CN111988168B - Edge service deployment method and device and electronic equipment - Google Patents

Edge service deployment method and device and electronic equipment Download PDF

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CN111988168B
CN111988168B CN202010723732.9A CN202010723732A CN111988168B CN 111988168 B CN111988168 B CN 111988168B CN 202010723732 A CN202010723732 A CN 202010723732A CN 111988168 B CN111988168 B CN 111988168B
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CN111988168A (en
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时岩
赵旭辉
陈山枝
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Beijing University of Posts and Telecommunications
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Abstract

The application provides an edge service deployment method and device and electronic equipment. The method comprises the steps of calculating service request estimators of all mobility characteristic blocks of a current time slice according to historical service request quantity of all mobility characteristic blocks, determining service requests of users corresponding to the mobility characteristic blocks in the current time slice aiming at any mobility characteristic block, obtaining a node set to be deployed of all mobility characteristic blocks according to the service requests of the users and all the service request estimators received by all nodes to be deployed, calculating service time delay and service deployment cost of all the node sets to be deployed, and accordingly selecting a target edge node set for deploying edge services corresponding to the service requests of all the users. By utilizing the mobility characteristic information of the user and learning the current user service requirement according to the historical user requirement, the problem of mobile edge application service deployment caused by mobility in the prior art is solved.

Description

Edge service deployment method and device and electronic equipment
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for edge service deployment, and an electronic device.
Background
The Mobile Edge network is a novel Mobile Edge Computing (MEC) based network, and can provide support for low delay and high Computing requirements of future Mobile applications, such as virtual reality, Mobile games, car networking applications, and the like.
Because a mobile user moves among different edge node service ranges and the space-time distribution rule of the user movement behavior causes that the number of mobile edge service requests from the mobile user for different edge nodes may be greatly different, for example, the number of service requests of edge nodes at certain positions is large, so that edge application service needs to be deployed, and certain edge nodes do not have service requests, so that edge application service does not need to be deployed. Therefore, an edge service deployment method for providing edge nodes is urgently needed.
Disclosure of Invention
The embodiment of the application aims to provide an edge service deployment method, an edge service deployment device and electronic equipment so as to provide an edge service deployment method of an edge node.
The specific technical scheme is as follows:
in a first aspect, an embodiment of the present application provides an edge service deployment method, which is applied to a manager in an edge service deployment system, where the edge service deployment system further includes a cloud platform and a plurality of edge nodes; the method comprises the following steps:
acquiring a mobility feature space, wherein the mobility feature space comprises a plurality of mobility feature blocks, mobility feature information and the mobility feature blocks have a corresponding relation, the mobility feature information in different mobility feature blocks is different, and the mobility feature information represents the mobility features of a user;
acquiring the historical service request quantity of each mobility characteristic block; calculating the service request estimation quantity of each mobility characteristic block of the current time slice according to the historical service request quantity of each mobility characteristic block; the time slice represents a time period in a preset time period;
acquiring mobility characteristic information of each user in a current time slice, and respectively determining a mobility characteristic block corresponding to each user according to the mobility characteristic information of each user;
the method comprises the steps of obtaining service requests of users received by edge nodes in a current time slice, taking the edge nodes with the number of the received service requests smaller than a preset service request number threshold value as nodes to be deployed, and determining the service requests of the users corresponding to the mobility feature blocks in the current time slice aiming at any mobility feature block; obtaining a node set to be deployed of each mobility characteristic block;
acquiring all edge node subsets of each node set to be deployed, wherein the edge node subsets comprise at least one node to be deployed;
aiming at any edge node subset, calculating service time delay and service deployment cost of the edge node subset according to the service request of the user received by each node to be deployed and the service request estimators;
and selecting a target edge node set for deploying the edge service corresponding to the service request of each user according to the service delay of each edge node subset and the cost of deploying the service.
Optionally, dividing a preset time period into T discrete time slices; denote the mobility feature space as χ ═ 0,1]DD is a mobility characteristic information dimension, and x is a D-dimensional mobility characteristic space;
the method comprises the steps of obtaining service requests of users received by edge nodes in a current time slice, taking the edge nodes with the number of the received service requests smaller than a preset service request number threshold value as nodes to be deployed, and determining the service requests of the users corresponding to a mobility feature block in the current time slice aiming at any mobility feature block; obtaining a set of nodes to be deployed of each mobility characteristic block, including:
setting the preset service request threshold according to the following formula:
Figure GDA0003191721170000021
wherein 1 is more than or equal to alpha and is more than 0; α is the Herdel index;
the set of nodes to be deployed is represented as:
Figure GDA0003191721170000022
wherein N is a set of edge nodes within a time slice t;
Figure GDA0003191721170000031
for the set of users on edge node N within a time slice T, T belongs to {1, …, T }, N belongs to N; m represents any user and satisfies
Figure GDA0003191721170000032
ptA set of mobility feature blocks for time slice t, where,
Figure GDA0003191721170000033
representing mobility characteristic blocks to which the user m belongs in the time slice t;
Figure GDA0003191721170000034
representing mobility feature blocks
Figure GDA0003191721170000035
Number of service requests;
Figure GDA0003191721170000036
a set of nodes to be deployed representing time slice t.
