Novas: Tackling Online Dynamic Video Analytics With Service Adaptation at Mobile Edge Servers

Abstract

Video analytics at mobile edge servers offers significant benefits like reduced response time and enhanced privacy. However, guaranteeing various quality-of-service (QoS) requirements of dynamic video analysis requests on heterogeneous edge devices remains challenging. In this paper, we propose a scalable online video analytics scheme, called Novas, which automatically makes precise service configuration adjustments upon constant video content changes. Specifically, Novas leverages the filtered confidence sum and a two-window t-test to online detect accuracy fluctuations without ground truth information. In such cases, Novas efficiently estimates the performance of all potential service configurations through a singular value decomposition (SVD)-based collaborative filtering method. Finally, given the NP-hardness of the optimal scheduling problem, a heuristic scheduling strategy that maximizes the minimum remaining resources is devised to schedule the most suitable configurations to servers for execution. We evaluate the effectiveness of Novas through extensive hybrid experiments conducted on a dedicated testbed. Results show that Novas can achieve a substantial over 27 $\times$ improvement in satisfying the accuracy requirements compared with existing methods adopting fixed configurations, while ensuring latency requirements. Moreover, Novas improves the goodput of the system by an average of 37.86% compared to existing state-of-the-art scheduling solutions.