Research On Vehicle Computing Offloading For Task Execution Efficiency And Fairness

The Internet of Vehicles is an intelligent network system composed of mobile vehicles,which have the ability of communication,computing,storage,and learning.It can enhance the road safety,improve the level of the road traffic management,and support immersive user experience.However,for resource-limited vehicles,how to deal with the huge amount of data collected by vehicles’ sensors and serve applications that require great computing power or ultra-low latency is a major challenge for the field of Internet of Vehicles today.Mobile edge computing,as an emerging network technology,can effectively improve the computation and offloading efficiency.Combining it with the Internet of Vehicles can solve some of these problems.However,the actual Internet of Vehicles environment is more complex,and some scenarios have not been considered.In this regard,this article focuses on two kinds of actual problems in the vehicular network environment and then designed corresponding vehicle computing offloading schemes,and the main work is as follows:1.On rural roads or highway scenarios without mobile edge computing servers,when vehicles have a lot of tasks to be processed,how to effectively utilize the nearby computing resources of idle vehicles is the primary issue that needs to be considered.Therefore,a multi-hop task offloading decision model based on task execution efficiency is proposed in this article.It includes two parts: candidate vehicles selection mechanism and the design of the task offloading decision algorithm.In the part of candidate vehicle selection mechanism,the task vehicle obtains the detailed information of neighboring vehicles within the k-hop communication range through wireless communication,and then,the maximum task processing capability of neighboring vehicles is calculated by considering the connection time between vehicles and the task transmission time to further determine the vehicles eligible to participate in the task offload.In the offloading decision algorithm design part,the article considers the task completion efficiency,takes the minimum delay required to complete all tasks as the optimization goal,and models the computing offloading problem as a generalized assignment model with constraints.Then it is solved with the help of the greedy algorithm and the discrete bat algorithm respectively.The experimental results show that both methods have superior performance in terms of delay,and the solution method based on the discrete bat algorithm yields better performance.2.When the vehicle is able to communicate with the mobile edge computing server,the vehicle can consider offloading the task to the edge node to complete it.However,the arrival of tasks is uncertain,how to directly get the optimal offloading strategy from the original information of the Internet of Vehicles environment and make the vehicle edge computing system obtain the maximum long-term reward is another issue that needs to be considered.To address this problem,this article proposes a dynamic computing offloading scheme based on the task execution efficiency and fairness.The scheme uses asynchronous advantage actor-critic algorithm of deep reinforcement learning.It takes the available resources of the mobile edge computing server as the system state and the offloading strategy as the action to establish a Markov decision process,and then uses the weighted value of the relative efficiency factor and the relative fairness factor as the objective function to design rewards.The simulation results show that the strategy proposed can be updated by the on-policy method.After the model converges,the average delay for completing all tasks is low,the fairness between tasks is high,and it can better adapt to complex environment changes.