The introduction of artificial intelligence (AI) into edge computing could significantly improve its quality of service. Connecting them into a system can provide services for a wider range. However, due to the mobility of the crowd and mobile devices, the load imbalance issue of these interconnected intelligent edge servers (IESs) will cause severe impacts on their service performance. To this end, we investigate load balancing for the distributed IESs from the game theoretic perspective and propose a state-based distributed learning algorithm. Firstly, by modelling the IES cost as the deviation between its execution time and the system average execution time, we formulate load balancing as a state-based game where each IES competes to maximize its own utility. Secondly, according to the definition of the recurrent state Nash equilibrium, we prove that this game has such an equilibrium by establishing a potential function at each reachable state. Finally, we propose a state-based distributed learning algorithm to obtain the pure Nash equilibrium strategy of each IES. Then, an ordinary differential equation is derived to prove the convergence of the algorithm. In comparison with existing works, our approach could largely improve load balancing for the distributed IESs and thus enhance their service performance.

中文翻译：

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分布式智能边缘计算的负载均衡：一种基于状态的博弈方法

将人工智能 (AI) 引入边缘计算可以显着提高其服务质量。将它们连接成一个系统可以提供更广泛的服务。然而，由于人群和移动设备的移动性，这些互连的智能边缘服务器（IES）的负载不平衡问题将对其服务性能造成严重影响。为此，我们从博弈论的角度研究了分布式 IES 的负载平衡，并提出了一种基于状态的分布式学习算法。首先，通过将 IES 成本建模为其执行时间和系统平均执行时间之间的偏差，我们将负载平衡制定为基于状态的游戏，其中每个 IES 竞争以最大化其自身的效用。其次，根据循环状态纳什均衡的定义，我们通过在每个可达状态建立一个势函数来证明这个博弈具有这样的均衡。最后，我们提出了一种基于状态的分布式学习算法来获得每个IES的纯纳什均衡策略。然后推导出常微分方程来证明算法的收敛性。与现有工作相比，我们的方法可以在很大程度上改善分布式 IES 的负载平衡，从而提高其服务性能。