Scheduling optimization method and system for vehicular edge computing

Abstract

The present invention relates to the technical field of mobile edge computing. Disclosed are a scheduling optimization method and system for vehicular edge computing. The method comprises: acquiring a region set and a vehicular edge server set, and establishing a network model; quantifying the service requirement completion situation of each region on the basis of a usage satisfaction degree of the region, and establishing a satisfaction degree model; formalizing a satisfaction degree maximization problem under a given traveling time constraint; converting the satisfaction degree maximization problem into a matching problem, and proving that the matching problem is a non-negative monotone submodular function maximization problem constrained by a partition matroid; and determining a scheduling scheme for vehicular edge servers by means of a scheduling optimization algorithm. In the present invention, the scheduling scheme is planned by taking the satisfaction degree as an objective, the dynamic nature and diversity of requirements of regions can both be taken into consideration, a region satisfaction problem is formalized, and under the limitation of a traveling time, the satisfaction degrees of all the regions are maximized.

Description

A vehicle-mounted edge computing scheduling optimization method and system Technical Field

[0001] The present invention relates to the field of mobile edge computing technology, and in particular to a vehicle-mounted edge computing scheduling optimization method and system. Background Art

[0002] Mobile edge computing (MEC) is a technology that can be deployed at the edge of the network to provide computing, storage, and communication services. It integrates wireless network and internet technologies, allowing some network services and functions to be off-chain from the core network. This allows for cost savings, latency reduction, traffic optimization, enhanced physical security, and improved caching efficiency. MEC has attracted widespread attention from both academia and industry. Several industry associations and standards organizations, including the 5G Automotive Association (5GAA) and the European Telecommunications Standards Institute (ETSI), have identified numerous use cases for MEC, such as augmented reality (AR) and autonomous driving.

[0003] Mobile edge computing focuses on computing task offloading, edge service caching, and edge system deployment. Currently, much of this research focuses on offloading tasks from existing fixed edge servers on the ground or leveraging the idle computing resources of public vehicles. However, in practice, due to user mobility, dynamic resource demands, and regional variations, the load on edge servers is highly dynamic and uneven. This results in a lack of flexibility in traditional mobile edge computing systems. Furthermore, public vehicles face issues such as computing resource uncertainty and difficulty in scheduling, making the offloading services provided by public vehicles only suitable for highway scenarios. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an on-vehicle edge computing scheduling optimization method and system to solve the problems of resource waste and shortage caused by the dynamic and diversity of regional demands in existing mobile edge computing scenarios.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a vehicle-mounted edge computing scheduling optimization method, comprising:

[0008] Obtain a set of regional and vehicle-mounted edge servers and build a network model;

[0009] Quantify the completion of service needs based on regional usage satisfaction and establish a satisfaction model;

[0010] Formalize the problem of maximizing satisfaction under given travel time constraints;

[0011] The problem of maximizing satisfaction is transformed into a matching problem, and it is proved that the matching problem is a non-negative monotone submodular function maximization problem constrained by partition matroids.

[0012] Determine the scheduling plan of the vehicle-mounted edge server through the scheduling optimization algorithm.

[0013] As a preferred solution of the vehicle-mounted edge computing scheduling optimization method described in the present invention, wherein: the establishment of the satisfaction model includes the following steps:

[0014] Discretize time; divide time T into different time slots T = {t1, t2, ..., t h}, where h is the number of time slots; the time slot size is determined by the number of regions, the number of onboard edge servers, and the number of resource requirements;

[0015] The time slot size is determined based on resource requirements and regional characteristics. At the beginning of each time slot, scheduling is performed based on the resources and service capabilities of the on-board edge server, and satisfaction is established by considering the shortest path length and time to the area, as well as the completion of the service requirements in the area.

[0016] As a preferred solution of the vehicle-mounted edge computing scheduling optimization method described in the present invention, wherein:

[0017] Completion of service requirements in the area, including t a Service satisfaction of all resources in region i under time slot Expressed as:

[0018] t a Satisfaction of region i with the lth resource in the time slot Expressed as:

[0019] in, Indicates that region i is in t a The time slot's demand for the lth resource, represents the lth resource provided by the vehicle-mounted edge server j, Indicates that the vehicle-mounted edge server j is at t a The arrival time of region i in the time slot, Indicates t a The lth resource provided by the vehicle-mounted edge server to region i in the time slot is When , it means that the demand for the lth resource is fully met, and the satisfaction with the lth resource is 1; when When , it means that the demand for the lth resource is not fully met, and the satisfaction with the lth resource is Decision variables The value is 0 or 1. Indicates that in time slot t a Vehicle-mounted edge server j serves area i, when Indicates time slot t a The vehicle-mounted edge server j does not serve area i.

