Calculation unloading method based on task transmission control and improved greedy strategy

Abstract

The invention provides a calculation unloading method based on task transmission control and an improved greedy strategy, and relates to the technical field of calculation unloading, and the method comprises the steps: constructing a system model composed of a communication model and a task unloading model; obtaining the transmission and execution time ratio of adjacent unloading tasks, determining the data volume of the optimal edge server for executing the unloading tasks, and generating a sorting sequence; according to the sorting sequence and the data volume, the unloading task is distributed to a road side unit or a service vehicle, and an optimal edge server and a task distribution result are determined in combination with the distance between the task vehicle and the edge server; and the base station sends a resource allocation message to the near-end road side unit, then the near-end road side unit forwards the resource allocation message to the task vehicle, the task vehicle sends complete task information to the allocated road side unit or the service vehicle after delay waiting, and a result is returned after the unloading task is executed. Through a task transmission control mechanism, the communication load of the edge server is relieved, and the calculation unloading and resource allocation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource allocation and computation offloading, and in particular to a computation offloading method based on task transmission control and an improved greedy strategy. Background Art

[0002] With the development of wireless communication technology and artificial intelligence and other scientific and technological advances, in-vehicle applications are becoming more and more intelligent and diverse, involving various aspects such as road safety, entertainment services, traffic control, intensive computing services, etc. These applications also bring challenges to the limited computing resources and energy of vehicles. However, with the increase in resource requirements and latency requirements of in-vehicle applications, in-vehicle cloud computing faces new challenges because of the low coverage of cloud computing service architecture, long data transmission distance, high response delay, unstable communication quality, and excessive data transmission that will also increase the burden on the cloud core network. In order to solve the problems existing in the above cloud service technologies, mobile edge computing has become a promising technology by transferring cloud computing resources to the edge close to mobile devices.

[0003] The emergence of in-vehicle edge computing has created conditions for the realization of more in-vehicle applications, but it also faces new challenges. In the in-vehicle edge computing scenario, the vehicle is mobile, and it is necessary to ensure the stability of the communication link between the edge server and the vehicle and the efficiency of data transmission; the resources in in-vehicle edge computing are diverse, but also limited, including not only roadside units, but also other vehicles with idle resources. In order to meet the latency requirements of vehicle tasks in the edge environment, a reasonable resource allocation and computing offloading method is required, and the workload of the edge server must also be considered; during peak traffic or congested periods, the task vehicle will generate a large number of computing and communication tasks. Most solutions use vehicle-to-infrastructure (V2I) communication to achieve computing task offloading, which may cause communication overload of the edge server, and low computing offloading and resource allocation efficiency.

[0004] Therefore, it is urgent to provide a solution to improve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a computation offloading method based on task transmission control and improved greedy strategy, so as to improve the problems in the above-mentioned background technology.

[0006] The present invention provides a computation offloading method based on task transmission control and improved greedy strategy, which adopts the following technical solutions: Constructing a system model between multiple task vehicles, multiple roadside units, and multiple service vehicles, and constructing multiple optional edge servers based on the roadside units and the service vehicles, wherein the system model includes a communication model and a task offloading model; Obtaining a time ratio of transmission and execution of adjacent offload tasks, and based on the time ratio, obtaining a ratio of the amount of data executed by a single optimal edge server on the offload task, to obtain a sorted sequence; Based on the order of the sorted sequence and the amount of data, the unloading task is assigned to the roadside unit or the service vehicle, and based on the coordinates of the task vehicle, the corresponding distances between the task vehicle and the edge server when the unloading task starts and ends are obtained, and the optimal edge server corresponding to all tasks, i.e., the task assignment result, is obtained; Based on the task allocation result, the base station sends the resource allocation message back to the proximal roadside unit, and the proximal roadside unit then sends the resource allocation message to the task vehicle. After a delayed waiting time, the task vehicle sends the complete task information to the assigned roadside unit or service vehicle. When the optimal edge server completes the unloading task, it sends the unloading result back to the task vehicle.

[0007] The beneficial effect of the computation offloading method based on task transmission control and improved greedy strategy provided by the present invention is that the present invention can effectively alleviate the communication load of the edge server based on the task transmission control mechanism, and improve the computation offloading and resource allocation efficiency.

