Adaptive Deep Reinforcement Learning Approach for Service Migration in MEC-Enabled Vehicular Networks

Abstract

Multiaccess edge computing (MEC) has emerged as a promising technology for time-sensitive and computation-intensive tasks. However, user mobility, particularly in vehicular networks, and limited coverage of Edge Server result in service interruptions and a decrease in Quality of Service (QoS). Service migration has the potential to effectively resolve this issue. In this paper, we investigate the problem of service migration in a MEC-enabled vehicular network to minimize the total service latency and migration cost. To this end, we formulate the service migration problem as a Markov decision process (MDP). We present novel contributions by providing optimal adaptive migration strategies which consider vehicle mobility, server load, and different service profiles. We solve the problem using the Double Deep Q-network algorithm (DDQN). Simulation results show that the proposed DDQN scheme achieves a better tradeoff between latency and migration cost compared with other approaches.