CN112822727B - Self-adaptive edge content caching method based on mobility and popularity perception - Google Patents

Self-adaptive edge content caching method based on mobility and popularity perception Download PDF

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CN112822727B
CN112822727B CN202110127420.6A CN202110127420A CN112822727B CN 112822727 B CN112822727 B CN 112822727B CN 202110127420 A CN202110127420 A CN 202110127420A CN 112822727 B CN112822727 B CN 112822727B
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popularity
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edge server
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CN112822727A (en
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鲍宁海
禹华春
许文彬
高鹏雷
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Chongqing University of Post and Telecommunications
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
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Abstract

The invention requests to protect a self-adaptive edge content caching method based on mobility and popularity perception, and belongs to the technical field of communication. Aiming at the problems of free movement of a user and real-time change of content popularity in a mobile edge computing scene, a self-adaptive edge content caching method is provided. Initializing the edge cache configuration of the content according to the global and local popularity of the content and the cache capacity constraint of the edge server; according to the real-time change of the content popularity, the time-space transfer characteristic of the user and the cache resource state of the edge server, the content cache configuration is subjected to self-adaptive dynamic updating, so that the edge cache hit rate is effectively improved, and the content downloading delay is reduced.

Description

Self-adaptive edge content caching method based on mobility and popularity perception
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a self-adaptive edge content caching method based on mobility and popularity perception.
Background
With the advent of the 5G era, the number of Internet of Things (IoT) applications has increased dramatically. The proliferation of network traffic results in increased latency and large consumption of energy, resulting in a decrease in Quality of Experience (QoE) for users. Mobile Edge Computing (MEC), as an emerging technology, is different from cloud Computing, and the MEC provides Computing and caching resources at the Edge of a network, caches popular content to an Edge server, reduces delay of network tasks and return of a large amount of data (such as video, audio and web pages), so that a user's request can be processed on the Edge server close to the user without being transmitted to the cloud, thereby effectively shortening delay time, reducing transmission energy consumption and bringing better user experience. However, the location of the user in the network is not constant, the user is always in a mobile state, when accessing the network, the user downloads different contents according to the own needs, when the user moves, the downloading contents may cause interruption, which greatly affects the user experience quality, when reaching a new base station, the same content request is initiated, the mobility of the user will cause a large impact on the content popularity, and the content downloading delay and the cache hit rate are closely related to the content popularity. Therefore, in order to adapt to the change of the current network, the caching policy should consider the influence of the user mobility and dynamically update the cached content.
At present, most caching schemes mainly consider that a user is in a static scene, neglect the mobility of the user and the real-time change of the popularity, cannot reflect the real popularity, are difficult to ensure the stability of user experience, and cause the continuous deterioration of content downloading delay.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A self-adaptive edge content caching method based on mobility and popularity perception is provided, wherein content downloading time delay is minimized, and content caching hit rate is maximized. The technical scheme of the invention is as follows:
a self-adaptive edge content caching method based on mobility and popularity perception is disclosed, which carries out self-adaptive caching configuration on content according to the global popularity and the local popularity of the content, the space-time transfer characteristics of a user and the caching resource state of an edge server, and specifically comprises the following steps:
101. putting all contents in the cloud server into the set C ═ C{ C }, putting a set M ═ M }, and a set N ═ 0 }. U.M into all edge servers, wherein C and M are both integers greater than 0, 0 represents a cloud server, and content in C is subjected to global popularity PcAnd local prevalence Pc,mPerforming initial cache configuration to obtain an initial cache solution space
Figure GDA0003642087970000021
Wherein the content of the first and second substances,
Figure GDA0003642087970000022
indicating that content c is cached at edge server m, otherwise,
Figure GDA0003642087970000023
102. waiting for the arrival of an event, detecting the arrival event CRm,cIf CR ism,cIf it is 0, it indicates that the user governed by the edge server m initiates a download request for the content c, and jumps to step 103, otherwise, CRm,c1, indicating that the user governed by the edge server m' moves to the jurisdiction of the edge server m in the downloading process of the content c, and jumping to the step 104;
103. global popularity P of update content ccAnd local prevalence Pc,mJumping to step 105;
104. local popularity P of update content cc,mJumping to step 105;
105. calculating the cumulative download average time delay D of the current and the previous K-1 times of event content ccIf D iscGreater than download delay tolerance threshold of content c
Figure GDA0003642087970000024
Skipping to step 106, otherwise, ending the event, and skipping to step 102;
106. caching content c to max { P) according to edge server cache capacity constraintsc,mCorresponding edge server m to replace min { P } in mcAnd c', ending the event, and jumping to the step 102.
Further, the step of performing initial cache configuration on the content in step C in step 101 includes:
1) global popularity P according to content ccSize, sort content C in descending order, according to content C local popularity P under edge server mc,mSize, sorting the content in each edge server M in M in descending order;
2) sequentially caching the content C in the C to max { Pc,mAnd f, corresponding edge server m until all edge servers reach the upper limit of the cache capacity.
Further, the cache capacity constraint of the edge server in step 2) is shown in formula (1),
Figure GDA0003642087970000031
in the formula (1), scIndicates the size of the content c, rmIndicating the cache capacity of the edge server m.