[0008] Optionally, when constructing a system model between multiple task vehicles and multiple roadside units and multiple service vehicles, the system model specifically includes: a base station, a roadside unit, a task vehicle and a service vehicle, wherein the task vehicle and the service vehicle travel in a straight line at a fixed speed during the calculation of unloading, the task vehicle is used to send a task unloading request to the roadside unit, and after transmitting the complete task information to the roadside unit, receive the result returned by the roadside unit after completing the task, the service vehicle is used to receive the complete task information sent by the task vehicle and send the task execution result to the task vehicle, the base station is used to receive the task unloading request sent by the roadside unit and return a resource allocation message to the roadside unit, and the roadside unit is used to receive the task unloading request sent by the task vehicle and return the resource allocation message to the task vehicle.

[0009] Optionally, the mathematical expression of the communication model is: ; ; in, Mission vehicle With edge servers The bandwidth of the transmission channel between Mission vehicle The transmission power, represents the channel gain, Indicates the environmental noise when the server is the receiving end. For mission vehicles With edge servers The Euclidean distance between represents the path loss component, Mission vehicle With edge servers Uplink data rate for communication between The mathematical expression of the task offloading model is: ; ; in, Mission vehicle The computational size of the task, Mission vehicle The data size of the computing task, represents the computational intensity of the task, It represents the transmission time for the task vehicle to send the unloading task to the edge server.

[0010] Optionally, the process of respectively obtaining the distances between the task vehicle and the edge server corresponding to when the unloading task starts and when the unloading task is completed based on the coordinates of the task vehicle includes: ; ; ; in, Mission vehicle Distance to edge server The distance Mission vehicle Distance to roadside unit The distance Mission vehicle Distance to service vehicle The distance , Mission vehicles Relative edge server The coordinates when sending the unloading task, , They are the task vehicles in the corresponding coordinate axis directions. The driving speed, , Represented as mission vehicles Relative roadside unit The coordinates when sending the unloading task, Mission vehicle Roadside Unit The total completion time of the uninstallation task. Mission vehicle In service vehicle The total completion time of the uninstallation task. , Mission vehicles Relative service vehicles The coordinates when sending the unloading task, , They are the service vehicles in the corresponding coordinate axis directions. speed; The process of selecting the optional edge servers based on the distance and the communication range radius to obtain the optimal edge server includes: ; ; ; in, , , Roadside Unit , Service Vehicles , Edge Server The communication range radius.

[0011] Optionally, the mathematical expression of the sum of the total task completion time of the edge server is: ; in, represents the total completion time of the task, Represents edge server The number of tasks that need to be executed, , , Represents edge servers In the order of execution, 1, 2, ..., The execution time of a task is hour, To obtain the minimum value, the edge server executes the assigned offloading tasks in the order of task execution time from small to large, and obtains the minimum value of the sum of the total task completion time.

[0012] Optionally, the process of obtaining the ratio of the data volume of a single optimal edge server to perform the offloading task includes: based on the uplink data rate of the communication and the task vehicle The data volume of the computing task is obtained by the transmission time of the task vehicle sending the unloaded task to the edge server, the task unloading time is obtained based on the optimal computing volume size and the CPU cycle frequency of the edge server, and the ratio of the task data volume is obtained based on the task transmission time and the task unloading time, where The mathematical expression of the transmission time for the task vehicle to send the unloading task to the edge server is: ; The task unloading time The mathematical expression is: ; The ratio of the task data volume The mathematical expression is: ; in, represents the transmission time for the task vehicle to send the unloading task to the edge server, Mission vehicle The data size of the computing task, Mission vehicle With edge servers The uplink data rate of communication between represents the bandwidth of the transmission channel, Mission vehicle The transmission power, represents the channel gain, represents the Gaussian white noise power, Mission vehicle The amount of data in the previous task. Represents edge server The CPU cycle frequency, represents the computational intensity of the task, Mission vehicle The amount of data for the previous task. Indicates the ratio of the current vehicle task data volume to the previous vehicle task data volume.