[0020] As a preferred solution of the vehicle-mounted edge computing scheduling optimization method described in the present invention, wherein:

[0021] The problem of maximizing satisfaction under given travel time constraints is formalized as follows:

[0022] Among them, formula (3) represents maximizing the sum of the satisfaction of all areas in all time slots; formula (3-1) indicates that the vehicle-mounted edge server j must reach the target area within a time slot size τ, and formula (3-2) indicates whether the vehicle-mounted edge server j serves area i. Indicates that in time slot t a Vehicle-mounted edge server j serves area i, Indicates time slot t a The vehicle-mounted edge server j does not serve area i, Indicates that the vehicle-mounted edge server j is at t a The shortest path length to region i within the time slot, the vehicle-mounted edge server must reach region i within τ, that is Formula (3-3) indicates that the vehicle-mounted edge server j is in a time slot t a Serves at most one target area.

[0023] As a preferred solution of the vehicle-mounted edge computing scheduling optimization method described in the present invention, wherein:

[0024] The problem of maximizing satisfaction is transformed into a matching problem, including constructing a bipartite graph M and a possible matching set of the bipartite graph M that satisfies all travel time constraints through a set of regions V = {1, 2, ..., n} and a set of vehicle-mounted edge servers S = {1, 2, ..., m}. Select a valid matching subset Maximizing the regional satisfaction problem while satisfying the constraints of travel time and the number of service areas of the vehicle-mounted edge server in a single time slot;

[0025] The problem of maximizing regional satisfaction while satisfying the constraints of travel time and the number of service areas of the vehicle-mounted edge server in a single time slot is expressed as:

[0026] Wherein, formula (1) is reformulated as:

[0027] Formula (2) is reformulated as:

[0028] As a preferred solution of the vehicle-mounted edge computing scheduling optimization method described in the present invention, wherein:

[0029] The proof matching problem is a non-negative monotonic submodular function maximization problem constrained by a partition matroid, including:

[0030] Prove the set function is a non-negative monotonic submodular function;

[0031] Prove that for any is a non-negative monotonic submodular function;

[0032] Prove that for the valid subset χ a 来说, is a partition matroid;

[0033] That is, the target problem is reformulated as a region satisfaction maximization problem constrained by a partition matroid, expressed as:

[0034] Where, Q i (χ a ) represents the sum of all resource satisfactions, E′ j represents the matching set associated with the vehicle-mounted edge server j; χ a represents a feasible schedule, and (i, j) represents the matching between the vehicle-mounted edge server j and the region i.

[0035] As a preferred solution of the vehicle-mounted edge computing scheduling optimization method described in the present invention, wherein:

[0036] The scheduling optimization algorithm includes the following steps:

[0037] Input the time slot set T, G=(V, E), the vehicle-mounted edge server set S, and the capabilities of the vehicle-mounted edge servers

[0038] Initialize the current time slot t a ,计算所有可行的匹配集合 为剩余可行匹配集合,令 等于 并初始化当前时隙下的有效匹配集合χ a 为空集;

[0039] 若存在车载边缘服务器未被匹配|χ a |<m,则选出具有最大边际满意度的匹配

[0040] If there is no onboard edge server and it is not matched, then exit the current time slot;

[0041] If the maximum marginal satisfaction is greater than zero, the current match (i, j) is added to the valid match set χ a , update the remaining feasible matching set The match associated with the currently matched vehicle edge server is removed from Delete, return to determine whether there is an in-vehicle edge server that is not matched;

[0042] If the maximum marginal satisfaction is less than or equal to zero Q i (χ a ∪{(i,j)})-Q i (χ a )≤0, then exit the current time slot;

[0043] Determine whether the time slot set T has been traversed;

[0044] If the time slot set has not been traversed, return to the initialization operation and repeat the following steps until all time slots have been traversed;

[0045] If the time slot set is traversed, the process ends.

