CN113347255B - Edge server site selection deployment model and solving method thereof - Google Patents

Edge server site selection deployment model and solving method thereof Download PDF

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CN113347255B
CN113347255B CN202110618740.1A CN202110618740A CN113347255B CN 113347255 B CN113347255 B CN 113347255B CN 202110618740 A CN202110618740 A CN 202110618740A CN 113347255 B CN113347255 B CN 113347255B
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base station
individuals
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CN113347255A (en
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鲁蔚锋
马鸿宇
徐佳
徐力杰
蒋凌云
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides an edge server site selection deployment model and a solving method thereof, wherein the edge server site selection deployment model comprises a base station and edge servers, i is defined to represent the edge servers, j is defined to represent the base station, and each edge server is connected with one or more base stations; and establishing the edge server site-selection deployment model so as to minimize the sum of average delay and cost expenditure between the edge server and the base station. The invention not only can effectively reduce the placement cost of the mobile edge server, but also can reduce the communication delay between the edge server and the user and accelerate the service response through the geographic position where the mobile edge server is placed. The model is an np-hard problem and is difficult to solve in constant time, and an optimal or near-optimal solution can be solved in constant time by adopting a genetic algorithm.

Description

Edge server site selection deployment model and solving method thereof
Technical Field
The invention relates to an edge server site selection deployment model and a solving method thereof, belonging to the field of edge computing communication.
Background
With the advent of the 5G age, mobile network services are no longer simple handsets, but rather various types of devices, such as VR, tablet, car, etc. Service scenarios are also becoming increasingly diverse, such as mobile broadband, large-scale machine type communications, industrial internet, etc. Therefore, mobile networks must meet higher requirements in terms of mobility, security, latency, and reliability.
In order to meet the high bandwidth, low latency requirements required for high speed mobile network development and to alleviate network load, mobile edge computing (Mobile Edge Computing, MEC) has evolved.
The key technology related to mobile edge computing is mainly divided into four technologies of edge cloud placement, computing unloading, service migration and crowd-sourced cooperation. We have mainly studied the problem of edge server placement in a mobile edge computing environment. Migrating computing tasks from the core network to the network edge, reducing core network congestion and data propagation delay is a major goal of mobile edge computing. However, there is no clear definition of the edge cloud, i.e. where edge servers should be placed, and thus edge server placement problems arise. The problem of edge server placement is that in a certain geographical position range, constraint limits of users, resources and the like are considered, and on the premise of meeting user requirements and targets, a proper geographical position is selected for the edge server according to a certain strategy so as to achieve the purposes of high resource utilization rate and small network time delay.
Placing edge servers in a mobile edge computing environment is a challenge. The location of the edge server is critical to the access delay of the mobile user and the resource utilization of the edge server, especially in smart cities, which comprise hundreds or thousands of base stations through which the mobile user accesses the edge server. Due to the large scale of these networks, the inefficient placement of edge servers will result in lengthy access delays and severely unbalanced workload among the edge servers, some of which will be overloaded, while others will be underutilized and even idle. Thus, the policy layout of the edge server will significantly improve the performance of various mobile applications, such as access latency of the edge server.
In view of the foregoing, it is necessary to provide an edge server site-selection deployment model and a solution method thereof to solve the above problems.
Disclosure of Invention
The invention aims to provide an edge server site selection deployment model, which not only can reduce time delay between a base station and an edge server, but also can reduce expenditure for deploying the edge server.