[0013] Optionally, the process of the optimal edge server performing the offloading task includes: When the unloading task is assigned to and When there are edge servers, the queue waiting time set after task offloading is obtained respectively and collection , remove the collection and collection Get the collection after equal elements ; Based on the collection Assign the current optimal edge server to the user to perform the unloading task, and obtain the maximum working time of all optional edge servers of the task vehicle to be minimized, where When , the improved greedy strategy is expressed as: ; ; in, Mission vehicle The waiting time of the task in the corresponding edge server, Mission vehicle The number of edge servers that can be selected, represents the total completion time of the task vehicle assigned to the edge server to perform the unloading task, Indicates Edge servers, Indicates Edge servers.

[0014] Optionally, when the mission vehicle When performing offloading tasks to the optimal edge server, the task vehicle The mathematical expression for the total completion time is: ; in, represents the roadside unit set, represents the set of service vehicles, Mission vehicle The total completion time, Mission vehicle Roadside Unit The total completion time of the uninstallation task. Mission vehicle In service vehicle The total completion time of the uninstallation task. Indicates the local total time. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart showing a method for computing offloading based on task transmission control and improved greedy strategy provided by the present invention; Figure 2 A schematic diagram showing the resource allocation and task offloading process of a computation offloading method based on task transmission control and improved greedy strategy provided by the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be the usual meanings understood by people with general skills in the field to which the present invention belongs. The words "including" and similar words used in the present invention mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0017] In some embodiments, see Figure 1 , which is a flowchart of a method for computing offloading based on task transmission control and improved greedy strategy provided by the present invention, comprising the following steps:

[0018] S1, constructing a system model between multiple task vehicles, multiple roadside units, and multiple service vehicles, and constructing multiple optional edge servers based on the roadside units and service vehicles, wherein the system model includes a communication model and a task offloading model; S2, obtaining a time ratio of transmission and execution of adjacent offload tasks, and based on the time ratio, obtaining a ratio of the amount of data executed by a single optimal edge server on the offload task, to obtain a sorting sequence; S3, assigning the unloading task to the roadside unit or service vehicle based on the order of the sorting sequence and the data volume, obtaining the corresponding distances between the task vehicle and the edge server when the unloading task starts and is completed based on the coordinates of the task vehicle, and obtaining the optimal edge server corresponding to all tasks, i.e., the task assignment result; S4. Based on the task allocation result, the base station sends the resource allocation message back to the proximal roadside unit, and the proximal roadside unit then sends the resource allocation message to the task vehicle. After the delayed waiting time, the task vehicle sends the complete task information to the assigned roadside unit or service vehicle. When the optimal edge server completes the unloading task, it sends the unloading result back to the task vehicle.

[0019] In some embodiments, when executing step S1, when constructing a system model between multiple task vehicles and multiple roadside units and multiple service vehicles, the system model specifically includes: a base station, a roadside unit, a task vehicle and a service vehicle, wherein the task vehicle and the service vehicle travel in a straight line at a fixed speed during the calculation of unloading, the task vehicle is used to send a task unloading request to the roadside unit, and after transmitting the complete task information to the roadside unit, receive the result returned by the roadside unit after completing the task, the service vehicle is used to receive the complete task information sent by the task vehicle, and send the task execution result to the task vehicle, the base station is used to receive the task unloading request sent by the roadside unit, and return a resource allocation message to the roadside unit, and the roadside unit is used to receive the task unloading request sent by the task vehicle, and return the resource allocation message to the task vehicle.

[0020] Specifically, the base station is responsible for computing offloading resource allocation, the roadside unit is deployed with an edge server, the task vehicle can generate computing tasks, and the service vehicle, as a mobile edge server, has idle computing resources and can provide services to other users. Due to the limited computing, storage performance and energy consumption of a single vehicle, the vehicle needs to offload tasks to the edge server through computing offloading to meet the needs of computing-intensive and delay-sensitive tasks. The roadside unit serves as a fixed edge server, and the service vehicle serves as a mobile edge server. Both of them serve as the server to provide task offloading services for the task vehicle. This invention considers the scenario in which a single task vehicle has at most one computing task and the computing tasks are unloaded independently. The server executes each unloaded task in sequence.

[0021] Furthermore, the mission vehicles in the model are represented as a set ,in is the number of mission vehicles, and the roadside unit is represented as a set ,in is the number of roadside units, and the service vehicles are represented as a set ,in is the number of service vehicles. The roadside units equipped with fixed edge servers and the service vehicles as mobile edge servers together constitute the edge server set , the total number of edge servers is .

