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× 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.

PDF