In order to achieve the above object, the present invention provides an edge server site selection deployment model, which includes a base station and edge servers, where i represents the edge servers, j represents the base stations, and each edge server is connected to one or more of the base stations; establishing an edge server site-specific deployment model to minimize the sum of average delay and cost expenditure between the edge server and the base station, the edge server site-specific deployment model being as follows:
Minimize:∑ i,j∈N (t*c ij *x ij +(1-t)*cost i ) (1)
Subject to∑ i∈N x ij ＝1 for eachj∈N， (2)
x ij ≤y i for each i,j∈N， (3)
∑ i∈N y i ＝k， (4)
x ij ∈{0,1},for each i,j∈N， (5)
y i ∈{0,1}for each i,j∈N， (6)
wherein y is i (i.e.N) indicates that the edge server alternate address is selected, x i,j (i, j.epsilon.N) means that the edge server is connected to the base station, c ij Representing the distance between the edge server and the base station, cost i Representing the arrangement cost of each edge server, and t represents a weight coefficient;
constraint (2) indicates that each base station can only connect to one edge server;
constraint (3) indicates that edge server i has been selected if edge server i is connected to base station j;
constraint (4) indicates that the number of alternative points of the selected edge server is k;
constraint (5) and constraint (6) represent x, respectively ij ,y i Is a 0-1 variable.
As a further improvement of the present invention, the c ij The calculation formula of (2) is as follows:
Figure GDA0004215389930000031
wherein l(s) i ) Representing the position coordinates of the edge server, l (b) j ) Representing the location coordinates of the base station.
As a further improvement of the present invention, the level node of the edge server is an undirected graph g= (V, E); where V represents the set of nodes of the base station and the candidate points of the edge server and E represents the link between the base station and the edge server.
As a further improvement of the present invention, the v=b u S, where B represents a set of nodes of the base station and S represents a set of candidate points of the edge server; b j ∈B(j∈N + ) S is the node of the base station i ∈S(i∈N + ) Is an alternative point to the edge server.
The invention also aims to provide a solving method for the edge server site selection deployment, which can solve the edge server site selection position of the optimal solution or the near optimal solution.
In order to achieve the above object, the present invention provides a solution method for edge server site selection deployment, where the solution method is applied to the above edge server site selection deployment model, and the solution method includes the following steps:
step 1, the position coordinates of the base station and the position coordinates of the edge server are calculated according to a calculation formula:
Figure GDA0004215389930000032
converting the distance between the base station and the alternative point of the edge server; wherein l(s) i ) Representing the position coordinates of the edge server, l (b) j ) Representing the position coordinates of the base station, b j E B (j=1, 2,., m) is the node of the base station, s i S (i=1, 2,., n) is an alternative point to the edge server;
step 2, setting genetic algorithm parameters; the genetic algorithm parameters comprise an initialized population scale, a base factor of each individual, a gene crossover probability, a gene mutation probability and a maximum iteration number;
step 3, encoding the alternative points of the edge server;
step 4, initializing a population, and selecting k genes as one individual from the candidate points of the edge server;
step 5, carrying out iterative evolution for appointed times;
step 6, calculating the average time delay and the cost of the fees of each individual;
step 7, calculating the fitness: taking the inverse of the average time delay and cost; the fitness is a scale for measuring the quality of individuals, and the number of the individuals to reproduce is determined or whether the individuals die is determined according to the fitness;
step 8, selecting surviving individuals by adopting a roulette manner;
step 9, random cross variation;
step 10, random variation operation: randomly selecting variant individuals and randomly selecting chromosome variation;
step 11, turning to individual evaluation, starting a new cycle: turning to step 6 to start the cycle;
and step 12, after the circulation is finished, selecting the optimal individual as a solution.
As a further improvement of the invention, in step 2, the population is a collection of individuals; the individual is a collection of genes; the gene crossover probability is the partial code of randomly exchanging two individuals; the genetic variation is a random alteration of one or more codes of an individual.
As a further improvement of the present invention, step 8 comprises the following specific steps:
step 8.1, calculating fitness ratio, i.e. selection probability of each individual
Figure GDA0004215389930000041
Step 8.2, calculating the cumulative probability q of each individual i The larger the span is, the easier the selection is
Figure GDA0004215389930000042
Step 8.3, randomly generating r E [0,1 ]]If q i >r, then select individual x i ；
And 8.4, selecting the individuals selected by the roulette algorithm as surviving individuals, eliminating the worst T individuals and replacing the worst T individuals by the optimal T individuals.
