloud computing
system. In some implementations, the MEC device 115 includes
computing hardware used in a cloud computing environment.
[0062] The core network 120 may include a core network or a RAN
that includes one or more base stations 105 that take the form of
eNBs, gNBs, among other examples, via which a user device (e.g., a
mobile phone, a laptop computer, a tablet computer, a desktop
computer, among other examples) communicates with a core network.
The core network 120 may include one or more wired and/or wireless
networks. For example, the core network 120 may include a cellular
network (e.g., a 5G network, an LTE network, a 3G network, a code
division multiple access (CDMA) network), a public land mobile
network (PLMN), a local area network (LAN), a wide area network
(WAN), a metropolitan area network (MAN), a telephone network
(e.g., the Public Switched Telephone Network (PSTN)), a private
network, an ad hoc network, an intranet, the Internet, a fiber
optic-based network, among other examples, and/or a combination of
these or other types of networks.
[0063] The data network 125 includes one or more wired and/or
wireless networks. For example, the data network 125 may include a
LAN, a WAN, a MAN, a telephone network (e.g., the PSTN), a private
network, an ad hoc network, an intranet, the Internet, a fiber
optic-based network, among other examples, and/or a combination of
these or other types of networks.
[0064] The content provider device 130 includes one or more devices
capable of receiving, generating, storing, processing, providing,
and/or routing information, as described elsewhere herein. The
content provider device 130 may include a communication device
and/or a computing device. For example, the content provider device
130 may include a server, such as an application server, a client
server, a web server, a database server, a host server, a proxy
server, a virtual server (e.g., executing on computing hardware),
or a server in a cloud computing system. In some implementations,
the content provider device 130 includes computing hardware used in
a cloud computing environment.
[0065] The number and arrangement of devices and networks shown in
FIG. 3 are provided as an example. In practice, there may be
additional devices and/or networks, fewer devices and/or networks,
different devices and/or networks, or differently arranged devices
and/or networks than those shown in FIG. 3. Furthermore, two or
more devices shown in FIG. 3 may be implemented within a single
device, or a single device shown in FIG. 3 may be implemented as
multiple, distributed devices. Additionally, or alternatively, a
set of devices (e.g., one or more devices) of environment 300 may
perform one or more functions described as being performed by
another set of devices of environment 300.
[0066] FIG. 4 is a diagram of example components of one or more
devices of FIG. 3. The one or more devices may include a device
400, which may correspond to the base station 105, the UE 110, the
MEC device 115, and/or the content provider device 130. In some
implementations, the base station 105, the UE 110, the MEC device
115, and/or the content provider device 130 may include one or more
devices 400 and/or one or more components of the device 400. As
shown in FIG. 4, the device 400 may include a bus 410, a processor
420, a memory 430, a storage component 440, an input component 450,
an output component 460, and a communication component 470.
[0067] The bus 410 includes a component that enables wired and/or
wireless communication among the components of device 400. The
processor 420 includes a central processing unit, a graphics
processing unit, a microprocessor, a controller, a microcontroller,
a digital signal processor, a field-programmable gate array, an
application-specific integrated circuit, and/or another type of
processing component. The processor 420 is implemented in hardware,
firmware, or a combination of hardware and software. In some
implementations, the processor 420 includes one or more processors
capable of being programmed to perform a function. The memory 430
includes a random-access memory, a read only memory, and/or another
type of memory (e.g., a flash memory, a magnetic memory, and/or an
optical memory).
[0068] The storage component 440 stores information and/or software
related to the operation of device 400. For example, the storage
component 440 may include a hard disk drive, a magnetic disk drive,
an optical disk drive, a solid-state disk drive, a compact disc, a
digital versatile disc, and/or another type of non-transitory
computer-readable medium. The input component 450 enables device
400 to receive input, such as user input and/or sensed inputs. For
example, the input component 450 may include a touch screen, a
keyboard, a keypad, a mouse, a button, a microphone, a switch, a
sensor, a global positioning system component, an accelerometer, a
gyroscope, and/or an actuator. The output component 460 enables
device 400 to provide output, such as via a display, a speaker,
and/or one or more light-emitting diodes. The communication
component 470 enables the device 400 to communicate with other
devices, such as via a wired connection and/or a wireless
connection. For example, the communication component 470 may
include a receiver, a transmitter, a transceiver, a modem, a
network interface card, and/or an antenna.