Further, the cumulative average download delay D of the content c in the step 105cThe calculation method of (c) is shown in formula (2),
Figure GDA0003642087970000032
in the formula (2), K is a constant and represents K events,
Figure GDA0003642087970000033
the time delay of the content c in the server n downloaded by the user managed by the edge server m in the k-th event is shown, and the calculation method is shown in formula (3),
Figure GDA0003642087970000034
in equation (3): v. ofm,nRepresenting the transmission rate between servers m and n, vm,mRepresenting the transmission rate between the edge server m and its affiliated users, vm,m′Denotes edge servers m and m'The transmission rate of each.
Further, the edge-cloud cooperative cache network model comprises a cloud server and a plurality of edge nodes, wherein the cloud server contains all contents, the edge nodes are composed of base stations and edge servers, the cloud server and the edge servers use backhaul link communication to distribute the service contents to the edge servers, and the edge servers are connected through optical fibers, so that the edge servers can communicate with each other.
The invention has the following advantages and beneficial effects:
the invention provides a self-adaptive edge content caching method based on mobility and popularity perception, aiming at the problems of random movement of users and real-time change of content popularity in an edge computing architecture. Because the existing static caching scheme is difficult to adapt to the global popularity change of the content and the local popularity change caused by the random movement of the user, the condition that the experience quality of the user is gradually reduced and even worsened is easily caused. According to the global and local popularity of the content, the content cache configuration is initialized on the premise of meeting the cache capacity constraint of the edge server, and the cache content is adaptively and dynamically updated according to the time-space transfer characteristic of the user, the real-time change of the popularity and the cache resource state of the edge server, so that the hit rate of the edge cache is obviously improved, and the content download delay is effectively reduced.
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Fig. 1 is a flow chart of an adaptive edge content caching method based on mobility and popularity perception according to a preferred embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the concepts and models involved in the present disclosure are as follows:
1. network model
An edge-cloud cooperative cache network model is assumed, wherein the model comprises a cloud server and a plurality of edge nodes, the cloud server contains all contents, the edge nodes are composed of base stations and edge servers, the cloud server and the edge servers communicate through backhaul links to distribute the service contents to the edge servers, and the edge servers are connected through optical fibers so that the edge servers can communicate with each other.
2. Other symbols relating to the present invention are described below:
c: content collection
M: edge server aggregation
N: all server set with cloud server
sc: size of content c
Pc: global popularity of content c
Pc,m: local popularity of content c under edge server m
rm: cache capacity of edge server m
vm,n: representing the transmission rate between servers m and n
vm,m: representing the transmission rate between the edge server m and the responsible user
vm,m′: representing the transfer rate between edge servers m and m
Figure GDA0003642087970000051
Time delay of content c on user request server n governed by edge server m at kth event
Figure GDA0003642087970000052
Download delay tolerance threshold for content c
The technical scheme of the invention is explained as follows:
1. initial configuration method
Step 1: global popularity P according to content ccSize, sort content in C in descending order, according to content C's locality under edge server mPopularity Pc,mSize, sorting the content in each edge server M in M in descending order;
step 2: sequentially caching the content C in the C to max { Pc,mAnd f, corresponding edge server m until all edge servers reach the upper limit of the cache capacity.
2. Edge server cache capacity constraints
As shown in the formula (1),
Figure GDA0003642087970000053
in the formula (1), scIndicates the size of the content c, rmIndicating the cache capacity of the edge server m.
3. Cumulative download average delay DcThe calculation method of (2) is shown in formula (2).
Figure GDA0003642087970000054
In equation (2): k is a constant, representing K events,
Figure GDA0003642087970000055
representing the time delay for the k-th event, the request under the edge server m to download the complete content c in the server n, is calculated as shown in equation (3).
Figure GDA0003642087970000061
In equation (3): v. ofm,nRepresenting the transmission rate between servers m and n, vm,mRepresenting the transmission rate between the edge server m and its affiliated users, vm,m′Representing the transfer rate between edge servers m and m'.
A self-adaptive edge content caching method based on mobility and popularity perception is specifically implemented by the following steps:
step 101: cloud serverAll the contents in the content list are put into a set C ═ C }, all the edge servers are put into a set M ═ M }, and a set N ═ 0 { [ 0 ] } U M, wherein C and M are integers larger than 0, 0 represents a cloud server, and the contents in the content list C are subjected to global popularity PcAnd local prevalence Pc,mPerforming initial cache configuration to obtain an initial cache solution space
Figure GDA0003642087970000062
Wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003642087970000063
indicating that content c is cached at edge server m, otherwise,
Figure GDA0003642087970000064
step 102: waiting for the arrival of an event, detecting the arrival event CRm,cIf CR ism,cIf it is 0, it represents that the user governed by the edge server m initiates a download request for the content c, and jumps to step 3, otherwise, CRm,c1, indicating that a user governed by the edge server m' moves to the jurisdiction of the edge server m in the downloading process of the content c, and skipping to the step 4;
step 103: global popularity P of update content ccAnd local prevalence Pc,mJumping to step 105;
step 104: local popularity P of update content cc,mSkipping to step 5;
step 105: calculating the cumulative download average time delay D of the current and the previous K-1 times of event content ccIf D iscGreater than download delay tolerance threshold of content c
Figure GDA0003642087970000065
Skipping to the step 6, otherwise, ending the event, and skipping to the step 2;
step 106: caching content c to max { P) according to edge server cache capacity constraintsc,mThe corresponding edge server m to replace min { P } in mcAnd c', ending the event, and jumping to the step 102.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.