As a further improvement of the present invention, step 9 comprises the following specific steps:
step 9.1, randomly dividing individuals in the population into two groups, and randomly selecting the two individuals for cross operation;
step 9.2, randomly generating two crossing bits min and max (min < max) within the range of [1, sz ] (sz is the number of cities), namely within the chromosome length, and exchanging the [ min, max ] areas of two individuals;
step 9.3, treating the repeated chromosome parts, randomly mutating the repeated chromosomes.
As a further improvement of the invention, the steps are also provided between the step 1 and the step 2: for the original data c ij And cost i Normalization processing is performed to map the result value to [0-1 ]]Between them; the calculation formula of the normalization processing is as follows:
Figure GDA0004215389930000051
as a further improvement of the invention, the coding mode in step 3 is decimal coding.
The beneficial effects of the invention are as follows: the invention not only can effectively reduce the placement cost of the mobile edge server, but also can reduce the communication delay between the edge server and the user and accelerate the service response through the geographic position where the mobile edge server is placed. The model is an np-hard problem and is difficult to solve in constant time, and an optimal or near-optimal solution can be solved in constant time by adopting a genetic algorithm.
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FIG. 1 is a schematic diagram of an edge server site-selection deployment model of the present invention.
FIG. 2 is a flow chart of the genetic algorithm of the present invention.
FIG. 3 is a flow chart of the roulette algorithm of the present invention.
Fig. 4 is a schematic diagram of the comparison of the results of the delay and cost of the genetic algorithm with other algorithms at each edge server in the present invention.
Fig. 5 is a schematic diagram of the sum of average time delay and cost of the genetic algorithm and other algorithms at the base station and the edge server in the present invention.
FIG. 6 is a schematic diagram of the results of the present invention solved by comparing different iterations of the genetic algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
The invention discloses an edge server site selection deployment model, which comprises a base station and an edge server. Definition i represents edge servers, j represents base stations, each edge server being connected to one or more base stations; establishing an edge server site deployment model to minimize the sum of average delay and cost between the edge server and the base station, the edge server site deployment model being as follows:
Minimize：∑ i，j∈N (t*c ij *x ij +(1-t)*cost i ) (1)
Subject to∑ i∈N x ij ＝1 for each j∈N， (2)
x ij ≤y i for each i，j∈N， (3)
∑ i∈N y i ＝k， (4)
x ij ∈{0，1}，for each i，j∈N， (5)
y i ∈{0，1}for each i，j∈N， (6)
wherein y is i (i.e.N) indicates that the edge server alternate address is selected, x i，j (i, j.epsilon.N) means that the edge server is connected to the base station, c ij Representing the distance between the edge server and the base station, cost i Representing the per edge server placement cost, t represents the weighting factor. Constraint (2) indicates that each base station can only connect to one edge server; constraint (3) indicates that edge server i has been selected if edge server i is connected to base station j; constraint (4) indicates that the number of alternative points of the selected edge server is k; constraint (5) and constraint (6) represent x, respectively ij ，y i Is a variable of 0-1。
The distance calculation formula between the edge server and the base station is as follows:
Figure GDA0004215389930000061
wherein l(s) i ) Representing the position coordinates of the edge server, l (b) j ) Representing the location coordinates of the base station. The level node of the edge server can be regarded as an undirected graph g= (V, E). Where v=b. Definition b j ∈B(j∈N + ) Is a node of the base station, s i ∈S(i∈N + ) Is an alternative point to the edge server. E denotes a link between the base station and the edge server. Each link not only represents a physical connection between a base station and a mobile edge server, but also describes a logical connection between a base station and a mobile edge server.
First a set of concepts is defined:
genotype: internal expression of the trait chromosome.
Evolution: the population gradually adapts to the living environment, and the quality is improved continuously. The evolution of organisms is in the form of populations.
