User access control method based on bipartite graph matching in mobile edge computing networks

Abstract

The present invention discloses a user access control method based on bipartite graph matching in a mobile edge computing network, including: configuring a base station and K user devices in the adopted mobile edge computing network; the base station is configured with an MEC server with a maximum computing capacity of F CPU cycles; establishing a user access control optimization problem model; step two: converting the optimization problem model into a bipartite graph matching problem for optimizing channel allocation; using the Hungarian algorithm to solve the bipartite graph matching problem for optimizing channel allocation to obtain a maximum matching value; step three: obtaining a user access control decision based on the maximum matching value. Under the condition of completing the computing task before the deadline, the user devices accessing the MEC server, the amount of data uploaded by the user devices, and the allocated channels are optimized to maximize the number of user devices that can complete the computing task.

Description

Technical Field

[0001] The present invention relates to a user access control method based on bipartite graph matching in a mobile edge computing network, and belongs to the technical field of mobile edge computing. Background Art

[0002] In recent years, wireless communication technology has developed rapidly, and the application functions of user devices are increasing, aiming to enrich users' network life and improve their network experience. However, the computing power of user devices is usually limited, and it is difficult to handle some tasks that require large amounts of computing and are sensitive to latency. In order to make up for the insufficient computing power of user devices, the concept of mobile edge computing is proposed. In mobile edge computing, user devices can offload computing tasks to nearby mobile edge computing servers (MEC servers) and perform computing at the edge of the network, thereby improving the computing power of user devices and avoiding the high latency problem caused by offloading data to the remote cloud in the traditional offloading method. On the other hand, the computing resources of MEC are limited. Although offloading computing tasks to MEC servers saves computing resources and energy consumption of user devices, it also increases the energy consumption of data transmission. In order to maximize the number of users in the channel and enable more user devices to transmit data to the MEC server, the computing task is optimized in advance before being offloaded to the MEC server to maximize the number of users in the channel. Summary of the invention

[0003] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a user access control method based on bipartite graph matching in a mobile edge computing network. Under the condition of completing the computing task before the deadline, the user equipment accessing the MEC server, the amount of data uploaded by the user equipment and the allocated channel are optimized to maximize the number of user equipment that can complete the computing task.

[0004] To achieve the above object, the present invention provides a user access control method based on bipartite graph matching in a mobile edge computing network, comprising:

[0005] The adopted mobile edge computing network is configured with one base station and K user devices;

[0006] The base station is configured with a MEC server with a maximum computing capacity of F CPU cycles. Under the condition of completing the computing task before the deadline, the user equipment accessing the MEC server, the amount of data uploaded by the user equipment and the allocated channel are optimized to maximize the number of user equipment that can complete the computing task.

[0007] Prioritize, under the condition of completing the computing task before the deadline, optimize the user equipment connected to the MEC server, the amount of data uploaded by the user equipment and the allocated channels, which is achieved by the following steps:

[0008] Step 1: Establish a user access control optimization problem model;

[0009] Step 2: Convert the optimization problem model into a bipartite graph matching problem for optimizing channel allocation;

[0010] Use the Hungarian algorithm to solve the bipartite graph matching problem for optimizing channel allocation and obtain the maximum matching value;

[0011] Step 3: Get the user access control decision based on the maximum matching value.

[0012] Preferably, step one includes:

[0013] Establish user access control optimization problem model P1:

[0014] P1:

[0015] Constraint C1:

[0016] Constraint C2:

[0017] Constraint C3:

[0018] Constraint C4:

[0019] Constraint C5:0≤α k ≤1,k=1,....,K,

[0020] Constraint C6:β k,n ∈{0,1},k=1,...K,n=1,...N;

[0021] in, D is the set of connected user devices that can complete the computing task before the deadline. k is the data size of the calculation task, C k The number of CPU cycles required to complete a 1-bit computation task, F k is the local computing frequency for completing a 1-bit computing task; F represents the maximum computing capacity of the MEC server; the base station has N orthogonal channel resources with a bandwidth of B, N ≥ K;

[0022] is the set of channels, α k Calculate the offloading ratio of tasks for user device k; β k,n is the allocation factor between user equipment k and channel n, β k,n =1 means channel n is allocated to user equipment k, β k,n=0 means that channel n is not allocated to user equipment k; D k Indicates the data size of the computing task; C k Indicates the number of CPU cycles required to complete a 1-bit computation task; F k Indicates the local calculation frequency for completing 1-bit calculation tasks;

[0023] represents the offloading rate of user equipment k on channel n, p k is the transmission power of user equipment k; h n,k is the channel gain from user k to the base station on channel n;

[0024] N0 is the channel noise power spectral density; B is the channel bandwidth.