[0046] In a second aspect, the present invention provides a vehicle-mounted edge computing scheduling optimization system, comprising:

[0047] Acquisition module, obtains the regional and vehicle-mounted edge server sets, and builds a network model;

[0048] Establish a module to quantify the completion of service needs based on regional user satisfaction and establish a satisfaction model;

[0049] The formalization module formalizes the problem of maximizing satisfaction under given travel time constraints;

[0050] The transformation module transforms the satisfaction maximization problem into a matching problem, and proves that the matching problem is a non-negative monotone submodular function maximization problem constrained by the partitioned matroid;

[0051] The optimization module determines the scheduling plan of the vehicle-mounted edge server through the scheduling optimization algorithm.

[0052] In a third aspect, the present invention provides a computing device, comprising:

[0053] Memory, used to store programs;

[0054] A processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the vehicle-mounted edge computing scheduling optimization method.

[0055] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the vehicle-mounted edge computing scheduling optimization method are implemented.

[0056] Beneficial effects of the present invention: The present invention plans a scheduling scheme with satisfaction as the purpose, can simultaneously consider the dynamics and diversity of regional demand, formalize the regional satisfaction problem, and maximize the satisfaction of all regions under the constraint of driving time. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0058] FIG1 is a schematic diagram of the basic flow of a vehicle-mounted edge computing scheduling optimization method provided by one embodiment of the present invention;

[0059] FIG2 is a schematic diagram of a network model of a vehicle-mounted edge computing scheduling optimization method provided by one embodiment of the present invention;

[0060] FIG3 is a flowchart of a scheduling optimization algorithm of a vehicle-mounted edge computing scheduling optimization method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0064] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0065] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0066] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0067] Example 1

[0068] 1-3 , an embodiment of the present invention provides a method for optimizing vehicle-mounted edge computing scheduling, as shown in FIG1 , including the following steps:

[0069] S1: Obtain the set of regional and vehicle-mounted edge servers and build a network model;

[0070] Furthermore, let V = {1, 2, ..., n} represent the set of regions on the two-dimensional plane Ω, and S = {1, 2, ..., m} represent the set of vehicle-mounted edge servers. The network diagram is shown in Figure 2.

[0071] S2: Quantify the completion of service needs based on regional usage satisfaction and establish a satisfaction model;

[0072] Furthermore, S2 includes the following steps:

[0073] S2.1: Because the number of users in different regions is different, different regions have different resource requirements. Resource requirements include requirements for multiple resources, such as CPU resources, task size, etc. The resource requirement set is represented by the set K = {1, 2, ..., k}. Region i at t a The resource requirement for a time slot is in Indicates that region i is in t a The demand for the lth resource in the time slot.

[0074] S2.2: To better quantify the dynamic changes in resource demand, consider discretizing time; for the dynamic changes in resource demand, discretize time; divide time T into different time slots T = {t1, t2, ..., t h}, where h is the number of time slots; the time slot size is determined by factors such as the number of regions, the number of vehicle-mounted edge servers, and the number of resource requirements;

[0075] S2.3: Schedule the vehicle-mounted edge servers at the beginning of each time slot. The vehicle-mounted edge server j is scheduled at t a The shortest path length to area i within a time slot is recorded as The vehicle-mounted edge server must reach region i within τ, i.e. Vehicle-mounted edge server a The time to reach area i within the time slot is recorded as Regional user satisfaction quantifies the completion of its service needs, Indicates t a The service satisfaction of all resources in area i under time slot is:

[0076] in, Indicates t a Satisfaction of region i with the lth resource in the time slot:

[0077] Region i in t a The resource requirement for a time slot is in Indicates that region i is in t a The time slot's demand for the lth resource, represents the lth resource provided by the vehicle-mounted edge server j, Indicates t a The lth resource provided by the vehicle-mounted edge server to region i in the time slot is When , it means that the demand for the lth resource is fully met, and the satisfaction with the lth resource is 1; when When , it means that the demand for the lth resource is not fully met, and the satisfaction with the lth resource is Among them, the decision variables The value is 0 or 1. Indicates that in time slot t a Vehicle-mounted edge server j serves area i, Indicates time slot t a The vehicle-mounted edge server j does not serve area i.

