EdgeEye: A Data-Driven Approach for Optimal Deployment of Edge Video Analytics

Deep neural network (DNN)-based video processing methods are applied in mobile video analytics because of high accuracy. Edge computing is an efficient paradigm that improves the performance of mobile video analytics. However, due to the limited computing and storage resources at edge devices, deploying DNN-based video analytics at edge devices may have difficulty to meet user’s requirements in terms of accuracy, delay, power consumption, and device costs. Choosing optimal system configuration, including resources on edge devices and parameters in video stream and DNN models, can better satisfy user’s performance requirements; however, there lacks practical approaches to find such optimal configurations. In this article, we take an initial step to investigate the optimal system configuration problem, and propose a data-driven approach, EdgeEye, which first models the above problem as a combinatorial optimization problem, and then designs an algorithm to find the solutions for the optimal configuration. These models and algorithms are applied in a real-world face detection and recognition application based on two edge computing models, including edge only and edge server. Comprehensive evaluation results demonstrate that EdgeEye can find both feasible and optimal system configurations including optimal edge computing model to satisfy varying user requirements under different network conditions.