Deep Learning Video Analytics Through Online Learning Based Edge Computing

Video analytics demand intensive computation resources, which means long processing delay when running on mobile devices. Although offloading computation to the cloud can partially solve the problem, transferring videos to the cloud introduces high transmission delay. With mobile edge computing, computation can be offloaded to the nearby edge servers to reduce the delay. However, the computation resources of the edge servers are usually limited and highly dynamic, and then server selection should be adaptive in order to improve the performance of video analytics. Also, frame resolution should be selected to achieve a better tradeoff between accuracy and frame processing rate. In this paper, we study the server resource-aware offloading problem for video analytics, where the goal is to maximize the utility which is a weighted function of accuracy and frame processing rate. The major challenge to solve this problem is the lack of server and network knowledge and the dynamic system environment. To overcome these challenges, we formulate the problem as a contextual Multi-armed Bandit problem, and propose a Bayesian Optimization based online learning algorithm to gradually learn the server status and the optimal solution, and make it adaptable for time-varying environments. Both theoretical analysis and evaluation results demonstrate the superior performance of our proposed algorithm.