Claims (2)

1. A self-adaptive edge content caching method based on mobility and popularity perception is characterized in that self-adaptive caching configuration is carried out on content according to the global popularity and the local popularity of the content, the space-time transfer characteristics of a user and the caching resource state of an edge server, and the method specifically comprises the following steps:
101. putting all contents in the cloud server into a set C ═ C }, putting all edge servers into a set M ═ M }, and putting a set N ═ 0 ═ U M, wherein C and M are both integers larger than 0, 0 represents the cloud server, and the contents in the C are subjected to global popularity PcAnd local prevalence Pc,mPerforming initial cache configuration to obtain an initial cache solution space
Figure FDA0003642087960000011
Wherein the content of the first and second substances,
Figure FDA0003642087960000012
indicating that content c is cached at edge server m, otherwise,
Figure FDA0003642087960000013
102. waiting for the arrival of an event, detecting the arrival event CRm,cIf CR ism,cIf it is 0, it indicates that the user governed by the edge server m initiates a download request for the content c, and jumps to step 103, otherwise, CR initiates a download request for the content cm,c1, indicating that the user governed by the edge server m' moves to the jurisdiction of the edge server m in the downloading process of the content c, and jumping to the step 104;
103. global popularity P of update content ccAnd local prevalence Pc,mJump to stepA step 105;
104. local popularity P of update content cc,mJumping to step 105;
105. calculating the cumulative download average time delay D of the current and the previous K-1 times of event content ccIf D iscGreater than download delay tolerance threshold of content c
Figure FDA0003642087960000014
Skipping to step 106, otherwise, ending the event, and skipping to step 102;
106. caching content c to max { P) according to edge server cache capacity constraintsc,mCorresponding edge server m to replace min { P } in mcCorresponding content c', ending the event, and jumping to step 102; the step of performing initial cache configuration on the content in step C in step 101 includes:
1) global popularity P according to content ccSize, sort content C in descending order, according to content C local popularity P under edge server mc,mSize, sorting the content in each edge server M in M in descending order;
2) sequentially caching the content C in the C to max { Pc,mCorresponding edge servers m until all edge servers reach the upper limit of the cache capacity;
the cache capacity constraint of the edge server in the step 2) is shown in the formula (1),
Figure FDA0003642087960000021
in the formula (1), scIndicates the size of the content c, rmRepresenting the cache capacity of the edge server m;
the average delay D of the cumulative downloading of the content c in the step 105cThe calculation method of (2) is shown in formula (2),
Figure FDA0003642087960000022
in the formula (2), K is a constant and represents K events,
Figure FDA0003642087960000023
the time delay of the content c in the server n downloaded by the user managed by the edge server m in the kth event is shown, the calculation method is shown as formula (3),
Figure FDA0003642087960000024
in equation (3): v. ofm,nRepresenting the transmission rate between servers m and n, vm,mRepresenting the transmission rate between the edge server m and its affiliated users, vm,m′Representing the transmission rate between edge servers m and m'.
2. The adaptive edge content caching method based on mobility and popularity perception according to claim 1, wherein the edge-cloud cooperative caching network model comprises a cloud server and a plurality of edge nodes, the cloud server contains all content, the edge nodes are composed of base stations and edge servers, the cloud server and the edge servers communicate through backhaul links to distribute service content to the edge servers, and the edge servers are connected through optical fibers to enable the edge servers to communicate with each other.
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