Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning
Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning
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发表时间：
2017-11
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通讯作者：
S. Ranadheera;S. Maghsudi;E. Hossain
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作者：
S. Ranadheera;S. Maghsudi;E. Hossain
由于资源紧缺和延迟受限的移动应用的日益流行，远程云的计算和存储能力已部分迁移到移动边缘，从而产生了移动边缘计算(MEC)的概念。虽然MEC服务器非常接近最终用户，从而以更短的延迟和更低的能源成本提供服务，但它们受到计算和无线电资源的限制，这就要求在MEC服务器中进行公平有效的资源管理。然而，由于下一代无线网络的超高密度、分布式和固有的随机性，这一问题具有挑战性。在这篇文章中，我们着重于博弈论和强化学习在MEC中有效的分布式资源管理中的应用，特别是在计算卸载方面。我们简要回顾了前沿研究，并讨论了未来的挑战。此外，我们建立了能量高效的分布式边缘服务器激活的博弈论模型，并研究了几种学习技术。数值结果说明了这些分布式学习技术的性能。此外，还讨论了MEC服务器中的资源管理方面的开放研究问题。
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.