A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks

TL;DR

This paper proposes a deep reinforcement learning method to optimize service migration in MEC-enabled vehicular networks, reducing latency and migration costs amid high vehicle mobility.

Contribution

It introduces a novel DQL-based approach for proactive service migration in vehicular MEC networks, improving performance over traditional methods.

Findings

DQL scheme achieves near-optimal performance.

Proactive migration reduces service latency.

Method effectively handles high mobility constraints.

Abstract

Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in a MEC-enabled vehicular network in order to minimize the total service latency and migration cost. This problem is formulated as a nonlinear integer program and is linearized to help obtaining the optimal solution using off-the-shelf solvers. Then, to obtain an efficient solution, it is modeled as a multi-agent Markov decision process and solved by leveraging deep Q learning (DQL) algorithm. The proposed DQL scheme performs a proactive services migration while ensuring their continuity under high mobility constraints. Finally, simulations results show that the proposed DQL scheme achieves close-to-optimal performance.