[0069] The device 400 may perform one or more processes described
herein. For example, a non-transitory computer-readable medium
(e.g., the memory 430 and/or the storage component 440) may store a
set of instructions (e.g., one or more instructions, code, software
code, and/or program code) for execution by the processor 420. The
processor 420 may execute the set of instructions to perform one or
more processes described herein. In some implementations, execution
of the set of instructions, by one or more processors 420, causes
the one or more processors 420 and/or the device 400 to perform one
or more processes described herein. In some implementations,
hardwired circuitry may be used instead of or in combination with
the instructions to perform one or more processes described herein.
Thus, implementations described herein are not limited to any
specific combination of hardware circuitry and software.
[0070] The number and arrangement of components shown in FIG. 4 are
provided as an example. The device 400 may include additional
components, fewer components, different components, or differently
arranged components than those shown in FIG. 4. Additionally, or
alternatively, a set of components (e.g., one or more components)
of the device 400 may perform one or more functions described as
being performed by another set of components of the device 400.
[0071] FIG. 5 is a flowchart of an example process 500 for
optimizing provision of high latency content by a network. In some
implementations, one or more process blocks of FIG. 5 may be
performed by a device (e.g., the MEC device 115). In some
implementations, one or more process blocks of FIG. 5 may be
performed by another device or a group of devices separate from or
including the device, such as a base station (e.g., the base
station 105) and/or a content provider device (e.g., the content
provider device 130). Additionally, or alternatively, one or more
process blocks of FIG. 5 may be performed by one or more components
of the device 400, such as the processor 420, the memory 430, the
storage component 440, the input component 450, the output
component 460, and/or the communication component 470.
[0072] As shown in FIG. 5, process 500 may include receiving
historical content data (block 510). For example, the MEC device
may receive historical content data associated with a content
application of a UE, as described above. The content application of
the UE may include a browser application. The MEC device may be
located in an access network associated with the UE and may receive
the historical content data from the UE and/or a content provider
device. In some implementations, the UE may have access to a
plurality of MEC devices and the MEC device may be one of the
plurality of MEC devices that is geographically closest to the
UE.
[0073] As further shown in FIG. 5, process 500 may include
identifying content to cache for based on the historical data
(block 520). For example, the MEC device may process the historical
content data, with a machine learning model, to identify content to
cache for the UE, as described above. The machine learning model
may include a heuristic prediction model. In some implementations,
the MEC device may receive additional historical content data
associated with a plurality of other UEs and the MEC device may
process the historical content data and the additional historical
content data, with the machine learning model, to identify the
content to cache for the UE.
[0074] In some implementations, the MEC device may receive network
data identifying a bandwidth associated with an access network of
the UE and a latency associated with the access network. The MEC
device may process the historical content data and the network
data, with the machine learning model, to identify the content to
cache. The content to cache may include content accessed by the
content application of the UE and that fails to satisfy a latency
threshold, a percentage of content accessed by the content
application of the UE, based on latencies associated with the
content, content accessed by the content application of the UE and
that is identical to content accessed by other UE, and/or the
like.
[0075] In some implementations, the MEC device may create a latency
table that identifies the content accessed by the content
application of the UE and latencies associated with the content.
The MEC device may identify, as the content to cache, a percentage
of the content identified in the latency table, based on the
latencies associated with the content.
[0076] As further shown in FIG. 5, process 500 may include
requesting the content to cache (block 530). For example, the MEC
device may provide, to a content provider device, a request for the
content to cache, as described above.
[0077] As further shown in FIG. 5, process 500 may include
receiving the content to cache based on the request for the content
to cache (block 540). For example, the MEC device may receive, from
the content provider device, the content to cache based on the
request for the content to cache, as described above. In some
implementations, the MEC device may receive the content to cache
during a time period identified by the content provider device.