[0022] See also Figure 2, the task vehicle sends a task unloading request message containing basic task information to the nearest roadside unit. The roadside unit receives the task unloading request and sends it to the base station responsible for resource allocation in the corresponding area. The base station is generally located in the central area of ​​the edge environment. The roadside unit and the vehicle are often far away from the central cloud, but the distance between the base station and the roadside unit, between the roadside unit and the vehicle, and between the task vehicle and the service vehicle is often very close, the propagation delay is very small, and the small amount of data in the message also determines that its sending delay is very short, so the delay of message transmission in the vehicle-mounted edge computing scenario is very small.

[0023] Furthermore, the base station collects the basic information of all tasks and the working status information of the roadside unit and the service vehicle, determines the execution server of each user task, and then returns the allocation message to the user's near-end roadside unit. The user's near-end roadside unit then returns the corresponding resource allocation message to the task vehicle. After receiving the message, the task vehicle sends the complete task information to the corresponding task execution server. Finally, the roadside unit or service vehicle receives the unloading task and returns the result to the task vehicle after execution.

[0024] Furthermore, when executing step S1, the mathematical expression of the communication model is: ; ; in, Mission vehicle With edge servers The bandwidth of the transmission channel between Mission vehicle The transmission power, represents the channel gain, Indicates the environmental noise when the server is the receiving end. For mission vehicles With edge servers The Euclidean distance between represents the path loss component, Mission vehicle With edge servers The uplink data rate for communication between .

[0025] The mathematical expression of the task offloading model is: ; ; in, Mission vehicle The computational size of the task, Mission vehicle The data size of the computing task, represents the computational intensity of the task, It represents the transmission time for the task vehicle to send the unloading task to the edge server.

[0026] Furthermore, during the period from when the task vehicle sends the unloading task to when the edge server returns the execution result, the task vehicle basically maintains a constant moving speed. The edge server position when the unloading task is sent is taken as the coordinate origin, and due east and due north are the positive directions of the X and Y axes, respectively. The coordinates of the task vehicle are obtained from the longitude and latitude information of the edge server and the task vehicle, and the process of obtaining the corresponding distances between the task vehicle and the edge server at the start and completion of the unloading task based on the coordinates of the task vehicle includes: ; ; ; in, Mission vehicle Distance to edge server The distance Mission vehicle Distance to roadside unit The distance Mission vehicle Distance to service vehicle The distance , Mission vehicles Relative edge server The coordinates when sending the unloading task, , They are the task vehicles in the corresponding coordinate axis directions. The driving speed, , Represented as mission vehicles Relative roadside unit The coordinates when sending the unloading task, Mission vehicle Roadside Unit The total completion time of the uninstallation task. Mission vehicle In service vehicle The total completion time of the uninstallation task. , Mission vehicles Relative service vehicles The coordinates when sending the unloading task, , They are the service vehicles in the corresponding coordinate axis directions. speed; The process of selecting the optional edge servers based on the distance and the communication range radius to obtain the optimal edge server includes: ; ; ; in, , , Roadside Unit , Service Vehicles , Edge Server The communication range radius.

[0027] For a task vehicle that meets the above conditions and its optional edge server, the base station selects one of the optional edge servers to perform the unloading task of the task vehicle according to a certain strategy.

[0028] In fact, in the on-board edge computing scenario, to allocate resources of the edge environment to the task vehicle, it is necessary to filter out the edge servers available for users to choose based on the communication range of the edge servers. First, for a certain task vehicle, the location of the task vehicle when it starts to send the unloading task must be within the communication range of the optional edge server; secondly, since the time from the task vehicle sending the task unloading request to receiving the calculation result is not long, simply considering the mobility of the vehicle, before the task vehicle receives the execution result, it cannot leave the communication range of the edge server that executes the unloading task, because this will affect the transmission of the execution result.

[0029] Further, to analyze the rationality of step S3, it is assumed that under a certain resource allocation and computation offloading method, the edge server The tasks to be performed are The corresponding task vehicles are represented in the execution order as follows: .