[0078] S3: Formalize the problem of maximizing satisfaction under given travel time constraints;

[0079] Furthermore, S3 includes the following steps:

[0080] Combined with the satisfaction model, the regional satisfaction maximization problem under given travel time constraints is formalized, namely:

[0081] Formula (3) represents maximizing the sum of the satisfaction of all regions in all time slots. Formula (3-1) indicates that the vehicle-mounted edge server j must reach the target area within a time slot size τ. Formula (3-2) indicates whether the vehicle-mounted edge server j serves area i. Indicates that in time slot t a Vehicle-mounted edge server j serves area i, Indicates time slot t a The vehicle-mounted edge server j does not serve area i. Formula (3-3) indicates that the vehicle-mounted edge server j is in a time slot t a Serves at most one target area.

[0082] S4: Convert the satisfaction maximization problem into a matching problem;

[0083] Furthermore, S4 includes the following steps:

[0084] The region set and the vehicle edge server set can be constructed as a bipartite graph M with a vertex set. The region set V = {1, 2, ..., n} and the vehicle edge server set S = {1, 2, ..., m} constitute the vertex set. is the set of possible matchings of the bipartite graph M that satisfies all travel time constraints;

[0085] Indicates that in time slot t a In the case of All possible vehicle-mounted edge server matching sets, (i, j) represents the matching between vehicle-mounted edge server j and region i; let χ a yes If χ a If any two elements in S have no common vertex, then χ a is the time slot t a The valid matching subset of ;

[0086] Therefore, the original problem can be expressed as selecting a valid matching subset The problem of maximizing regional satisfaction while satisfying the constraints of travel time and the number of service areas of the vehicle-mounted edge server in a single time slot is:

[0087] Wherein, formula (1) is reformulated as:

[0088] Formula (2) can be restated as:

[0089] S5: Prove that the matching problem is a non-negative monotone submodular function maximization problem constrained by the partition matroid:

[0090] Furthermore, S5 includes the following steps:

[0091] First we need to prove that the set function is non-negative monotone submodule;

[0092] Case 1: According to formula (6), when the lth resource in region i is not fully satisfied, we have:

[0093] because is non-negative and monotonically increasing, so is a non-negative and monotonically increasing function; in order to prove The submodularity of and The following formula holds:

[0094] Let the decision variable corresponding to the matching e be the decision variable It is only necessary to prove that:

[0095] because The above formula is obviously true, so the set function is non-negative monotone submodule;

[0096] Case 2: When the lth resource in region i is fully satisfied, At this time, the aggregate function It is also non-negative monotone submodule;

[0097] Second, we need to prove that for any is a non-negative monotone submodular function; if a function is composed of the sum of several independent non-negative monotone submodular functions, then the function is also non-negative monotone submodular; according to formula (5), the set function Q i (χ a ) is the sum of all resource satisfaction, so Q i (χ a ) is a nonnegative monotone submodule; similarly, the set function It is also non-negative monotone submodule;

[0098] Then, we need to prove that for the valid subset χa For example, is a partition matroid; a bipartite graph Can form a pseudo-matrix; the edge set Divide into m disjoint subsets, Among them E j ′ is the matching set associated with the vehicle-mounted edge server j; if a is a valid matching subset, then |χ a ∩E j ′|≤1, because a single vehicle-mounted edge server can only serve one regional point in a time slot; therefore, the set of independent sets therefore, is a partitioning matroid;

[0099] In summary, the target problem can be reformulated as a regional satisfaction maximization problem subject to partition matroid constraints:

[0100] Formula (10) represents the maximum sum of satisfaction of all regions in all time slots, and formula (10-1) represents the set χ a is a valid matching subset, and formula (10-2) represents the set χ a yes A subset of , formula (10-3) represents the set is the set E j ′, E j ′ is the matching set associated with the vehicle-mounted edge server j, and formula (10-4) represents is the set of possible matches that satisfy all travel time constraints.

[0101] S6: Determine the scheduling plan for the vehicle edge server through the scheduling optimization algorithm.