Alternatively to this, the first and second parts may,
Figure GDA0003191721170000037
is denoted as LjWhere j ∈ {1, …, k }; k is the number of edge node subsets;
each edge node subset L is calculated according to the following formulajService delay of (2):
Figure GDA0003191721170000038
wherein:
Figure GDA0003191721170000039
Figure GDA00031917211700000310
Figure GDA00031917211700000311
wherein the content of the first and second substances,
Figure GDA00031917211700000312
is the service latency of a subset of the edge nodes,
Figure GDA00031917211700000313
Figure GDA00031917211700000314
representing the uncertainty of the estimate;
Figure GDA00031917211700000315
blocking as p for mobility featurestIs in the edge server subset Lj(n∈Lj) Number of service requests;
Figure GDA00031917211700000316
as a motion feature vector of
Figure GDA00031917211700000317
The mobility characteristics of the user m to which the user m belongs;
Figure GDA00031917211700000318
representing mobility feature blocks
Figure GDA00031917211700000319
A total service request estimator at a current time slice;
Figure GDA0003191721170000041
representing mobility characteristics is partitioned into
Figure GDA0003191721170000042
The service request amount of (2);
Figure GDA0003191721170000043
to moveThe characteristic features are divided into blocks
Figure GDA0003191721170000044
The set of user service requests of (a),
Figure GDA0003191721170000045
wherein the content of the first and second substances,
Figure GDA0003191721170000046
wherein the content of the first and second substances,
Figure GDA0003191721170000047
representing the response time delay when the edge node provides service;
Figure GDA0003191721170000048
representing response time delay when the cloud platform provides the service;
Figure GDA0003191721170000049
wherein, λ is the input data size of the edge service corresponding to the service request of the user; mu is a calculation resource period required for calculating the edge service corresponding to the service request of the user;
Figure GDA00031917211700000410
is the transmission rate between user m and edge node n in time slice t, fnIs the processing speed of the edge node n; f. ofCThe processing speed of the cloud platform; ctThe transmission rate between the edge node n and the cloud platform is obtained; h istSending a service request to a user until the user receives the time for providing a service response for the user by the cloud platform;
each edge node subset L is calculated according to the following formulajDeployment service cost of (2):
Figure GDA00031917211700000411
wherein:
Figure GDA00031917211700000412
wherein, ω is the deployment cost of deploying edge service at an edge node; f (omega. L)jL) is at the edge node subset LjThe deployment cost of the edge node in (1) to deploy the edge service.
Optionally, in
Figure GDA00031917211700000413
In the case of a non-empty set, selecting a target edge node set for deploying an edge service corresponding to a service request of each user according to the service delay of each edge node subset and the cost of deploying the service, including:
in that
Figure GDA00031917211700000414
In the case of (a) in (b),
Figure GDA00031917211700000415
beta, L are constants, LtExpressed as a set of target edge nodes; determining a target edge node set l according to the following formulat：
Figure GDA0003191721170000051
In that
Figure GDA0003191721170000052
In the case of (a) in (b),
Figure GDA0003191721170000053
determining a target edge node set according to the following formula:
Figure GDA0003191721170000054
wherein the content of the first and second substances,
Figure GDA0003191721170000055
optionally, in
Figure GDA0003191721170000056
In the case of an empty set, selecting a target edge node set for deploying an edge service corresponding to a service request of each user according to the service delay of each edge node subset and the cost of deploying the service includes:
determining a target edge node set according to the following formula:
Figure GDA0003191721170000057
in a second aspect, an embodiment of the present application provides an edge service deployment apparatus, which applies a manager in an edge service deployment system, where the edge service deployment system further includes a cloud platform and a plurality of edge nodes; the device comprises:
the mobile terminal comprises a first acquisition module, a second acquisition module and a processing module, wherein the first acquisition module is used for acquiring a mobile characteristic space, the mobile characteristic space comprises a plurality of mobile characteristic blocks, mobile characteristic information and the mobile characteristic blocks have a corresponding relation, the mobile characteristic information in different mobile characteristic blocks is different, and the mobile characteristic information represents the mobile characteristic of a user;
the second acquisition module is used for acquiring the historical service request quantity of each mobility characteristic block; calculating the service request estimation quantity of each mobility characteristic block of the current time slice according to the historical service request quantity of each mobility characteristic block; the time slice represents a time period in a preset time period;
the third acquisition module is used for acquiring the mobility characteristic information of each user in the current time slice and respectively determining the mobility characteristic blocks corresponding to each user according to the mobility characteristic information of each user;
the fourth acquisition module is used for acquiring the service requests of the users received by each edge node in the current time slice, taking the edge nodes with the number of the received service requests smaller than a preset service request number threshold value as nodes to be deployed, and determining the service requests of the users corresponding to the mobility feature blocks in the current time slice aiming at any mobility feature block; obtaining a node set to be deployed of each mobility characteristic block;
a fifth acquisition module, configured to acquire all edge node subsets of each to-be-deployed node set, where the edge node subsets include at least one to-be-deployed node;
the computing module is used for computing the service delay and the service deployment cost of any edge node subset according to the service request of the user received by each node to be deployed and each service request estimator;
and the selecting module is used for selecting a target edge node set for deploying the edge service corresponding to the service request of each user according to the service delay of each edge node subset and the service deployment cost.
Optionally, dividing a preset time period into T discrete time slices; representing mobility feature space as x ═ 0,1]DD is a mobility characteristic information dimension, and x is a D-dimensional mobility characteristic space;
the fourth acquisition module is specifically configured to:
setting the preset service request threshold according to the following formula:
Figure GDA0003191721170000061
wherein 1 is more than or equal to alpha and is more than 0; α is the Herdel index;
the set of nodes to be deployed is represented as:
Figure GDA0003191721170000062
wherein N is a time slicet inner edge node set;
Figure GDA0003191721170000063
for the set of users on edge node N within a time slice T, T belongs to {1, …, T }, N belongs to N; m represents any user and satisfies
Figure GDA0003191721170000064
ptA set of mobility feature blocks for time slice t, where,
Figure GDA0003191721170000065
representing mobility characteristic blocks to which the user m belongs in the time slice t;
Figure GDA0003191721170000066
representing mobility feature blocks
Figure GDA0003191721170000067
Number of service requests;
Figure GDA0003191721170000071
a set of nodes to be deployed representing time slice t.