[0030] Furthermore, to allocate Performing these tasks is optimal for the entire edge environment with the goal of reducing the average latency of users. When there is no connection between edge servers in offloading tasks, it is more convenient for edge servers to The sum of the total completion time of all tasks on The minimum value should be taken. The mathematical expression of the sum of the total task completion time of the edge server is: ; in, represents the total completion time of the task, Represents edge server The number of tasks that need to be executed, , , Represents edge servers In the order of execution, 1, 2, ..., The execution time of a task is hour, To obtain the minimum value, the edge server executes the assigned offloading tasks in the order of task execution time from small to large, and obtains the minimum value of the sum of the total task completion time.

[0031] Further, according to , since the transmission time of the computing task is shorter than the execution time of the computing task and there is an order of magnitude difference, the edge server can be approximated The total completion time of all unloading tasks as follows: ; in, represents the total completion time of the task, Represents edge server The number of tasks that need to be executed, , , Represents edge servers In the order of execution, 1, 2, ..., The execution time of a task is hour, To obtain the minimum value, the edge server executes the assigned offloading tasks in the order of task execution time from small to large, and obtains the minimum value of the sum of the total task completion time.

[0032] In fact, the above analysis is for a single edge server. To extend the relationship between the execution time of tasks to all roadside units and service vehicles in the system, it is necessary to consider the relationship between the computational size of all tasks, which can be expressed as follows: ; in, , , Represents edge server In the order of execution, 1, 2, ..., The amount of computation for each task, represents the edge server, Represents a collection of edge servers.

[0033] From the above formula, we can see that at the beginning of the resource allocation and computation offloading algorithm, the base station should allocate all the offloading tasks of the task vehicles according to the computational size. The tasks are sorted from small to large, and the tasks with small computational complexity are assigned first. In this way, when the unloading tasks are assigned to different roadside units or service vehicles, it can be ensured that for any edge server, the computational complexity of the unloading tasks assigned is increasing.

[0034] Furthermore, based on the greedy strategy, the minimum value in the maximum value set of the current queue waiting time of the optional edge server can be obtained. The specific steps are: When the unloading task is assigned to and When there are edge servers, the queue waiting time set after task offloading is obtained respectively and collection , remove the collection and collection Get the collection after equal elements ; Based on the collection Assign the current optimal edge server to the user to perform the unloading task, and obtain the maximum working time of all optional edge servers of the task vehicle to be minimized.

[0035] Specifically, if for the mission vehicle The edge servers you can choose are , respectively , if the unloading tasks of the task vehicles are assigned to these edge servers for execution, their total completion times are Before the task is unloaded, the task vehicle The queuing waiting time of the tasks at the corresponding edge servers are: .

[0036] If under the current computation offloading method, the offloading task is assigned to the edge server , while in other possible methods, the offloading task is assigned to any Optional Edge Servers ,Right now and .

[0037] Furthermore, consider the sequence number In the case of offloading tasks to edge servers In the case of mission vehicles The queue waiting time of the optional edge server after task offloading is expressed as the set : , and the task is assigned to In the case of collection , after removing the elements with equal values ​​from the two sets, the set : and .

[0038] Specifically, if a greedy strategy is adopted, that is, the user is assigned the current optimal edge server to perform the offloading task, then: ; Furthermore, combining the relationship , we can get , , that is, it can ensure that the mission vehicle After the task is unloaded, compared with other possible resource allocation methods, under the same allocation order, the greedy strategy can make the task vehicle The maximum working time of all optional edge servers is the smallest.

[0039] In fact, when When , the improved greedy strategy is expressed as: ; ; in, Mission vehicle The waiting time of the task in the corresponding edge server, Mission vehicle The number of edge servers that can be selected, represents the total completion time of the task vehicle assigned to the edge server to perform the unloading task, Indicates Edge servers, Indicates Edge servers.

[0040] The above is the serial number In the case of Therefore, before and after a single task is unloaded, the greedy strategy can simultaneously ensure that the current user task completion delay and the maximum value of all edge server working time are minimized.

[0041] The base station sequentially calculates the task completion time under task unloading for all roadside units or service vehicles within the communication range of all users' current locations, and obtains a roadside unit or service vehicle corresponding to the minimum value, which is the optimal edge server to which the task vehicle unloading task should be assigned. This ensures that after each computing task is unloaded, the total completion time of the current task is minimized, and the maximum user experience value in terms of latency is maximized. Moreover, for all optional roadside units and service vehicles for the current task, it can ensure that after the task is unloaded, the maximum value of the current queue waiting time of all optional edge servers is minimized, that is, a certain balance in time is achieved between edge servers.