[0078] As further shown in FIG. 5, process 500 may include
generating intermediary content that corresponds to the content to
cache (block 550). For example, the MEC device may process the
content to cache, with a document object model and a browser object
model, to generate intermediary content that corresponds to the
content to cache, as described above. The intermediary content may
include a format that requires less processing by the content
application of the UE relative to a format of the content to
cache.
[0079] As further shown in FIG. 5, process 500 may include storing
the intermediary content (block 560). For example, the MEC device
may store the intermediary content in a data structure associated
with the MEC device, as described above. The MEC device may
receive, from the UE, a request for particular content included in
the content to cache. The MEC device may identify, in the data
structure and based on the request for the particular content,
particular intermediary content that corresponds to the particular
content. The MEC device may provide the particular intermediary
content to the UE.
[0080] Although FIG. 5 shows example blocks of process 500, in some
implementations, process 500 may include additional blocks, fewer
blocks, different blocks, or differently arranged blocks than those
depicted in FIG. 5. Additionally, or alternatively, two or more of
the blocks of process 500 may be performed in parallel.
[0081] As used herein, the term "component" is intended to be
broadly construed as hardware, firmware, or a combination of
hardware and software. It will be apparent that systems and/or
methods described herein may be implemented in different forms of
hardware, firmware, and/or a combination of hardware and software.
The actual specialized control hardware or software code used to
implement these systems and/or methods is not limiting of the
implementations. Thus, the operation and behavior of the systems
and/or methods are described herein without reference to specific
software code--it being understood that software and hardware can
be used to implement the systems and/or methods based on the
description herein.
[0082] As used herein, satisfying a threshold may, depending on the
context, refer to a value being greater than the threshold, greater
than or equal to the threshold, less than the threshold, less than
or equal to the threshold, equal to the threshold, not equal to the
threshold, or the like.
[0083] To the extent the aforementioned implementations collect,
store, or employ personal information of individuals, it should be
understood that such information shall be used in accordance with
all applicable laws concerning protection of personal information.
Additionally, the collection, storage, and use of such information
can be subject to consent of the individual to such activity, for
example, through well known "opt-in" or "opt-out" processes as can
be appropriate for the situation and type of information. Storage
and use of personal information can be in an appropriately secure
manner reflective of the type of information, for example, through
various encryption and anonymization techniques for particularly
sensitive information.
[0084] Even though particular combinations of features are recited
in the claims and/or disclosed in the specification, these
combinations are not intended to limit the disclosure of various
implementations. In fact, many of these features may be combined in
ways not specifically recited in the claims and/or disclosed in the
specification. Although each dependent claim listed below may
directly depend on only one claim, the disclosure of various
implementations includes each dependent claim in combination with
every other claim in the claim set. As used herein, a phrase
referring to "at least one of" a list of items refers to any
combination of those items, including single members. As an
example, "at least one of: a, b, or c" is intended to cover a, b,
c, a-b, a-c, b-c, and a-b-c, as well as any combination with
multiple of the same item.
[0085] No element, act, or instruction used herein should be
construed as critical or essential unless explicitly described as
such. Also, as used herein, the articles "a" and "an" are intended
to include one or more items and may be used interchangeably with
"one or more." Further, as used herein, the article "the" is
intended to include one or more items referenced in connection with
the article "the" and may be used interchangeably with "the one or
more." Furthermore, as used herein, the term "set" is intended to
include one or more items (e.g., related items, unrelated items, or
a combination of related and unrelated items), and may be used
interchangeably with "one or more." Where only one item is
intended, the phrase "only one" or similar language is used. Also,
as used herein, the terms "has," "have," "having," or the like are
intended to be open-ended terms. Further, the phrase "based on" is
intended to mean "based, at least in part, on" unless explicitly
stated otherwise. Also, as used herein, the term "or" is intended
to be inclusive when used in a series and may be used
interchangeably with "and/or," unless explicitly stated otherwise
(e.g., if used in combination with "either" or "only one of").
[0086] In the preceding specification, various example embodiments
have been described with reference to the accompanying drawings. It
will, however, be evident that various modifications and changes
may be made thereto, and additional embodiments may be implemented,
without departing from the broader scope of the invention as set
forth in the claims that follow. The specification and drawings are
accordingly to be regarded in an illustrative rather than
restrictive sense.
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