Alternatively to this, the first and second parts may,
Figure GDA0003191721170000072
is denoted as LjWhere j ∈ {1, …, k }; k is the number of edge node subsets; the fourth acquisition module is specifically configured to:
each edge node subset L is calculated according to the following formulajService delay of (2):
Figure GDA0003191721170000073
wherein:
Figure GDA0003191721170000074
Figure GDA0003191721170000075
Figure GDA0003191721170000076
wherein the content of the first and second substances,
Figure GDA0003191721170000077
is the service latency of a subset of the edge nodes,
Figure GDA0003191721170000078
Figure GDA0003191721170000079
representing the uncertainty of the estimate;
Figure GDA00031917211700000710
blocking as p for mobility featurestIs in the edge server subset Lj(n∈Lj) Number of service requests;
Figure GDA00031917211700000711
as a motion feature vector of
Figure GDA00031917211700000712
The mobility characteristics of the user m to which the user m belongs;
Figure GDA00031917211700000713
representing mobility feature blocks
Figure GDA00031917211700000714
A total service request estimator at a current time slice;
Figure GDA00031917211700000715
representing mobility characteristics is partitioned into
Figure GDA00031917211700000716
The service request amount of (2);
Figure GDA00031917211700000717
is blocked for mobility characteristics as
Figure GDA00031917211700000718
The set of user service requests of (a),
Figure GDA00031917211700000719
wherein the content of the first and second substances,
Figure GDA00031917211700000720
wherein the content of the first and second substances,
Figure GDA0003191721170000081
representing the response time delay when the edge node provides service;
Figure GDA0003191721170000082
representing response time delay when the cloud platform provides the service;
Figure GDA0003191721170000083
wherein, λ is the input data size of the edge service corresponding to the service request of the user; mu is a calculation resource period required for calculating the edge service corresponding to the service request of the user;
Figure GDA0003191721170000084
is the transmission rate between user m and edge node n in time slice t, fnIs the processing speed of the edge node n; f. ofCThe processing speed of the cloud platform; ctThe transmission rate between the edge node n and the cloud platform is obtained; h istSending a service request to a user until the user receives the time for providing a service response for the user by the cloud platform;
according to the following formulaThe following formula, calculate each edge node subset LjDeployment service cost of (2):
Figure GDA0003191721170000085
wherein:
Figure GDA0003191721170000086
wherein, ω is the deployment cost of deploying edge service at an edge node; f (omega. L)jL) is at the edge node subset LjThe deployment cost of the edge node in (1) to deploy the edge service.
Optionally, in
Figure GDA0003191721170000087
In the case of a non-empty set, the selection module is specifically configured to:
in that
Figure GDA0003191721170000088
In the case of (a) in (b),
Figure GDA0003191721170000089
beta, L are constants, LtExpressed as a set of target edge nodes; determining a target edge node set l according to the following formulat：
Figure GDA00031917211700000810
In that
Figure GDA00031917211700000811
In the case of (a) in (b),
Figure GDA00031917211700000812
determining a target edge node set according to the following formula:
Figure GDA0003191721170000091
wherein the content of the first and second substances,
Figure GDA0003191721170000092
optionally, in
Figure GDA0003191721170000093
In the case of an empty set, the selection module is specifically configured to:
determining a target edge node set according to the following formula:
Figure GDA0003191721170000094
in a third aspect, an embodiment of the present application provides an electronic device, including: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
a memory for storing a computer program;
a processor, configured to implement the edge service deployment method according to any one of the first aspect or the second aspect when executing a program stored in a memory.
In a fourth aspect, an embodiment of the present application provides a storage medium, where instructions are stored in the storage medium, and when the storage medium is run on a computer, the storage medium causes the computer to execute the edge service deployment method according to any one of the first aspect.
In a fifth aspect, an embodiment of the present application provides a computer program product containing instructions, which when run on a computer, causes the computer to perform the edge service deployment method according to any one of the first aspect.
The method, the device and the electronic equipment for edge service deployment provided by the embodiment of the application acquire mobility feature information of each user in a current time slice, respectively determine mobility feature blocks corresponding to each user according to the mobility feature information of each user, acquire service requests of the users received by each edge node in the current time slice, take the edge nodes with the quantity of the received service requests smaller than a preset service request quantity threshold value as nodes to be deployed, determine the service requests of the users corresponding to the mobility feature blocks in the current time slice aiming at any mobility feature block, acquire a node set to be deployed of each mobility feature block, acquire all edge node subsets of each node set to be deployed, wherein the edge node subsets comprise at least one node to be deployed, and estimate quantities according to the service requests and each service request of the users received by each node to be deployed aiming at any edge node subset, calculating the service delay and the service deployment cost of the edge node subsets, selecting a target edge node set for deploying the edge service corresponding to the service request of each user according to the service delay and the service deployment cost of each edge node subset, learning the current user service requirement according to the historical user requirement by utilizing the mobility characteristic information of the user, and determining the edge node for deploying the edge service according to the current user service requirement, thereby solving the problem that the prior art does not adapt to the waste of edge node resources caused by the dynamic change of the mobile edge service request caused by the movement of the user. Of course, not all advantages described above need to be achieved at the same time in the practice of any one product or method of the present application.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of an edge service deployment method according to an embodiment of the present application;
fig. 2 is a schematic diagram of an edge service deployment apparatus according to an embodiment of the present application;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The number of user requests is usually unknown before the mobile edge application service is deployed to a particular edge node, and the revenue for the mobile edge application service deployment depends mainly on the number of user requests. Therefore, the mobile edge service deployment strategy must adapt to the dynamic change of the mobile edge service request caused by the user movement, and when the edge service is deployed to the edge node without adapting to the dynamic change of the mobile edge service request caused by the user movement, the waste of the edge node resource is caused.
In view of the foregoing problems, embodiments of the present application disclose an edge service deployment method, an edge service deployment device, and an electronic device, which are described below.
An embodiment of the present application provides an edge service deployment method, which is applied to a manager in an edge service deployment system, where the edge service deployment system further includes a cloud platform and a plurality of edge nodes, referring to fig. 1, fig. 1 is a schematic diagram of the edge service deployment method according to the embodiment of the present application, and includes:
step 110, obtaining a mobility feature space, where the mobility feature space includes a plurality of mobility feature blocks, mobility feature information has a corresponding relationship with the mobility feature blocks, and mobility feature information in different mobility feature blocks is different, where the mobility feature information indicates a mobility feature of a user.
The edge service deployment method in the embodiment of the present application may be implemented by an electronic device, and specifically, the electronic device is any server that can provide an edge service, for example, a computer, a digital broadcast terminal, a messaging device, a game console, and the like.
The edge service deployment system comprises a plurality of edge nodes and a cloud platform; the edge node is used for deploying edge services, and when receiving a service request of a user, the edge services respond to the service request and provide services for the user; the cloud platform is used for deploying edge services, and under the condition that the edge node does not deploy the edge services corresponding to the service requests of the users, the cloud platform receives the service requests of the users forwarded by the edge node, responds to the service requests and provides the services to the users.
