Multiple Services Mobile edge computing (MSs-MEC) can adaptively adjust service cache decisions according to needs, so that edge servers deployed 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 article first decouples task migration and service caching decisions. To address the shortcomings of deep reinforcement learning (DRL) in high-dimensional mixed decision spaces where performance improvement is not significant (such as low utilization rate during resource allocation) , DRL is combined with Transformer to learn from historical data and output task migration decisions for the current time slot and the next time slot, Ensure that tasks can be executed immediately when they reach the edge server. Secondly, in order to improve resource utilization in resource allocation problems, the problem is decomposed into continuous resource allocation problems and discrete task migration and service caching problems, and convex optimization techniques are used to solve the optimal 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.

[2024-01-22] Accepted Paper /doc/2023.10.0538/v1