[0042] Specifically, the mathematical expression of the maximum user experience value is: ; Among them, the indicator , when the user's vehicle The computing tasks are assigned to the edge servers hour, ,otherwise , Indicates the number of roadside units, Represents the number of service vehicles.

[0043] In some embodiments, when executing step S4, the delay waiting time is the task vehicle After receiving the computation offloading and resource allocation message, the offloading task is sent to the edge server. The mathematical expression of the waiting time is: ; in, For mission vehicles After receiving the computation offloading and resource allocation message, the offloading task is sent to the edge server. The time you need to wait, Indicates the waiting time in queue. It represents the transmission time for the task vehicle to send the unloading task to the edge server.

[0044] After obtaining the allocation result of the task vehicle unloading task, the base station sends the resource allocation message back to the roadside unit closest to the task vehicle, and the user's near-end roadside unit then sends it to the corresponding task vehicle. After that, the complete task information content is sent to the assigned roadside unit or service vehicle. After the edge server completes the unloading task and obtains the result, it directly sends it back to the task vehicle.

[0045] Furthermore, when the mission vehicle When performing offloading tasks to the optimal edge server, the task vehicle The mathematical expression for the total completion time is: ; in, represents the roadside unit set, represents the set of service vehicles, Mission vehicle The total completion time, Mission vehicle Roadside Unit The total completion time of the uninstallation task. Mission vehicle In service vehicle The total completion time of the uninstallation task. Indicates the local total time.

[0046] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A computation offloading method based on task transmission control and improved greedy strategy, characterized in that: include: Constructing a system model between multiple task vehicles, multiple roadside units, and multiple service vehicles, and constructing multiple optional edge servers based on the roadside units and the service vehicles, wherein the system model includes a communication model and a task offloading model; Obtaining a time ratio of transmission and execution of adjacent offload tasks, and based on the time ratio, obtaining a ratio of the amount of data executed by a single optimal edge server on the offload task, to obtain a sorted sequence; Based on the order of the sorted sequence and the amount of data, the unloading task is assigned to the roadside unit or the service vehicle, and based on the coordinates of the task vehicle, the corresponding distances between the task vehicle and the edge server when the unloading task starts and ends are obtained, and the optimal edge server corresponding to all tasks, i.e., the task assignment result, is obtained; Based on the task allocation result, the base station sends the resource allocation message back to the proximal roadside unit, and the proximal roadside unit then sends the resource allocation message to the task vehicle. After a delayed waiting time, the task vehicle sends the complete task information to the assigned roadside unit or service vehicle. When the optimal edge server completes the unloading task, it sends the unloading result back to the task vehicle.

2. According to claim 1, a computation offloading method based on task transmission control and improved greedy strategy is characterized in that: When constructing a system model between multiple task vehicles, multiple roadside units, and multiple service vehicles, the system model specifically includes: a base station, a roadside unit, a task vehicle, and a service vehicle, wherein the task vehicle and the service vehicle travel in a straight line at a fixed speed during the calculation of unloading, the task vehicle is used to send a task unloading request to the roadside unit, and after transmitting complete task information to the roadside unit, receive the result returned by the roadside unit after completing the task, the service vehicle is used to receive the complete task information sent by the task vehicle, and send the task execution result to the task vehicle, the base station is used to receive the task unloading request sent by the roadside unit, and return a resource allocation message to the roadside unit, and the roadside unit is used to receive the task unloading request sent by the task vehicle, and return the resource allocation message to the task vehicle.

3. According to claim 1, a computation offloading method based on task transmission control and improved greedy strategy is characterized in that: The mathematical expression of the communication model is: ； ； in, Mission vehicle With edge servers The bandwidth of the transmission channel between Mission vehicle The transmission power, represents the channel gain, Indicates the environmental noise when the server is the receiving end. For mission vehicles With edge servers The Euclidean distance between represents the path loss component, Mission vehicle With edge servers Uplink data rate for communication between The mathematical expression of the task offloading model is: ； ； in, Mission vehicle The computational size of the task, Mission vehicle The data size of the computing task, represents the computational intensity of the task, It represents the transmission time for the task vehicle to send the unloading task to the edge server.