In order to accommodate mobility characteristics of users and mobile edge service deployment issues caused by unknown number of service requests of users, a user mobility characteristic information space may be obtained. Mobility characteristics information may be defined from multiple dimensions, thereby forming a multi-dimensional mobility characteristics space. And then the value range of the mobility characteristic information in each dimension can be divided according to different granularities according to needs, so that the whole mobility characteristic space is divided into a plurality of mobility characteristic blocks. For a certain mobile user, the edge service placement manager may sense and collect mobility feature information through mobile equipment and a big data technology carried by the user, so as to construct a historical mobility feature information set of the user, where the mobility feature information indicates a mobility feature of the user, the mobility feature information may include time, location, user gender, age, occupation, and the like when the user sends a service request, specifically, the mobility feature information may be one-dimensional information or multidimensional information, specifically, the setting is performed according to an actual situation, and is not limited herein, and each mobility feature information may correspond to a certain mobility feature block according to a value thereof.
For example, the user mobility feature information is two-dimensional information, and includes time and a position of a service request sent by a user, the mobility feature space is a two-dimensional space, the mobility feature space is divided to obtain a mobility feature block set representing the time and the position of the service request sent by the user, and each mobility feature block is a rectangle. Furthermore, the user mobility characteristic information is three-dimensional information and comprises time for sending the service request by the user, a position for sending the service request by the user and occupation of the user, the mobility characteristic space is a three-dimensional space, the mobility characteristic space is divided to obtain a set of occupational mobility characteristic blocks representing the time and the position for sending the service request by the user and the user, and each mobility characteristic block is a cuboid.
In order to predict the number of service requests of the current user, when the mobility characteristic information of each user is similar, the service requests of each user are also similar.
For example, people prefer to use mobile text and voice communication services and instant messaging software services in residential areas around 9 pm; using an online sales platform and online retailer services in a business region at 8 pm; it is predicted that the user request sent by the user at the same location is the same for the same time period, e.g., the user in the residential area at about 9 pm sends a service request for mobile text and voice messaging services and a service request for instant messaging software services.
Representing mobility feature space as x ═ 0,1]DD is a mobility characteristic information dimension, and x is a D-dimensional mobility characteristic space; in particular, there is L>0，1≥α>And 0, alpha is a hello index, which characterizes that when the mobility information of the users is similar, the service request number of the users is also similar, and the size of alpha depends on the similarity of the mobility characteristic vectors of the users and the similarity of the service request number, namely, when the following hello continuity formula is satisfied, the mobility characteristic information of the users is similar, and the service request of the users is also similar.
|μn(x1)-μn(x2)|≤L||x1-x2||α
Wherein x1, x2 ∈ χ, x1 is the mobility feature information vector of user 1, x2 is the mobility feature information vector of user 2, | | x1-x2| | is the euclidean distance between vector x1 and vector x 2; l and α represent the proximity of the service requests of the user 1 and the user 2 when the mobility characteristic information of the user 1 and the user 2 is close to a certain extent. Mu.sn(x1) is the expected value of the service request quantity of the user with the mobility feature information vector of x1 on the edge server n. Mu.sn(x2) is the expected value of the amount of service requests on the edge server n by the user with mobility profile information vector x 2. Wherein, mun(x1)＝E(d(x1))，μn(x2) ═ E (d (x 2)). Where d (x1) represents the number of service requests of x1 user and d (x2) represents the number of service requests of x2 user.
Step 120, obtaining the historical service request quantity of each mobility characteristic block; calculating the service request estimation quantity of each mobility characteristic block of the current time slice according to the historical service request quantity of each mobility characteristic block; the time slice represents a period of time within a preset time period.
In order to adapt to the problem of mobile edge service deployment caused by the unknown number of service requests of the user, the historical number of service requests of each mobility feature block can be obtained, so that the current number of service requests can be predicted according to the historical number of service requests, namely, the service request estimation quantity of each mobility feature block of the current time slice is calculated.
For example, a preset time period is divided into T discrete time slices, T is the length of the preset time period, and a counter is set for each mobility feature block
Figure GDA0003191721170000131
And the number of service requests sent to the edge node n by the users with the corresponding relation between the mobility characteristic information and the mobility characteristic block p in the time slice t is shown.
Figure GDA0003191721170000132
Is set to 0.
Figure GDA0003191721170000133
A set of service requests representing users of mobility feature block p at historical time slice t,
Figure GDA0003191721170000134
to representThe mobility characteristics information for the current time slice t corresponds to the service request estimate for the user of the mobility characteristics block p,
Figure GDA0003191721170000135
the calculation is carried out by a sample averaging method, specifically,
Figure GDA0003191721170000136
wherein
Figure GDA0003191721170000137
That is, the number of service requests observed is a counter that is set in blocks according to each mobility characteristic
Figure GDA0003191721170000138
Is obtained by counting.
And calculating the service request estimation quantity of each mobility characteristic block of the current time slice according to the historical service request quantity of each mobility characteristic block, so that the method can adapt to the mobile edge service deployment problem caused by the service request quantity of unknown users.
Step 130, obtaining mobility characteristic information of each user in the current time slice, and respectively determining the mobility characteristic blocks corresponding to each user according to the mobility characteristic information of each user.
At time slice t, the mobility feature block set in the mobility feature space is denoted as ptM is a user set in a time slice t; m is equal to {1, …, M }, and the mobility characteristic vector of the user M is expressed as the mobility characteristic vector of the user M at the current time slice t
Figure GDA0003191721170000139
According to
Figure GDA00031917211700001310
Determining the mobility characteristic block corresponding to the user as
Figure GDA00031917211700001311
Then
Figure GDA00031917211700001312
Wherein the mobility feature vector is used to represent mobility feature information of the user.
Step 140, obtaining service requests of users received by each edge node in the current time slice, taking the edge nodes with the number of the received service requests smaller than a preset service request number threshold as nodes to be deployed, and determining the service requests of the users corresponding to the mobility feature blocks in the current time slice aiming at any mobility feature block; and obtaining a node set to be deployed of each mobility characteristic block.
The method comprises the steps of obtaining service requests of users received by each edge node in a current time slice, and taking the edge nodes with the number of the received service requests smaller than a preset service request number threshold value as nodes to be deployed, so that the deviation caused by uncertainty of mobility feature blocks obtained by dividing a mobility feature space can be fully considered, edge nodes without edge services are fully utilized, and a better edge service deployment scheme can be obtained.