[0025] Preferably, step 2 specifically includes:

[0026] Step 2-1: Order

[0027] Step 2-2: Create problem P2:

[0028] P2:

[0029] Constraint C2:

[0030] Constraint C3:

[0031] Constraint C4:

[0032] Constraint C5:0≤α k ≤1,k=1,....,K,

[0033] Constraint C6:β k,n ∈{0,1},k=1,...K,n=1,...N;

[0034] Step 2-3: Using constraints C3, C4, and C6, transform constraint C2 into the following constraint:

[0035] Constraint C7:

[0036] Let the execution time of each user device k to complete the computing task locally be the deadline, and we get T k The deadline for user device k to complete a computing task, k∈[1,K];

[0037] Step 2-4: Optimize {β k,n} is transformed into a bipartite graph matching problem for optimizing channel allocation:

[0038] Step 2-5: Find the maximum matching value of the bipartite graph through the Hungarian algorithm.

[0039] Preferably, steps 2-4 are implemented by:

[0040] The collection of user devices Each user device in is regarded as a set of vertices, and the set of channels Each channel of is considered as another set of vertices;

[0041] When the inequality When it is established, there is an edge between the vertex of user device k and the vertex of channel n, indicating that the user device is connected to the MEC server.

[0042] Preferably, step three is achieved by:

[0043] Step 3-1: If user device k accesses the MEC server at the maximum configuration value, channel n is allocated to the user device k, that is, the channel allocation coefficient β k,n =1;

[0044] If user device k is not connected to the MEC server at the maximum matching value, let β k,n =0;

[0045] Step 3-2: Calculation If the value of If the value of is greater than F, remove the user set The user device k with the largest uploaded data volume:

[0046] make in Represents a user set The user device with the largest upload data volume, When α k D k C k The value of k when it is the maximum value;

[0047] Step 3-3, repeat step 3-2 until The value of is less than or equal to F;

[0048] Step 3-4: If all β k,n If the values ​​are all zero, the user device k is removed from the set. Get new access user set

[0049] Set the new access user As a user access control decision.

[0050] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above methods when executing the program.

[0051] A computer-readable storage medium stores a computer program, which implements the steps of any of the above methods when executed by a processor.

[0052] The beneficial effects achieved by the present invention are:

[0053] (1) The user access control method based on bipartite graph matching in the mobile edge computing network proposed in the present invention proposes a new method for maximizing the utilization of MEC computing resources. Under the condition of completing the computing task before the deadline, the user equipment accessing the MEC server, the amount of data uploaded by the user equipment and the allocated channels are optimized to maximize the number of user equipment that can complete the computing task;

[0054] (2) The user access control method based on bipartite graph matching in the present invention, step 2: transforming the optimization problem model into a bipartite graph matching problem for optimizing channel allocation, which aims to minimize the required MEC server computing power and verify whether a given set of user devices is accessible;

[0055] The Hungarian algorithm is used to solve the bipartite graph matching problem of optimizing channel allocation, obtain the maximum matching value, and verify whether each user device is accessible and the channel that can be allocated to it;

[0056] Step 3: Based on the maximum matching value, a user access control decision is obtained to maximize the number of user devices that can complete the computing task before the deadline.

