Distributed DNN-based Video Analytics with Adaptive Multi-Device Collaboration

Zhihao Xu, Maozhe Zhao, Qinya Li, Shengzhong Liu, Fan Wu, Guihai Chen

Abstract: This paper optimizes the computation efficiency for DNN-based video analytics on interconnected edge devices like surveillance cameras and unmanned aerial vehicles (UAVs). Existing solutions depend heavily on computation offloading to edge servers but overlook the potential for cross-device collaboration and leave resources on some edge devices underutilized. We instead propose AdaCollab, an adaptive multi-device collaboration framework for resource-efficient distributed edge video analytics. On the one hand, as the machine perception complexity fluctuates with runtime video content, AdaCollab dynamically calibrates DNN inspection configurations on each device without degrading the model accuracy. On the other hand, AdaCollab aligns mismatched resources and workload among edge devices by selectively offloading partial computations from overloaded devices to underloaded devices, optimizing both computation and bandwidth resource utilization. Through extensive evaluations with large-scale real-world surveillance videos on testbeds of heterogeneous NVIDIA Jetson platforms, AdaCollab outperforms the SOTA baselines by up to 25.8% in frame processing through-put and 19.8% in DNN accuracy, while exhibiting enhanced robustness under restricted network bandwidth.

External IDs:dblp:conf/icdcs/XuZLL0C25