[0102] Furthermore, as shown in FIG3 , S6 includes the following steps:

[0103] S61: Input time slot set T, G = (V, E), vehicle edge server set S, and vehicle edge server capabilities

[0104] S62: Initialize current time slot t a , calculate all feasible matching sets Let be the remaining feasible matching set, equal And initialize the valid matching set χ under the current time slot a is an empty set;

[0105] S63: If there is an in-vehicle edge server that is not matched |a If m is less than the maximum marginal satisfaction, select the match with the maximum marginal satisfaction

[0106] If there is no vehicle-mounted edge server that has not been matched, exit the current time slot;

[0107] S64: If the maximum marginal satisfaction is greater than zero, add the current match (i, j) to the set of valid matches χ a , and update the remaining set of feasible matches Delete the matches associated with the vehicle-mounted edge servers that have already obtained matches from , and return to determine whether there is a vehicle-mounted edge server that has not been matched;

[0108] If the maximum marginal satisfaction is less than or equal to zero Q i (χ a ∪{(i, j)}) - Q i (χ a ) ≤ 0, then exit the current time slot;

[0109] S65: Determine whether the set of time slots T has been traversed;

[0110] If the set of time slots has not been traversed, return to the initialization operation and repeat the following steps until all time slots have been traversed;

[0111] If the set of time slots has been traversed, the process ends.

[0112] It should be noted that in the mobile edge computing scenario, this invention considers the dynamics and diversity of regional requirements; proposes the problem of maximizing the satisfaction of all regions, and designs a scheduling scheme for vehicle-mounted edge servers for this problem.

[0113] The scheduling optimization algorithm described in step (6) can obtain an approximation ratio for the problem of maximizing the satisfaction of all regions as

[0114] As can be seen from step (5), the problem of maximizing regional satisfaction is a problem of maximizing a non-negative monotone submodular function with matroid constraints. The scheduling optimization algorithm described in step (6) is a classical greedy algorithm. And the approximation ratio of the classical greedy algorithm for the problem of maximizing a monotone submodular function with matroid constraints is Therefore, the approximation ratio of the problem of maximizing the satisfaction of all regions can be obtained.

[0115] This embodiment also provides a vehicle-mounted edge computing scheduling optimization system, including:

[0116] An acquisition module, which acquires the set of regions and vehicle-mounted edge servers and establishes a network model;

[0117] Establish a module to quantify the completion of service needs based on regional user satisfaction and establish a satisfaction model;

[0118] The formalization module formalizes the problem of maximizing satisfaction under given travel time constraints;

[0119] The transformation module transforms the satisfaction maximization problem into a matching problem, and proves that the matching problem is a non-negative monotone submodular function maximization problem constrained by the partitioned matroid;

[0120] The optimization module determines the scheduling plan of the vehicle-mounted edge server through the scheduling optimization algorithm.

[0121] Furthermore, it also includes:

[0122] Memory, used to store programs;

[0123] A processor is used to load the program to execute the vehicle-mounted edge computing scheduling optimization method.

[0124] This embodiment also provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the vehicle-mounted edge computing scheduling optimization method.

[0125] The storage medium proposed in this embodiment and the vehicle-mounted edge computing scheduling optimization method proposed in the above embodiment belong to the same inventive concept. For technical details not described in detail in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0126] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0127] Example 2

[0128] Referring to Table 1, an embodiment of the present invention provides a vehicle-mounted edge computing scheduling optimization method. In order to verify its beneficial effects, scientific demonstration is carried out through specific implementation methods and implementation effects.

[0129] The specific implementation of this embodiment is as follows:

[0130] Taking a certain mobile edge computing scenario as an example, the vehicle-mounted edge server scheduling method in this embodiment includes the following steps:

[0131] On the two-dimensional plane Ω, the set of regions is represented by V = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, the set of vehicle-mounted edge servers is represented by S = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, the parameters of the vehicle-mounted edge servers are shown in Table 1, the service request parameters of the regions are shown in Table 2, and the distance parameters between the regions are shown in Table 3.

[0132] Table 1 Parameters of Vehicle-mounted Edge Servers

[0133] Table 2: Service Request Parameters of Regions

[0134] Table 3: Distance Parameters between Regions

[0135] The relevant parameters are set as follows: the number of time slots h = 5, the vehicle-mounted edge server cpu = [150, 400], bandwidth = [8, 25], v = [40, 60], the cpu of each task in the region = [500, 1500], the task size = [20, 100], and the number of tasks sumTask = [500, 1500].

