DDPG-based Computation Offloading and Service Caching in Mobile Edge Computing

Abstract

Mobile Edge Computing (MEC) migrates the computing center to the network edge to provide computing services for Mobile Users (MUs). However, due to the limited capacity of MUs and insufficient types of services stored by edge servers, it brings great challenges to computation offloading and service caching in Internet of Things (IoT) network. In this paper, we investigate the joint optimization problem of computation offloading, resource allocation and service caching replacement in a collaborative MEC system to minimize the total cost of all MUs under the latency and resource constraints. Accordingly, we formulate the problem as a Mixed Integer Non-linear Programming (MINLP) problem. In order to solve this problem, the Deep Deterministic Policy Gradient (DDPG)-based algorithm is proposed. The simulation results show that the proposed algorithm is better than other benchmark schemes in different scenarios.