In one possible embodiment, the predetermined time period is divided into T discrete time slices; representing mobility feature space as x ═ 0,1]DD is a mobility characteristic information dimension, and x is a D-dimensional mobility characteristic space; 1/r ofTSide length of a block for mobility characteristics; (r)T)DThe number of blocks for mobility features;
the service requests of the users received by the edge nodes in the current time slice are obtained, the edge nodes with the service requests smaller than a preset service request threshold value are used as nodes to be deployed, and the service requests of the users corresponding to the mobility feature blocks in the current time slice are determined aiming at any mobility feature block; obtaining a set of nodes to be deployed of each mobility characteristic block, including:
setting the preset service request threshold according to the following formula:
Figure GDA0003191721170000141
wherein 1 is more than or equal to alpha and is more than 0; α is the Herdel index;
the set of nodes to be deployed is represented as:
Figure GDA0003191721170000142
wherein N is a set of edge nodes within a time slice t;
Figure GDA0003191721170000143
for the set of users on edge node N within a time slice T, T belongs to {1, …, T }, N belongs to N; m represents any user and satisfies
Figure GDA0003191721170000144
ptA set of mobility feature blocks for time slice t, where,
Figure GDA0003191721170000145
representing mobility characteristic blocks to which the user m belongs in the time slice t;
Figure GDA0003191721170000146
representing mobility feature blocks
Figure GDA0003191721170000147
Number of service requests;
Figure GDA0003191721170000151
a set of nodes to be deployed representing time slice t.
Step 150, obtaining all edge node subsets of each to-be-deployed node set, where the edge node subsets include at least one to-be-deployed node.
Figure GDA0003191721170000152
Is denoted as LjWherein j belongs to {1, …, k }, and k is the number of the edge node subsets; wherein the number of edge node subsets may beThe number of edge nodes in each edge node subset can also be set according to actual conditions, and in one possible embodiment, the edge node subset LjIs that
Figure GDA0003191721170000153
All subsets are aggregated.
Step 160, for any edge node subset, calculating the service delay and the service deployment cost of the edge node subset according to the service request of the user received by each node to be deployed and the service request estimators.
And aiming at any edge node subset, calculating the service delay and the service deployment cost of the edge node subset according to the service request of the user received by each node to be deployed and the service request estimators. And determining an optimal mobile edge service deployment strategy by calculating the service delay of the edge node subset and the cost of deploying the service.
In one possible embodiment of the method according to the invention,
Figure GDA0003191721170000154
is denoted as LjWhere j ∈ {1, …, k };
calculating the service delay of the edge node subset according to the following formula:
Figure GDA0003191721170000155
wherein:
Figure GDA0003191721170000156
Figure GDA0003191721170000157
Figure GDA0003191721170000161
wherein the content of the first and second substances,
Figure GDA0003191721170000162
is the service latency of a subset of the edge nodes,
Figure GDA0003191721170000163
Figure GDA0003191721170000164
representing the uncertainty of the estimate;
Figure GDA0003191721170000165
blocking as p for mobility featurestIs in the edge server subset Lj(n∈Lj) Number of service requests;
Figure GDA0003191721170000166
as a motion feature vector of
Figure GDA0003191721170000167
The mobility characteristics of the user m to which the user m belongs;
Figure GDA0003191721170000168
representing mobility feature blocks
Figure GDA0003191721170000169
A total service request estimator at a current time slice;
Figure GDA00031917211700001610
representing mobility characteristics is partitioned into
Figure GDA00031917211700001611
The service request amount of (2);
Figure GDA00031917211700001612
is blocked for mobility characteristics as
Figure GDA00031917211700001613
The set of user service requests of (a),
Figure GDA00031917211700001614
wherein the content of the first and second substances,
Figure GDA00031917211700001615
wherein the content of the first and second substances,
Figure GDA00031917211700001616
representing the response time delay when the edge node provides service;
Figure GDA00031917211700001617
representing response time delay when the cloud platform provides the service;
Figure GDA00031917211700001618
wherein, λ is the input data size of the edge service corresponding to the service request of the user; mu is a calculation resource period required for calculating the edge service corresponding to the service request of the user;
Figure GDA00031917211700001619
is the transmission rate between user m and edge node n in time slice t, fnIs the processing speed of the edge node n; f. ofCThe processing speed of the cloud platform; ctThe transmission rate between the edge node n and the cloud platform is obtained; h istSending a service request to a user until the user receives the time for providing a service response for the user by the cloud platform;
each edge node subset L is calculated according to the following formulajDeployment service cost of (2):
Figure GDA00031917211700001620
wherein:
Figure GDA0003191721170000171
wherein, ω is the deployment cost of deploying edge service at an edge node; f (omega. L)jL) is at the edge node subset LjThe deployment cost of the edge node in (1) to deploy the edge service.
Step 170, according to the service delay of each edge node subset and the cost of service deployment, selecting a target edge node set for deploying the edge service corresponding to the service request of each user.
And selecting a target edge node set for deploying the edge service corresponding to the service request of each user according to the service delay of each edge node subset and the service deployment cost. After the edge service corresponding to the service request of each user is deployed, each edge node provides corresponding service for the user according to the received service request of the user.
The method comprises the steps of obtaining mobility characteristic information of each user in a current time slice, respectively determining mobility characteristic blocks corresponding to each user according to the mobility characteristic information of each user, obtaining service requests of the users received by each edge node in the current time slice, taking the edge nodes with the number of the received service requests smaller than a preset service request number threshold value as nodes to be deployed, determining the service requests of the users corresponding to the mobility characteristic blocks in the current time slice aiming at any mobility characteristic block, obtaining a node set to be deployed of each mobility characteristic block, obtaining all edge node subsets of each node set to be deployed, wherein the edge node subsets comprise at least one node to be deployed, and calculating service delay and service deployment cost of the edge node subsets aiming at any edge node subset according to the service requests of the users and service request estimators received by each node to be deployed, according to the service delay of each edge node subset and the service deployment cost, a target edge node set is selected for deploying the edge service corresponding to the service request of each user, and the current user service requirement is learned according to the historical user requirement by utilizing the mobility characteristic information of the user, so that the problem of mobile edge application service deployment caused by mobility in the prior art is solved.