Fitness degree: the adaptation of a species to the living environment is measured. Selecting: several individuals are selected from the population with a certain probability. In general, the selection process is a process of winner and winner based on fitness.
Replication: upon cell division, the genetic material DNA is transferred by replication into newly generated cells, which inherit the genes of the old cells.
Crossing: the DNA is cut off at a certain same position of the two chromosomes, and the front and rear strings are respectively crossed and combined to form two new chromosomes. Also known as gene recombination or hybridization; the crossover probability is the partial code of randomly swapping two individuals.
Variation: some replication errors may occur (with very little probability) during replication, and the mutation produces a new chromosome, exhibiting a new trait. Genetic variation is the random alteration of one or more codes of an individual.
Encoding: the genetic information in DNA is arranged in a pattern over a long chain. Genetic coding can be seen as a mapping from phenotype to genotype.
Individuals: the entity with characteristics of chromosome is the collection of genes.
Population: a collection of individuals, the number of individuals within the collection being referred to as a population.
The solution method for edge server site selection deployment will be described in detail below.
The specific solving method comprises the following steps: step 1, the position coordinates of the base station and the position coordinates of the edge server are calculated according to a calculation formula:
Figure GDA0004215389930000071
converting the distance between the base station and the alternative point of the edge server; wherein l(s) i ) Representing the position coordinates of the edge server, l (b) j ) Representing the position coordinates of the base station, b j E B (j=1, 2,., m) is the node of the base station, s i S (i=1, 2,., n) is an alternative point to the edge server;
step 2, setting genetic algorithm parameters; the genetic algorithm parameters comprise an initialized population scale, a base factor of each individual, a gene crossover probability, a gene mutation probability and a maximum iteration number;
step 3, encoding the alternative points of the edge server;
step 4, initializing a population, and selecting k genes as one individual from the candidate points of the edge server;
step 5, carrying out iterative evolution for appointed times;
step 6, calculating the average time delay and the cost of the fees of each individual;
step 7, calculating the fitness: taking the inverse of the average time delay and cost; the fitness is a scale for measuring the quality of individuals, and the number of the individuals to reproduce is determined or whether the individuals die is determined according to the fitness;
step 8, selecting surviving individuals by adopting a roulette mode:
(8.1) calculating fitness ratio, i.e., probability of selection for each individual
Figure GDA0004215389930000081
(8.2) calculating cumulative probability q of each individual i The larger the span is, the easier the selection is
Figure GDA0004215389930000082
(8.3) randomly generating rE [0,1 ]]If q i >r, then select individual x i ；
(8.4) selecting surviving individuals by the roulette algorithm, eliminating the worst T individuals and replacing the worst T individuals by the optimal T individuals.
Step 9, random cross variation:
(9.1) randomly dividing individuals in the population into two groups, and randomly selecting the two individuals for crossing operation; (9.2) first randomly generating two crossing bits min and max (min < max) within the range of [1, sz ] (sz is the number of cities), i.e. within the chromosome length, and exchanging the [ min, max ] regions of the two individuals;
(9.3) treating the repeated portions of the chromosome, randomly mutating the repeated chromosomes.
Step 10, random variation operation: randomly selecting variant individuals and randomly selecting chromosome variation;
step 11, turning to individual evaluation, starting a new cycle: turning to step 6 to start the cycle;
and step 12, after the circulation is finished, selecting the optimal individual as a solution.
Specifically, as shown in fig. 1, initially, the candidate points of the mobile edge server and the topology of the base station network have 7 base stations in total, and 5 candidate points of deployment of the mobile edge server. Let us assume first that we need to select 3 points as actual deployment points among the deployment alternatives of 5 mobile edge servers. Each base station and mobileThe edge server candidate points each have a coordinate (x, y), and each mobile edge server candidate point has a deployment cost i . Calculating Euclidean distance c between each base station and each mobile edge server using formula (7) ij 。
If there is a normalized demand for data, then for c ij Cost i And (5) carrying out normalization processing. As shown in fig. 2, the genetic algorithm parameters are determined according to step 2. Namely, the maximum iteration number m, the initial population size num, each individual basis factor k, the gene crossover probability cross and the gene mutation probability mutation are determined. As shown in fig. 6, the maximum number of iterations affects the solution result.
