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Due to the ever-increasing popularity of resource-hungry anddelay-constrained mobile applications, the computation and storage capabilitiesof remote cloud has partially migrated towards the mobile edge, giving rise tothe concept known as Mobile Edge Computing (MEC). While MEC servers enjoy theclose proximity to the end-users to provide services at reduced latency andlower energy costs, they suffer from limitations in computational and radioresources, which calls for fair efficient resource management in the MECservers. The problem is however challenging due to the ultra-high density,distributed nature, and intrinsic randomness of next generation wirelessnetworks. In this article, we focus on the application of game theory andreinforcement learning for efficient distributed resource management in MEC, inparticular, for computation offloading. We briefly review the cutting-edgeresearch and discuss future challenges. Furthermore, we develop agame-theoretical model for energy-efficient distributed edge server activationand study several learning techniques. Numerical results are provided toillustrate the performance of these distributed learning techniques. Also, openresearch issues in the context of resource management in MEC servers arediscussed.

Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning