EdgeBrain: A Game Based Collaborative Computational Task Offloading Framework for Edge Video Analytics

With the development of the Internet of Everything (IoE), huge amount of data is being generated at network edge and needs to be processed in real time, such as edge video analytics applications. However, limited computing resources and energy on edge devices make it challenging to efficiently perform the intensive computing tasks. Existing solutions of offloading tasks to edge servers depend on the availability of edge servers, and they are difficult to satisfy massive computation needs. Instead, this article proposes a novel offloading framework named EdgeBrain, which offloads computational tasks from one edge device (Producer of Computation, i.e., PCO) to other edge devices (Consumers of Computation, i.e., CCOs). We formulate this offloading problem as a multi-round non-cooperative Stackelberg game, prove the existence of unique Nash Equilibrium (NE) and Stackelberg Equilibrium (SE) in the game, and design the gradient search algorithm to calculate the optimal offloading decision. The performance evaluation based on a prototype implementation of EdgeBrain in the context of a video analytics application, face detection and recognition, shows that EdgeBrain outperforms several comparable techniques in terms of execution time, frame rate, transfer data volume, and power consumption.