Deep Reinforcement Learning based Mobility-Aware Service Migration for Multi-access Edge Computing Environment

Abstract

Multi-access Edge Computing (MEC) plays an im-portant role for providing end users with high reliability and low latency services at the edge of mobile network. In the scenario of Internet of Vehicles (IoV), vehicle users continually access nearby base stations to offload real-time tasks for reducing their computing overhead, while the ongoing services on current deployed edge nodes may be far away from users with the vehicles moving, potentially resulting in a high delay of data transmission. To address this challenge, in this paper, we propose a Deep Reinforcement Learning (DRL)-based mobility-aware service migration mechanism for effectively reducing the service delay and migration delay of the network. The proposed technique is adopted by re-calibrating required services at edge locations near the mobile user. Edge network state and user movement information are considered to ensure the generation of real-time service migration decision. Extensive experiments are conducted, and evaluation results demonstrate that our proposed DRL-based technique can effectively reduce the long-term average delay of the MEC system, compared with the state-of-the-art techniques.