[0057] Compared with traditional methods and local computing methods, this method achieves a higher number of users who complete the computing tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a model diagram of the mobile edge computing system;

[0059] Figure 2 is a flow chart of the present invention;

[0060] Figure 3 It is a description of the C in the method proposed by the present invention, the traditional method and the local calculation method k A graph showing the impact on the number of users required to meet the computing task;

[0061] Figure 4 It is a diagram of the method proposed by the present invention, the traditional method and the local calculation method. k=The influence of T on the number of users required to meet the computing task;

[0062] Figure 5 It is a curve diagram that depicts the influence of the maximum computing capacity F of MEC on the number of users required to meet the computing task in the method proposed in the present invention, the traditional method and the local computing method. DETAILED DESCRIPTION

[0063] The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and are not intended to limit the protection scope of the present invention.

[0064] Embodiment 1

[0065] This embodiment provides a user access control method based on bipartite graph matching in a mobile edge computing network, including:

[0066] The adopted mobile edge computing network is configured with one base station and K user devices;

[0067] The base station is configured with a MEC server with a maximum computing capacity of F CPU cycles. Under the condition of completing the computing task before the deadline, the user equipment accessing the MEC server, the amount of data uploaded by the user equipment and the allocated channel are optimized to maximize the number of user equipment that can complete the computing task.

[0068] Furthermore, in this embodiment, under the condition that the computing task is completed before the deadline, the user equipment connected to the MEC server, the amount of data uploaded by the user equipment and the allocated channel are optimized, which is achieved by the following steps:

[0069] Step 1: Establish a user access control optimization problem model;

[0070] Step 2: Convert the optimization problem model into a bipartite graph matching problem for optimizing channel allocation;

[0071] Use the Hungarian algorithm to solve the bipartite graph matching problem for optimizing channel allocation and obtain the maximum matching value;

[0072] Step 3: Get the user access control decision based on the maximum matching value.

[0073] Furthermore, step one in this embodiment specifically includes:

[0074] Establish user access control optimization problem model P1:

[0075] P1:

[0076] Constraint C1:

[0077] Constraint C2:

[0078] Constraint C3:

[0079] Constraint C4:

[0080] Constraint C5:0≤α k ≤1,k=1,....,K,

[0081] Constraint C6:β k,n ∈{0,1},k=1,...K,n=1,...N;

[0082] in, D is the set of connected user devices that can complete the computing task before the deadline. k is the data size of the calculation task, C k The number of CPU cycles required to complete a 1-bit computation task, F k is the local computing frequency for completing a 1-bit computing task; F represents the maximum computing capacity of the MEC server; the base station has N orthogonal channel resources with a bandwidth of B, N ≥ K;

[0083] is the set of channels, α k Calculate the offloading ratio of tasks for user device k; β k,n is the allocation factor between user equipment k and channel n, β k,n =1 means channel n is allocated to user equipment k, β k,n =0 means that channel n is not allocated to user equipment k; D k Indicates the data size of the computing task; C k Indicates the number of CPU cycles required to complete a 1-bit computation task; F k Indicates the local calculation frequency for completing 1-bit calculation tasks;

[0084] represents the offloading rate of user equipment k on channel n, p k is the transmission power of user equipment k; h n,k is the channel gain from user k to the base station on channel n;

[0085] N0 is the channel noise power spectral density; B is the channel bandwidth;

[0086] Constraint C1 indicates that the computing power required for the computing tasks assigned to the MEC server for processing is not greater than the maximum computing power of the MEC server;

[0087] Constraint C2 Indicates the time it takes for the user device to complete the computing task locally. It represents the time taken by user device k to upload part of the computing task to the MEC server using channel n. represents the time it takes for user device k to upload part of the computing task to the MEC server. C2 indicates that the time it takes for each user device to complete the local computing task and offload the computing task must be less than the deadline T. k ;

[0088] C3 and C4 indicate that each user device can only be assigned one channel, and each channel can only be assigned to one user device; C5 indicates that the computing task offloading ratio of the user device is between the interval [0,1].

