Abstract
Video cameras have been deployed widely today. Although existing systems aim to optimize live video analytics from a variety of perspectives, they are agnostic to the workload dynamics in real-world. We propose EC-MASS, an edge computing-based video scheduling system achieving both cost and performance optimization with multiple cameras and edge data centers. The intuition behind EC-MASS is to adaptively map cameras to different edge data centers according to dynamically updated configurations of cameras. We prove that generating the optimal mapping scheduling scheme is NP-Complete, and develop the scheduling algorithm by leveraging the insights of the economy consideration of camera allocation. Using the algorithm, EC-MASS is able to balance the workload among edge data centers while reducing the cost of video analytics system. We evaluate EC-MASS with datasets of video configurations from real-world cameras which randomly generate configurations for cameras, with a testbed that consists of 60 cameras and 4 edge data centers. Our results show that EC-MASS consistently outperforms the status quo in terms of cost and performance stability.
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