In one possible embodiment, in
Figure GDA0003191721170000172
In the case of a non-empty set, selecting a target edge node set for deploying an edge service corresponding to the service request of each user according to the service delay of each edge node subset and the cost of deploying the service includes:
in that
Figure GDA0003191721170000173
In the case of (a) in (b),
Figure GDA0003191721170000174
beta, L are constants, LtExpressed as a set of target edge nodes; determining a target edge node set l according to the following formulat：
Figure GDA0003191721170000181
In that
Figure GDA0003191721170000182
In the case of (a) in (b),
Figure GDA0003191721170000183
determining a target edge node set according to the following formula:
Figure GDA0003191721170000184
wherein the content of the first and second substances,
Figure GDA0003191721170000185
in one possible embodiment, in
Figure GDA0003191721170000186
In the case of an empty set, selecting a target edge node set to deploy an edge service corresponding to the service request of each user according to the service delay of each edge node subset and the cost of deploying the service includes:
determining a target edge node set according to the following formula:
Figure GDA0003191721170000187
referring to fig. 2, fig. 2 is a schematic diagram of an edge service deployment apparatus according to an embodiment of the present application, where the apparatus is applied to a manager in an edge service deployment system, and the edge service deployment system further includes a cloud platform and a plurality of edge nodes; the above-mentioned device includes:
the first acquiring module 210 is configured to acquire a mobility feature space, where the mobility feature space includes a plurality of mobility feature blocks, mobility feature information has a corresponding relationship with the mobility feature blocks, mobility feature information in different mobility feature blocks is different, and the mobility feature information indicates a mobility feature of a user.
A second collecting module 220, configured to obtain the historical service request quantity of each mobility feature block; calculating the service request estimation quantity of each mobility characteristic block of the current time slice according to the historical service request quantity of each mobility characteristic block; the time slice represents a time period in a preset time period;
a third collecting module 230, configured to obtain mobility feature information of each user in a current time slice, and determine a mobility feature block corresponding to each user according to the mobility feature information of each user;
a fourth acquisition module 240, configured to acquire service requests of users received by each edge node in a current time slice, use the edge nodes whose number of received service requests is smaller than a preset service request number threshold as nodes to be deployed, and determine, for any mobility feature block, a service request of a user corresponding to the mobility feature block in the current time slice; obtaining a node set to be deployed of each mobility characteristic block;
a fifth acquiring module 250, configured to acquire all edge node subsets of each to-be-deployed node set, where the edge node subsets include at least one to-be-deployed node;
a calculating module 260, configured to calculate, for any edge node subset, service delay and service deployment cost of the edge node subset according to the service request of the user received by each node to be deployed and the service request estimator;
a selecting module 270, configured to select a target edge node set for deploying the edge service corresponding to the service request of each user according to the service delay of each edge node subset and the cost of deploying the service.
In one possible embodiment, the predetermined time period is divided into T discrete time slices; denote the mobility feature space as χ ═ 0,1]DD is a mobility characteristic information dimension, and x is a D-dimensional mobility characteristic space;
the fourth acquisition module 240 is specifically configured to:
setting the preset service request threshold according to the following formula:
Figure GDA0003191721170000191
wherein 1 is more than or equal to alpha and is more than 0; α is the Herdel index;
the set of nodes to be deployed is represented as:
Figure GDA0003191721170000192
wherein N is a set of edge nodes within a time slice t;
Figure GDA0003191721170000193
for the set of users on edge node N within a time slice T, T belongs to {1, …, T }, N belongs to N; m represents any user and satisfies
Figure GDA0003191721170000194
ptA set of mobility feature blocks for time slice t, where,
Figure GDA0003191721170000195
representing mobility characteristic blocks to which the user m belongs in the time slice t;
Figure GDA0003191721170000201
representing mobility feature blocks
Figure GDA0003191721170000202
Number of service requests;
Figure GDA0003191721170000203
a set of nodes to be deployed representing time slice t.
In one possible embodiment of the method according to the invention,
Figure GDA0003191721170000204
is denoted as LjWhere j ∈ {1, …, k }; k is the number of edge node subsets; the fourth acquisition module 240 is specifically configured to:
each edge node subset L is calculated according to the following formulajService delay of (2):
Figure GDA0003191721170000205
wherein:
Figure GDA0003191721170000206
Figure GDA0003191721170000207
Figure GDA0003191721170000208
wherein the content of the first and second substances,
Figure GDA0003191721170000209
is the service latency of a subset of the edge nodes,
Figure GDA00031917211700002010
Figure GDA00031917211700002011
representing the uncertainty of the estimate;
Figure GDA00031917211700002012
blocking as p for mobility featurestIs in the edge server subset Lj(n∈Lj) Number of service requests;
Figure GDA00031917211700002013
as a motion feature vector of
Figure GDA00031917211700002014
The mobility characteristics of the user m to which the user m belongs;
Figure GDA00031917211700002015
representing mobility feature blocks
Figure GDA00031917211700002016
A total service request estimator at a current time slice;
Figure GDA00031917211700002017
representing mobility characteristics is partitioned into
Figure GDA00031917211700002018
The service request amount of (2);
Figure GDA00031917211700002019
is blocked for mobility characteristics as
Figure GDA00031917211700002020
The set of user service requests of (a),
Figure GDA00031917211700002021
wherein the content of the first and second substances,
Figure GDA0003191721170000211
wherein the content of the first and second substances,
Figure GDA0003191721170000212
representing the response time delay when the edge node provides service;
Figure GDA0003191721170000213
representing response time delay when the cloud platform provides the service;
Figure GDA0003191721170000214
wherein, λ is the input data size of the edge service corresponding to the service request of the user; mu is a calculation resource period required for calculating the edge service corresponding to the service request of the user;
Figure GDA0003191721170000215
is the transmission rate between user m and edge node n in time slice t, fnIs the processing speed of the edge node n; f. ofCThe processing speed of the cloud platform; ctThe transmission rate between the edge node n and the cloud platform is obtained; h istSending a service request to a user until the user receives the time for providing a service response for the user by the cloud platform;
calculating each edge node child according to the following formulaCollection LjDeployment service cost of (2):
Figure GDA0003191721170000216
wherein:
Figure GDA0003191721170000217
wherein, ω is the deployment cost of deploying edge service at an edge node; f (omega. L)jL) is at the edge node subset LjThe deployment cost of the edge node in (1) to deploy the edge service.