[0089] Furthermore, step 2 in this embodiment specifically includes:

[0090] Step 2-1: Order

[0091] Step 2-2: Create problem P2:

[0092] P2:

[0093] Constraint C2:

[0094] Constraint C3:

[0095] Constraint C4:

[0096] Constraint C5:0≤α k ≤1,k=1,....,K,

[0097] Constraint C6:β k,n ∈{0,1},k=1,...K,n=1,...N;

[0098] Step 2-3: Using constraints C3, C4, and C6, transform constraint C2 into the following constraint:

[0099] Constraint C7:

[0100] Let the execution time of each user device k to complete the computing task locally be the deadline, and we get T k The deadline for user device k to complete a computing task, k∈[1,K];

[0101] Step 2-4: Optimize {β k,n} is transformed into a bipartite graph matching problem for optimizing channel allocation:

[0102] Step 2-5: Find the maximum matching value of the bipartite graph through the Hungarian algorithm.

[0103] Furthermore, steps 2-4 in this embodiment are implemented by the following steps:

[0104] The collection of user devices Each user device in is regarded as a set of vertices, and the set of channels Each channel of is considered as another set of vertices;

[0105] When the inequality When it is established, there is an edge between the vertex of user device k and the vertex of channel n, indicating that the user device is connected to the MEC server.

[0106] Furthermore, step three in this embodiment is implemented by the following steps:

[0107] Step 3-1: If user device k accesses the MEC server at the maximum configuration value, channel n is allocated to the user device k, that is, the channel allocation coefficient β k,n =1;

[0108] If user device k is not connected to the MEC server at the maximum matching value, let β k,n =0;

[0109] Step 3-2: Calculation If the value of If the value of is greater than F, remove the user set The user device k with the largest uploaded data volume:

[0110] make in Represents a user set The user device with the largest upload data volume, When α k D k C k The value of k when it is the maximum value;

[0111] Step 3-3, repeat step 3-2 until The value of is less than or equal to F;

[0112] Step 3-4: If all β k,n If the values ​​are all zero, the user device k is removed from the set. Get new access user set

[0113] Set the new access user As a user access control decision.

[0114] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above methods when executing the program.

[0115] A computer-readable storage medium stores a computer program, which implements the steps of any of the above methods when executed by a processor.

[0116] There are many models of the above components of the base station, user equipment and MEC server that can be used in the prior art. Those skilled in the art can select appropriate models according to actual needs, and this embodiment will not give examples one by one.

[0117] Embodiment 2

[0118] The algorithm of the present invention is simulated, and the method proposed by the present invention is compared with the traditional method and the local calculation method to obtain the superiority of the method proposed by the present invention. The specific steps are as follows:

[0119] Assume that all nodes in the network are located on a two-dimensional plane, and assume that 20 UEs are randomly distributed around the MEC server in a ring with an inner radius of 100m and an outer radius of 200m. There are 20 channels in total, each with a 1MHz bandwidth and a noise power spectral density of -170dBm / Hz. The channel model is modeled as 10 -4 d 2.5 e, where d is the distance and e is an exponentially distributed random variable with unit mean. Assume that the parameter C k , D k and T k Uniformly distributed in [200,400]×10 6 CPU cycles / Mbit, [0.5, 2]Mbit and [1, 2]s. Parameter F k is from {1,…,9}×10 8 In addition, F=10 is set 10 The number of CPU cycles, p k =26dBm. The proposed method is compared with two methods. One is the traditional access control algorithm, which sequentially selects users with the largest channel power gain to access in order to meet the time limit requirements until the required MEC computing resources exceed the MEC computing capacity. The other is a local computing method, that is, the user's task is only calculated locally.

[0120] Figure 3 Depicts C k The impact on the number of user devices required to meet the computing task. The results show that with the increase of C kAs the number of users who can meet the computing task requirements increases, the number of users who can meet the computing task requirements decreases. This is because the higher C k This increases the time it takes to complete the computing task, making it difficult to complete the task within the time limit. It also shows that the method proposed in this invention can meet the needs of more users with computing tasks, especially C k When the value is very large.

[0121] Figure 4 Depicts T k = The impact of T on the number of user devices required to meet the computing task. When T increases, the number of users who meet the computing task requirements also increases. This is because a larger T can make the time limit of the task easier to meet. It shows that the method proposed in the present invention can meet the number of users required by more computing tasks compared to traditional methods and local computing methods, especially when T is large.