In step 3, decimal is used to encode the genes, e.g., [0,1,2] represents that the individual has selected edge server candidates 0,1, 2. In step 4, num×k two-dimensional arrays are created as an initialization population array. Each individual gene is randomly initialized, and k points are randomly selected from the edge server candidate points to serve as the genes of one individual. And step 5 to step 7, traversing the sum of the shortest time delay and the cost from each base station to the edge server, and then obtaining the sum of the average time delay and the cost of the individual. The individuals are then ranked from large to small according to the sum of the average delay and the cost.
As shown in fig. 3, the surviving individuals are then calculated by the step 8 roulette algorithm. The worst n individuals were eliminated and replaced by the best n individuals.
Randomly exchanging genes of any individual in step 9, for example: exchange of [0,1,2] with [1,3,4] yields [0,2,3] with [1, 4]. If a chromosomal duplication occurs, the chromosome is mutated, for example: [1, 4] is mutated to [1,2,4]. Step 10 performs random chromosomal variation on random individuals. A certain segment of a gene of a certain individual is randomly selected and then randomly altered, for example: [1,2,4] variant is [1,2,3]. And continuing to calculate the fitness, and starting the next iteration until the designated iteration times are reached. Outputting the best individual. I.e., edge server deployment scheme.
As shown in fig. 4 and fig. 5, by comparing different algorithms through experiments, the invention can solve the optimal solution or the near optimal solution of the np problem in constant time, and can reduce the average time delay and cost between the edge server and the base station in the edge server deployment stage. Meanwhile, the problem that the problem is difficult to solve in a constant time in terms of time complexity is avoided.
In summary, by setting the edge server site selection deployment model and the solving method thereof, the invention not only can effectively reduce the placement cost of the mobile edge server, but also can reduce the communication delay between the edge server and the user and accelerate the service response through the geographic position where the mobile edge server is placed. The model is an np-hard problem and is difficult to solve in constant time, and an optimal or near-optimal solution can be solved in constant time by adopting a genetic algorithm.
The above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present invention.

Claims (6)

1. An edge server site selection deployment method is characterized in that: the method comprises the following steps:
step 1, the position coordinates of the base station and the position coordinates of the edge server are calculated according to a calculation formula:
Figure QLYQS_1
calculating the distance between the base station and the edge server; wherein c ij Representing the distance between the edge server and the base station, definition i representing the edge server, j representing the base station, l(s) i ) Representing the position coordinates of the edge server, l (b) j ) Representing the position coordinates of the base station, b j E B is the node of the base station, B represents the set of the nodes of the base station, s i S is an alternative point of the edge server, S represents a set of alternative points of the edge server, and x is a coordinate correction coefficient;
step 2, setting genetic algorithm parameters; the genetic algorithm parameters comprise an initialized population scale, a base factor of each individual, a gene crossover probability, a gene mutation probability and a maximum iteration number;
step 3, encoding the nodes of the edge server;
step 4, initializing a population, and selecting k genes as one individual from the candidate points of the edge server;
step 5, carrying out iterative evolution for appointed times;
step 6, calculating the average time delay and the cost of the fees of each individual;
step 7, calculating the fitness: taking the inverse of the average time delay and cost; the fitness is a scale for measuring the quality of individuals, and the number of the individuals to reproduce is determined or whether the individuals die is determined according to the fitness;
step 8, selecting surviving individuals by adopting a roulette manner, wherein the step 8 comprises the following specific steps:
step 8.1, calculating fitness ratio, i.e. selection probability of each individual
