In a multiple-services mobile edge computing network environment, the requirements and popularity of services, the computing requirement and the amount of data transferred from users to edge servers are dynamic with time. How to adaptively adjust the caching subset of total service types in the resource-limited edge server and determine the task offloading destination and resource allocation decisions to obtain the optimum overall system performance is a challenge problem. To solve this challenge, this paper firstly converted it into a Markov decision process, then proposed a soft actor-critic(SAC) -based deep reinforcement learning algorithm to jointly determine not only the discrete decisions of service caching and task offloading but also the continuous allocation of bandwidth and computing resource. The algorithm employed an effective trick of converting multiple continuous action outputs into discrete action choices to deal with the key design challenge that arised from continuous-discrete hybrid action space while improving the accuracy of algorithm decisions. Additionally, the algorithm integrated an efficient reward function that added auxiliary reward terms to improve resource utilization. Extensive numerical results show that compared with other baseline algorithms, the proposed algorithm can effectively reduce the long-term average completion delay of tasks while accessing excellent performance in terms of stability.

[2022-12-07] Accepted Paper /doc/2022.08.0443/v1

[2023-05-05] Printed Article /doc/2022.08.0443/v2