Mobility-aware Service Migration in MEC System

Multi-access edge computing (MEC) has emerged as an effective approach for enhancing system quality. Nevertheless, the movement of users and variations in demand for the service might lead to an increase in system delays. This article investigates the issue of service migration, with a particular focus on the factors of mobility and service availability. Specifically, we model the Markov Decision Process (MDP) problem. To make effective service migration decisions, we propose a deep reinforcement learning (DRL) model. Furthermore, a recurrent neural network (RNN) is implemented in order to enhance model performance by predicting user movement. The experimental results demonstrate the effectiveness of the proposed method in reducing the system delay.