MSs-MEC中基于DRL的服务缓存和任务迁移联合优化算法OA北大核心CSTPCD

MSs-MEC can adaptively adjust service cache decisions according to needs,so that edge servers deploys on the user side can flexibly handle tasks of different service types.However,in practical applications,the successful migration of specific types of tasks depends on the early installation of the service environment.In addition,simultaneous task migration and service caching may result in time conflicts and computation delays.Therefore,in response to the above issues,this paper firstly decoupled task migration and service caching decisions.To address the shortcomings of DRL in high-dimensional mixed decision spaces,where performance improvement was not significant(such as low utilization rate during resource allocation),it combined DRL with Transformer to learn from historical data and output task migration decisions for the current time slot and the next time slot,ensuring that tasks could be executed immediately when they reached the edge server.Secondly,in order to improve resource utilization in resource allocation problems,it decomposed the problem into continuous resource allocation problems and discreted task migration and service caching problems,and used convex optimization technique to solve the opti-mal decision for resource allocation.Extensive numerical results indicate that the proposed algorithm can effectively reduce the average completion delay of tasks compared to other baseline algorithms,at the same time,it also has excellent performance in resource utilization and stability.