Figure QLYQS_2
M is the number of individuals, f (x i ) For individual x i Is adapted to f (x) j ) For individual x j Is adapted to the degree of adaptation of (a);
step 8.2, calculating the cumulative probability q of each individual i The span is larger and is easier to select, corresponding to the span on the turntable
Figure QLYQS_3
Step 8.3, randomly generating a random value r E [0,1 ]]If q i >r, then select individual x i ；
Step 8.4, selecting individuals selected by the roulette algorithm as surviving individuals, eliminating worst T individuals, and replacing the worst T individuals by optimal T individuals;
step 9, random cross mutation, wherein the step 9 comprises the following specific steps:
step 9.1, randomly dividing individuals in the population into two groups, and randomly selecting the two individuals for cross operation;
step 9.2, firstly, randomly generating two crossing bits min and max in the range of [1, sz ], namely in the length of a chromosome, and exchanging the [ min, max ] areas of two individuals, wherein sz is the number of city numbers, and min < max;
step 9.3, treating the repeated parts of the chromosome, and randomly mutating the repeated chromosome;
step 10, random variation operation: randomly selecting variant individuals and randomly selecting chromosome variation;
step 11, turning to individual evaluation, starting a new cycle: turning to step 6 to start the cycle;
and step 12, after the circulation is finished, selecting the optimal individual as a solution.
2. The edge server site-directed deployment method of claim 1, wherein: in step 2, the population is a collection of individuals; the individual is a collection of genes; the gene crossover probability is the partial code of randomly exchanging two individuals; the genetic variation is a random alteration of one or more codes of an individual.
3. The edge server site selection deployment method according to claim 1, wherein the steps between step 1 and step 2 are further provided with: for the original data c ij And cost i Normalization processing is performed to map the result value to [0-1 ]]Between, wherein cost i Representing a cost per said edge server arrangement; the calculation formula of the normalization processing is as follows:
Figure QLYQS_4
4. the edge server site-directed deployment method of claim 1, wherein: the coding mode in the step 3 is decimal coding.
5. An edge server site selection deployment system, characterized in that: the edge server site selection deployment system applies the edge server site selection deployment method of any one of claims 1-4, the edge server site selection deployment system comprising the base station and the edge servers, each of the edge servers being connected to one or more of the base stations; establishing the edge server site-selection deployment system to minimize the sum of average delay and cost expenditure between the edge server and the base station, wherein the edge server site-selection deployment system is as follows:
Minimize:∑ i,j∈N (t*c ij *x ij +(1-t)*cost i ) (1)
Subject to ∑ i∈N x ij ＝1 for each j∈N， (2)
x ij ≤y i for each i,j∈N， (3)
∑ i∈N y i ＝k， (4)
x ij ∈{0,1},for each i,j∈N， (5)
y i ∈{0,1}for each i,j∈N， (6)
wherein y is i X represents whether the edge server candidate address is selected i,j The edge server is connected with the base station, wherein i, j epsilon N, N represent natural numbers, and t represents weight coefficients;
constraint (2) indicates that each base station can only connect to one edge server;
constraint (3) indicates that edge server i has been selected if edge server i is connected to base station j;
constraint (4) indicates that the number of alternative points of the selected edge server is k;
constraint (5) and constraint (6) represent x, respectively ij ,y i Is a 0 or 1 variable.
6. The edge server site-directed deployment system according to claim 5, wherein: the c ij Is calculated according to the formula:
Figure QLYQS_5
when l(s) i )+l(b j ) When positive, x=1, when l (s i )+l(b j ) When negative, x is an imaginary number; the level node of the edge server is an undirected graph g= (V, E); wherein V represents a set of alternative points of the edge server and nodes of the base station, and E represents a link between the base station and the edge server; the v=b u S, where B represents a set of nodes of the base station and S represents a set of candidate points of the edge server; b j S is the node of the base station i Is an alternative point to the edge server.
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