Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning

TL;DR

This paper explores game theory and reinforcement learning to improve resource management and computation offloading in Mobile Edge Computing, addressing challenges of resource limitations and network complexity.

Contribution

It introduces a game-theoretical model for energy-efficient edge server activation and evaluates various learning techniques for distributed resource management in MEC.

Findings

Distributed learning techniques improve resource allocation efficiency

Game-theoretical models enable fairer resource management

Numerical results demonstrate performance gains in MEC scenarios

Abstract

Due to the ever-increasing popularity of resource-hungry and delay-constrained mobile applications, the computation and storage capabilities of remote cloud has partially migrated towards the mobile edge, giving rise to the concept known as Mobile Edge Computing (MEC). While MEC servers enjoy the close proximity to the end-users to provide services at reduced latency and lower energy costs, they suffer from limitations in computational and radio resources, which calls for fair efficient resource management in the MEC servers. The problem is however challenging due to the ultra-high density, distributed nature, and intrinsic randomness of next generation wireless networks. In this article, we focus on the application of game theory and reinforcement learning for efficient distributed resource management in MEC, in particular, for computation offloading. We briefly review the cutting-edge research and discuss future challenges. Furthermore, we develop a game-theoretical model for energy-efficient distributed edge server activation and study several learning techniques. Numerical results are provided to illustrate the performance of these distributed learning techniques. Also, open research issues in the context of resource management in MEC servers are discussed.