[0136] Step 1: Input the time slot set T, G = (V, E), the set of vehicle-mounted edge servers S, and the capabilities of the vehicle-mounted edge servers

[0137] Step 2: Initialize the current time slot t a , calculate all feasible matching sets is the remaining feasible matching set, let be equal to and initialize the effective matching set χ in the current time slot a as an empty set;

[0138] Step 3: When there are vehicle-mounted edge servers that have not been matched |χ a | < m, select the matching with the maximum marginal satisfaction

[0139] Step 4: If the maximum marginal satisfaction is less than or equal to zero Q i (χ a ∪{(i, j)}) - Q i (χ a)≤0, then exit the current time slot and execute step 2; otherwise, add the current match (i, j) to the valid match set χ a , update the remaining feasible matching set The match associated with the currently matched vehicle edge server is removed from Delete in;

[0140] Step 5: Repeat steps 3 and 4 until all the vehicle-mounted edge servers are traversed;

[0141] At this time, the matchings with the maximum marginal satisfaction are (11,1), (9,2), (12,3), (10,9), (1,4), (3,5), (3,6), (15,7), (8,8), (4,10);

[0142] Step 6: Similarly, in the second time slot, the matchings with the maximum marginal satisfaction are (9,1), (8,2), (12,3), (1,4), (3,5), (4,6), (6,7), (14,8), (10,9), (13,10); in the third time slot, the matchings with the maximum marginal satisfaction are (9,1), (7,2), (1,4), (3,5), (13,6), (6,7), (14,8), (10,9), (4,10), (12,3); in the fourth time slot, the matchings with the maximum marginal satisfaction are (9,1), (7,2), (1,4), (3,5), (13,6), (6,7), (14,8), (10,9), (4,10), (12,3); In the first time slot, the matchings with the maximum marginal satisfaction are (8,1), (8,2), (12,3), (1,4), (13,6), (6,7), (14,8), (10,9), (3,10), and (4,5); in the fifth time slot, the matchings with the maximum marginal satisfaction are (8,1), (7,2), (12,3), (1,4), (4,5), (13,6), (15,7), (14,8), (10,9), and (3,10); at this time, all time slot cycles end.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A vehicle-mounted edge computing scheduling optimization method, characterized in that, Including: Obtain an area and a set of vehicle-mounted edge servers, and establish a network model; Based on the regional usage satisfaction, quantify the completion of its service requirements, and establish a satisfaction model; Formalize the problem of maximizing satisfaction under a given driving time constraint; Transform the satisfaction maximization problem into a matching problem, and prove that the matching problem is a non-negative monotonic submodular function maximization problem constrained by a partition matroid; Determine the scheduling scheme of the vehicle-mounted edge servers through a scheduling optimization algorithm.

2. The vehicle-mounted edge computing scheduling optimization method according to claim 1, wherein: The establishment of the satisfaction model includes the following steps: Discretize time; divide time T into different time slots with time slot size τ, T = {t1, t2,..., t h}, where h is the number of time slots; the time slot size is comprehensively determined by factors such as the number of regions, the number of vehicle edge servers, and the number of resource requirements; Determine the time slot size according to the resource requirements and regional characteristics; at the beginning of each time slot, perform scheduling according to the resources and service capabilities of the vehicle-mounted edge servers, and consider the shortest path length and time to the area they reach, as well as the completion of the service requirements in the area to establish satisfaction.

3. The vehicle-mounted edge computing scheduling optimization method according to claim 1 or 2, characterized in that: The completion of service demand in the said area, including t a The service satisfaction of area i in all resource service time slots is expressed as: t a Satisfaction of area i with the l-th type of resource in the time slot Expressed as: Among them, Indicates the demand of region i for the l-th type of resource in time slot t a ​ Denote the l-th type of resource provided by the vehicle-mounted edge server j. Denote the time when the vehicle-mounted edge server j arrives at area i within the t a time slot, Denote t a the l-th type of resource provided by the on-vehicle edge server for area i during the time slot, when When it indicates that the demand for the l-th resource is fully satisfied, the satisfaction degree for the l-th resource is 1 at this time; when When it indicates that the demand for the l-th resource is not fully satisfied, the satisfaction degree for the l-th resource at this time is Decision variable The value is 0 or 1. When Indicating at time slot t a The on-vehicle edge server j serves area i when Denote time slot t a Unserved area i of vehicle-mounted edge server j.

