Joint Service Caching, Resource Allocation and Computation Offloading in Three-Tier Cooperative Mobile Edge Computing System

Abstract

Mobile Edge Computing (MEC) provides cloud-like computing functionalities for mobile devices (MDs) by deploying servers at the edge of future 6 G network. The cooperation among multi-servers and remote cloud offers low-latency services for MDs, meeting the demand of numerous delay-sensitive computation tasks. However, the limited storage capacity of edge servers makes it difficult to cache all kinds of services from the cloud simultaneously. Furthermore, the available bandwidth of communication links and the computation capacity of edge servers are insufficient for all MDs. In this paper, we consider a multi-server MEC scenario, where exists the competitions of multiple MDs for communication and computation resource, and various services for storage capacity. We investigate task offloading and resource allocation strategy in the three-tier MEC system, and explore the service caching strategy of the edge server. We aim at minimizing the cost of delay and energy consumption, and formulate the joint optimization problem as Markov Decision Process (MDP). Based on Double Deep Q-Network (DDQN) algorithm, the joint service caching, resource allocation and computation offloading scheme (SCRACO) is proposed to solve the problem. The simulation results demonstrate that the proposed scheme can effectively reduce total cost of the system compared with other three benchmarks.