Load Balancing for Distributed Intelligent Edge Computing: A State-Based Game Approach

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.