Joint Task Offloading, Resource Allocation, and Service Caching in Mobile Edge Computing via Soft Actor-Critic Learning

Mobile edge computing (MEC) has emerged as a powerful solution for handling computation-intensive applications at the network edge. Task offloading has become a widely adopted strategy to optimize network resource utilization and deliver enhanced services to user equipment (UE). To meet stringent end-to-end delay and energy consumption requirements, users can offload tasks to edge servers where relevant services are pre-cached. However, the limited storage capacity and the heterogeneity of edge nodes make collaborative cache decision-making and computing resource allocation at the edge critical challenges. We address the optimization of task offloading, resource allocation, service caching, and push decisions within the MEC system through the coordinated efforts of edge servers. The objective is to minimize long-term offloading latency while maximizing the cache hit ratio, presenting a mixed-integer nonlinear programming (MINLP) problem. To tackle this challenge, we propose a novel soft actor-critic approach, transforming the problem into a Markov decision process (MDP). Numerical results demonstrate that our algorithm significantly outperforms existing methods in terms of system cost, average delay, and cache hit ratio.