Multi-server Multi-user Game at Edges for Heterogeneous Video Analytics

In past years, artificial intelligence related services and applications have boomed, which require high computation, high bandwidth and low latency. Edge computing is regarded as an appropriate solution for them, especially video analytics. In this paper, we study the multi-server multi-user heterogeneous video analytics offloading problem, where users select appropriate edge servers and then offload their raw video data to the servers for essential analytics. To deal with the cooperation and conflicts among users and get a stable situation where each user has no incentive to change the offloading decision unilaterally, we formulate the video analytics offloading problem as a multiplayer game. Based on the goal of minimizing the overall delay, we design the potential optimal server selection strategy and then propose a game theory-based algorithm, through which the Nash equilibrium can be reached. Furthermore, we analyze its near-optimal performance via rigorous proof. Finally, extensive trace-driven experiments show that our method improves the overall delay by 48% on average, compared with other algorithms.