4. The vehicle-mounted edge computing scheduling optimization method according to claim 3, wherein: The problem of maximizing satisfaction under a given driving time constraint is formally expressed as: Among them, formula (3) represents maximizing the sum of satisfaction degrees of all regions in all time slots; formula (3-1) means that the vehicular edge server j must reach the target region within a time slot size τ, and formula (3-2) indicates whether the vehicular edge server j serves region i. Indicating at time slot t a The on-vehicle edge server j serves area i Indicates time slot t a The unserved area i of the vehicle-mounted edge server j Denote the shortest path length from the vehicular edge server j to area i within the time slot t. The vehicular edge server must reach area i within τ, that is a ​ Formula (3-3) indicates that the on-vehicle edge server j serves at most one target area within a time slot t a ​ 5. The vehicle-mounted edge computing scheduling optimization method according to claim 4, characterized in that: The problem of maximizing satisfaction is transformed into a matching problem, including constructing a bipartite graph \(M\) through a set of regions \(V = \{1, 2, \ldots, n\}\) and a set of vehicle-mounted edge servers \(S=\{1, 2, \ldots, m\}\), and a set of possible matchings of the bipartite graph \(M\) that satisfy all travel time constraints. Select an effective matching subset Maximize the regional satisfaction problem under the constraints of travel time and the number of service regions of vehicle-mounted edge servers within a single time slot; The problem of maximizing the area satisfaction under the constraints of travel time and the number of vehicle-mounted edge server service areas within a single time slot is expressed as: s.t. (3-1), (3-2), (3-3) (4) Among them, formula (1) is rephrased as: Formula (2) is reformulated as:

6. The vehicle-mounted edge computing scheduling optimization method according to claim 5, characterized in that: The proof that the matching problem is a non-negative monotonic submodular function maximization problem constrained by a partition matroid includes: Prove set function is a non-negative monotonic submodular function; Prove that for any is a non-negative monotonic submodular function; Prove for the valid subset χ a that is a partition matroid; That is, the target problem is reformulated as a problem of maximizing the regional satisfaction degree subject to the partition matroid constraint, which is expressed as: Among them, Q i (χ a ) represents the sum of all resource satisfaction degrees, and E′ j represents the matching set associated with the vehicle-mounted edge server j; χ a represents a feasible schedule, and (i, j) represents the matching between the vehicle-mounted edge server j and the area i.

7. The vehicle-mounted edge computing scheduling optimization method according to claim 6, wherein: The scheduling optimization algorithm includes the following steps: Input time slot set T, G=(V, E), set S of vehicle-mounted edge servers, capabilities of vehicle-mounted edge servers Initialize the current time slot t a , calculate all feasible matching sets be the remaining set of feasible matches, and let Equal to And initialize the effective matching set χ in the current time slot a as an empty set; If there is an in-vehicle edge server that is not matched|χ a | < m, then select the matching with the maximum marginal satisfaction If there is no unmatched vehicle-mounted edge server, exit the current time slot; If the maximum marginal satisfaction is greater than zero, add the current match (i, j) to the set of valid matches χ a , and update the set of remaining feasible matches Remove the match associated with the vehicle edge server that has currently obtained a match from Delete in, and return to judge whether there is an unmatched vehicle-mounted edge server; If the maximum marginal satisfaction is less than or equal to zero Q i (χ a ∪{(i,j)}) - Q i (χ a ) ≤ 0, then exit the current time slot; Judge whether the time slot set T has been traversed; If the time slot set has not been traversed, return to the initialization operation and repeat the following steps until all time slots have been traversed; If the time slot set has been traversed, the process ends.

8. An optimization system based on the vehicle-mounted edge computing scheduling optimization method according to any one of claims 1 to 7, wherein: An acquisition module, which acquires an area and a set of vehicle-mounted edge servers, and establishes a network model; An establishment module, which quantifies the completion of its service requirements based on the regional usage satisfaction, and establishes a satisfaction model; A formalization module, which formalizes the problem of maximizing satisfaction under a given driving time constraint; A transformation module, which transforms the satisfaction maximization problem into a matching problem, and proves that the matching problem is a non-negative monotonic submodular function maximization problem constrained by a partition matroid; An optimization module, which determines the scheduling scheme of the vehicle-mounted edge servers through a scheduling optimization algorithm.

9. An electronic device, characterized in that, Including: A memory for storing programs; A processor for loading the program to execute the steps of the vehicle-mounted edge computing scheduling optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, characterized in that, When the program is executed by the processor, the steps of the vehicle-mounted edge computing scheduling optimization method according to any one of claims 1 to 7 are implemented.

Images