[0122] Figure 5 The effect of the maximum computing power F of MEC on the number of user devices required to meet the computing task is depicted. When F increases, the number of users required to meet the computing task also increases. This is because a larger F allows more tasks to be offloaded to the MEC server. It shows that the method proposed by the algorithm of the present invention can meet the number of users required by more computing tasks compared to traditional methods and local computing methods, especially when the F value is large. When the F value is small, the number of users who meet the computing task requirements in all methods is almost the same, because when the F value is small, no tasks can be offloaded to the MEC server.

[0123] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0126] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A user access control method based on bipartite graph matching in a mobile edge computing network, characterized in that: include: The adopted mobile edge computing network is configured with one base station and K user devices; The base station is configured with a MEC server with a maximum computing capacity of F CPU cycles. Under the condition that the computing task is completed before the deadline, the user devices connected to the MEC server, the amount of data uploaded by the user devices, and the allocated channels are optimized to maximize the number of user devices that can complete the computing task; Among them, under the condition of completing the computing task before the deadline, the user equipment connected to the MEC server, the amount of data uploaded by the user equipment and the allocated channel are optimized, which is achieved through the following steps: Step 1: Establish a user access control optimization problem model; Step 2: Convert the optimization problem model into a bipartite graph matching problem for optimizing channel allocation; Use the Hungarian algorithm to solve the bipartite graph matching problem for optimizing channel allocation and obtain the maximum matching value; Step 3: Obtain user access control decision based on the maximum matching value; Among them, step one specifically includes: Establish user access control optimization problem model P1: Constraint C1: Constraint C2: Constraint C3: Constraint C4: Constraint C5:0≤α k ≤1,k=1,…,K, Constraint C6:β k,n ∈{0,1},k=1,…K,n=1,…N; in, D is the set of connected user devices that can complete the computing task before the deadline. k is the data size of the calculation task, C k The number of CPU cycles required to complete a 1-bit computation task, F k is the local computing frequency for completing a 1-bit computing task; F represents the maximum computing capacity of the MEC server; the base station has N orthogonal channel resources with a bandwidth of B, N ≥ K; is the set of channels, α k Calculate the offloading ratio of tasks for user device k; β k,n is the allocation factor between user equipment k and channel n, β k,N =1 means channel n is allocated to user equipment k, β k,n =0 means that channel n is not allocated to user equipment k; represents the offloading rate of user equipment k on channel n, p k is the transmission power of user equipment k; h n,k is the channel gain from user k to the base station on channel n; N0 is the channel noise power spectral density; B is the channel bandwidth; Among them, step two specifically includes: Step 2-1: Order Step 2-2: Create problem P2: Constraint C2: Constraint C3: Constraint C4: Constraint C5:0≤α k ≤1,k=1,…,K, Constraint C6:β k,n ∈{0,1},k=1,…K,n=1,…N; Step 2-3: Using constraints C3, C4, and C6, transform constraint C2 into the following constraint: Constraint C7: Let the execution time of each user device k to complete the computing task locally be the deadline, and we get T k The deadline for user device k to complete a computing task, k = 1, ..., K; Step 2-4: Optimize {β k,n } is transformed into a bipartite graph matching problem for optimizing channel allocation, which is achieved by the following steps: The collection of user devices Each user device in is regarded as a set of vertices, and the set of channels Each channel of is considered as another set of vertices; When the inequality When established, there is an edge between the vertex of user device k and the vertex of channel n, indicating that the user device is connected to the MEC server; Step 2-5: Find the maximum matching value of the bipartite graph through the Hungarian algorithm; Among them, step three is implemented by the following steps: Step 3-1: If user device k accesses the MEC server at the maximum matching value, channel n is allocated to the user device k, that is, the channel allocation coefficient β k,n =1; If user device k is not connected to the MEC server at the maximum matching value, let β k,n =0; Step 3-2: Calculation If the value of If the value of is greater than F, remove the user set The user device k with the largest uploaded data volume: make in Represents a user set The user device with the largest upload data volume, When α k D k C k The value of k when it is the maximum value; Step 3-3, repeat step 3-2 until The value of is less than or equal to F; Step 3-4: If all β k,n If the values ​​are all zero, the user device k is removed from the set. Get new access user set Set the new access user As a user access control decision.

2. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method according to claim 1 are implemented.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

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