In one possible embodiment, in
Figure GDA0003191721170000218
In the case of a non-empty set, the selecting module 270 is specifically configured to:
in that
Figure GDA0003191721170000219
In the case of (a) in (b),
Figure GDA00031917211700002110
beta, L are constants, LtExpressed as a set of target edge nodes; determining a target edge node set l according to the following formulat：
Figure GDA00031917211700002111
In that
Figure GDA0003191721170000221
In the case of (a) in (b),
Figure GDA0003191721170000222
determining a target edge node set according to the following formula:
Figure GDA0003191721170000223
wherein the content of the first and second substances,
Figure GDA0003191721170000224
in one possible embodiment, in
Figure GDA0003191721170000225
In the case of an empty set, the selecting module 270 is specifically configured to:
determining a target edge node set according to the following formula:
Figure GDA0003191721170000226
with regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
An embodiment of the present invention further provides an electronic device, as shown in fig. 3, where fig. 3 is a schematic diagram of the electronic device according to the embodiment of the present application, and the electronic device includes: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 are communicated with each other through the communication bus 340,
the memory 330 is used for storing computer programs;
the processor 310 is configured to implement the following steps when executing the computer program stored in the memory 330:
acquiring a mobility feature space, wherein the mobility feature space comprises a plurality of mobility feature blocks, mobility feature information and the mobility feature blocks have a corresponding relation, the mobility feature information in different mobility feature blocks is different, and the mobility feature information represents the mobility features of a user;
acquiring the historical service request quantity of each mobility characteristic block; calculating the service request estimation quantity of each mobility characteristic block of the current time slice according to the historical service request quantity of each mobility characteristic block; the time slice represents a time period in a preset time period;
acquiring mobility characteristic information of each user in a current time slice, and respectively determining a mobility characteristic block corresponding to each user according to the mobility characteristic information of each user;
the method comprises the steps of obtaining service requests of users received by edge nodes in a current time slice, taking the edge nodes with the number of the received service requests smaller than a preset service request number threshold value as nodes to be deployed, and determining the service requests of the users corresponding to the mobility feature blocks in the current time slice aiming at any mobility feature block; obtaining a node set to be deployed of each mobility characteristic block;
acquiring all edge node subsets of each node set to be deployed, wherein the edge node subsets comprise at least one node to be deployed;
aiming at any edge node subset, calculating the service delay and the service deployment cost of the edge node subset according to the service request of the user received by each node to be deployed and the service request estimators;
and selecting a target edge node set for deploying the edge service corresponding to the service request of each user according to the service delay of each edge node subset and the service deployment cost.
In one possible embodiment, the processor 310, when executing the program stored in the memory 330, may further implement any of the edge service deployment methods described above.
The communication bus mentioned in the above terminal may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the terminal and other equipment.
The Memory may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
In yet another embodiment of the present invention, a computer-readable storage medium is further provided, which stores instructions that, when executed on a computer, cause the computer to execute any of the above-mentioned edge service deployment methods in the above-mentioned embodiments.
In yet another embodiment, a computer program product containing instructions is provided, which when run on a computer, causes the computer to perform any of the above-described edge service deployment methods in the embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions described above in accordance with the embodiments of the invention may be generated, in whole or in part, when the computer program instructions described above are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, the technical features in the various alternatives can be combined to form the scheme as long as the technical features are not contradictory, and the scheme is within the scope of the disclosure of the present application. Relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the same element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the apparatus, the electronic device, and the storage medium, since they are substantially similar to the method embodiments, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiments.
The above description is only a preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims (5)

1. The edge service deployment method is characterized by being applied to a manager in an edge service deployment system, wherein the edge service deployment system further comprises a cloud platform and a plurality of edge nodes; the method comprises the following steps:
acquiring a mobility feature space, wherein the mobility feature space comprises a plurality of mobility feature blocks, mobility feature information and the mobility feature blocks have a corresponding relation, the mobility feature information in different mobility feature blocks is different, and the mobility feature information represents the mobility features of a user;
acquiring the historical service request quantity of each mobility characteristic block; calculating the service request estimation quantity of each mobility characteristic block of the current time slice according to the historical service request quantity of each mobility characteristic block; the time slice represents a time period in a preset time period;
acquiring mobility characteristic information of each user in a current time slice, and respectively determining a mobility characteristic block corresponding to each user according to the mobility characteristic information of each user;
the method comprises the steps of obtaining service requests of users received by edge nodes in a current time slice, taking the edge nodes with the number of the received service requests smaller than a preset service request number threshold value as nodes to be deployed, and determining the service requests of the users corresponding to the mobility feature blocks in the current time slice aiming at any mobility feature block; obtaining a node set to be deployed of each mobility characteristic block;
acquiring all edge node subsets of each node set to be deployed, wherein the edge node subsets comprise at least one node to be deployed;
aiming at any edge node subset, calculating service time delay and service deployment cost of the edge node subset according to the service request of the user received by each node to be deployed and the service request estimators;
and selecting a target edge node set for deploying the edge service corresponding to the service request of each user according to the service delay of each edge node subset and the cost of deploying the service.
2. The method of claim 1, wherein the predetermined time period is divided into T discrete time slices; denote the mobility feature space as χ ═ 0,1]DD is a mobility characteristic information dimension, and x is a D-dimensional mobility characteristic space;
the method comprises the steps of obtaining service requests of users received by edge nodes in a current time slice, taking the edge nodes with the number of the received service requests smaller than a preset service request number threshold value as nodes to be deployed, and determining the service requests of the users corresponding to a mobility feature block in the current time slice aiming at any mobility feature block; obtaining a set of nodes to be deployed of each mobility characteristic block, including:
setting the preset service request quantity threshold value according to the following formula:
Figure FDA0003191721160000021
wherein, 1 is more than or equal to alpha and is more than 0, and t is the current time slice; α is the Herdel index;
the set of nodes to be deployed is represented as:
Figure FDA0003191721160000022
wherein N is a set of edge nodes within a time slice t;
Figure FDA0003191721160000023
for the set of users on edge node N within a time slice T, T belongs to {1, …, T }, N belongs to N; m represents any user and satisfies
Figure FDA0003191721160000024
ptA set of mobility feature blocks for time slice t, where,
Figure FDA0003191721160000025
Figure FDA0003191721160000026
representing mobility characteristic blocks to which the user m belongs in the time slice t;
Figure FDA0003191721160000027
representing mobility feature blocks
Figure FDA0003191721160000028
Number of service requests;
Figure FDA0003191721160000029
a set of nodes to be deployed representing time slice t.