4. According to claim 1, a computation offloading method based on task transmission control and improved greedy strategy is characterized in that: The process of respectively obtaining the corresponding distances between the task vehicle and the edge server when the unloading task starts and is completed based on the coordinates of the task vehicle includes: ； ； ； in, Mission vehicle Distance to edge server The distance Mission vehicle Distance to roadside unit The distance Mission vehicle Distance to service vehicle The distance , Mission vehicles Relative edge server The coordinates when sending the unloading task, , They are the task vehicles in the corresponding coordinate axis directions. The driving speed, , Represented as mission vehicles Relative roadside unit The coordinates when sending the unloading task, Mission vehicle Roadside Unit The total completion time of the uninstallation task. Mission vehicle In service vehicle The total completion time of the uninstallation task. , Mission vehicles Relative service vehicles The coordinates when sending the unloading task, , They are the service vehicles in the corresponding coordinate axis directions. speed; The process of selecting the optional edge servers based on the distance and the communication range radius to obtain the optimal edge server includes: ； ； ； in, , , Roadside Unit , Service Vehicles , Edge Server The communication range radius.

5. According to claim 1, a computation offloading method based on task transmission control and improved greedy strategy is characterized in that: The mathematical expression of the sum of the total task completion time of the edge server is: ； in, represents the total completion time of the task, Represents edge server The number of tasks that need to be executed, , , Represents edge servers In the order of execution, 1, 2, ..., The execution time of a task is hour, To obtain the minimum value, the edge server executes the assigned offloading tasks in the order of task execution time from small to large, and obtains the minimum value of the sum of the total task completion time.

6. The method for computing offloading based on task transmission control and improved greedy strategy according to claim 1, characterized in that: The process of obtaining the ratio of the data volume of a single optimal edge server to perform the offloading task includes: based on the uplink data rate of the communication and the task vehicle The data volume of the computing task is obtained by the transmission time of the task vehicle sending the unloaded task to the edge server, the task unloading time is obtained based on the optimal computing volume size and the CPU cycle frequency of the edge server, and the ratio of the task data volume is obtained based on the task transmission time and the task unloading time, where The mathematical expression of the transmission time for the task vehicle to send the unloading task to the edge server is: ； The task unloading time The mathematical expression is: ； The ratio of the task data volume The mathematical expression is: ； in, represents the transmission time for the task vehicle to send the unloading task to the edge server, Mission vehicle The data size of the computing task, Mission vehicle With edge servers The uplink data rate of communication between represents the bandwidth of the transmission channel, Mission vehicle The transmission power, represents the channel gain, represents the Gaussian white noise power, Mission vehicle The amount of data in the previous task. Represents edge server The CPU cycle frequency, represents the computational intensity of the task, Mission vehicle The amount of data for the previous task. Indicates the ratio of the current vehicle task data volume to the previous vehicle task data volume.

7. The method for computing offloading based on task transmission control and improved greedy strategy according to claim 1, characterized in that: The process of the optimal edge server performing the offloading task includes: When the unloading task is assigned to and When there are edge servers, the queue waiting time set after task offloading is obtained respectively and collection , remove the collection and collection Get the collection after equal elements ; Based on the collection Assign the current optimal edge server to the user to perform the unloading task, and obtain the maximum working time of all optional edge servers of the task vehicle to be minimized, where When , the improved greedy strategy is expressed as: ； ； in, Mission vehicle The waiting time of the task in the corresponding edge server, Mission vehicle The number of edge servers that can be selected, represents the total completion time of the task vehicle assigned to the edge server to perform the unloading task, Indicates Edge servers, Indicates Edge servers.

8. The method for computing offloading based on task transmission control and improved greedy strategy according to claim 1, characterized in that: When the mission vehicle When performing offloading tasks to the optimal edge server, the task vehicle The mathematical expression for the total completion time is: ； in, represents the roadside unit set, represents the set of service vehicles, Mission vehicle The total completion time, Mission vehicle Roadside Unit The total completion time of the uninstallation task. Mission vehicle In service vehicle The total completion time of the uninstallation task. Indicates the local total time.

Images