3. The edge service deployment device is characterized in that a manager in an edge service deployment system is applied, and the edge service deployment system further comprises a cloud platform and a plurality of edge nodes; the device comprises:
the mobile terminal comprises a first acquisition module, a second acquisition module and a processing module, wherein the first acquisition module is used for acquiring a mobile characteristic space, the mobile characteristic space comprises a plurality of mobile characteristic blocks, mobile characteristic information and the mobile characteristic blocks have a corresponding relation, the mobile characteristic information in different mobile characteristic blocks is different, and the mobile characteristic information represents the mobile characteristic of a user;
the second acquisition module is used for acquiring the historical service request quantity of each mobility characteristic block; calculating the service request estimation quantity of each mobility characteristic block of the current time slice according to the historical service request quantity of each mobility characteristic block; the time slice represents a time period in a preset time period;
the third acquisition module is used for acquiring the mobility characteristic information of each user in the current time slice and respectively determining the mobility characteristic blocks corresponding to each user according to the mobility characteristic information of each user;
the fourth acquisition module is used for acquiring the service requests of the users received by each edge node in the current time slice, taking the edge nodes with the number of the received service requests smaller than a preset service request number threshold value as nodes to be deployed, and determining the service requests of the users corresponding to the mobility feature blocks in the current time slice aiming at any mobility feature block; obtaining a node set to be deployed of each mobility characteristic block;
a fifth acquisition module, configured to acquire all edge node subsets of each to-be-deployed node set, where the edge node subsets include at least one to-be-deployed node;
the computing module is used for computing the service delay and the service deployment cost of any edge node subset according to the service request of the user received by each node to be deployed and each service request estimator;
and the selecting module is used for selecting a target edge node set for deploying the edge service corresponding to the service request of each user according to the service delay of each edge node subset and the service deployment cost.
4. The apparatus of claim 3, wherein the predetermined time period is divided into T discrete time slices; denote the mobility feature space as χ ═ 0,1]DD is a mobility characteristic information dimension, and x is a D-dimensional mobility characteristic space;
the fourth acquisition module is specifically configured to:
setting the preset service request quantity threshold value according to the following formula:
Figure FDA0003191721160000031
wherein 1 is more than or equal to alpha and is more than 0; t is the current time slice, and α is the Hull exponent;
the set of nodes to be deployed is represented as:
Figure FDA0003191721160000032
wherein N is a set of edge nodes within a time slice t;
Figure FDA0003191721160000033
for the set of users on edge node N within a time slice T, T belongs to {1, …, T }, N belongs to N; m represents any user and satisfies
Figure FDA0003191721160000034
ptA set of mobility feature blocks for time slice t, where,
Figure FDA0003191721160000035
Figure FDA0003191721160000036
representing mobility characteristic blocks to which the user m belongs in the time slice t;
Figure FDA0003191721160000041
representing mobility feature blocks
Figure FDA0003191721160000042
Number of service requests;
Figure FDA0003191721160000043
a set of nodes to be deployed representing time slice t.
5. An electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus;
the memory is used for storing a computer program;
the processor, when executing the computer program stored in the memory, implementing the method of any of claims 1-2.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108933815A (en) * 2018-06-15 2018-12-04 燕山大学 A kind of control method of the Edge Server of mobile edge calculations unloading
CN110187973A (en) * 2019-05-31 2019-08-30 浙江大学 A kind of service arrangement optimization method towards edge calculations
CN110381131A (en) * 2019-07-15 2019-10-25 北京奇艺世纪科技有限公司 Implementation method, mobile terminal, server and the storage medium of MEC node identification
CN110662238A (en) * 2019-10-24 2020-01-07 南京大学 Reinforced learning scheduling method and device for burst request under edge network
CN111163178A (en) * 2020-01-10 2020-05-15 中国地质大学（武汉） Game theory-based service deployment and task unloading method in edge computing
CN111371603A (en) * 2020-02-27 2020-07-03 长沙市源本信息科技有限公司 Service instance deployment method and device applied to edge computing

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105975330B (en) * 2016-06-27 2019-06-18 华为技术有限公司 A kind of virtual network function dispositions method that network edge calculates, device and system
US10778800B2 (en) * 2018-03-04 2020-09-15 Netskrt Systems, Inc. System and apparatus for propagating content throughout a network using a mobile environment
CN110096318B (en) * 2019-05-08 2021-01-08 北京邮电大学 Task unloading method and device based on mobile edge calculation

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108933815A (en) * 2018-06-15 2018-12-04 燕山大学 A kind of control method of the Edge Server of mobile edge calculations unloading
CN110187973A (en) * 2019-05-31 2019-08-30 浙江大学 A kind of service arrangement optimization method towards edge calculations
CN110381131A (en) * 2019-07-15 2019-10-25 北京奇艺世纪科技有限公司 Implementation method, mobile terminal, server and the storage medium of MEC node identification
CN110662238A (en) * 2019-10-24 2020-01-07 南京大学 Reinforced learning scheduling method and device for burst request under edge network
CN111163178A (en) * 2020-01-10 2020-05-15 中国地质大学（武汉） Game theory-based service deployment and task unloading method in edge computing
CN111371603A (en) * 2020-02-27 2020-07-03 长沙市源本信息科技有限公司 Service instance deployment method and device applied to edge computing

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
基于实测数据集预测用户请求行为对主动边缘缓存的影响;戚凯强，杨晨阳，韩圣千;《信号处理》;20190425;全文 *
面向移动边缘计算的边缘服务器部署及资源分配研究;赵磊;《中国优秀硕士学位论文全文数据库信息科技辑》;20190215;全文 *

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