Distream: scaling live video analytics with workload-adaptive distributed edge intelligence

Video cameras have been deployed at scale today. Driven by the breakthrough in deep learning (DL), organizations that have deployed these cameras start to use DL-based techniques for live video analytics. Although existing systems aim to optimize live video analytics from a variety of perspectives, they are agnostic to the workload dynamics in real-world deployments. In this work, we present Distream, a distributed live video analytics system based on the smart camera-edge cluster architecture, that is able to adapt to the workload dynamics to achieve low-latency, high-throughput, and scalable live video analytics. The key behind the design of Distream is to adaptively balance the workloads across smart cameras and partition the workloads between cameras and the edge cluster. In doing so, Distream is able to fully utilize the compute resources at both ends to achieve optimized system performance. We evaluated Distream with 500 hours of distributed video streams from two real-world video datasets with a testbed that consists of 24 cameras and a 4-GPU edge cluster. Our results show that Distream consistently outperforms the status quo in terms of throughput, latency, and latency service level objective (SLO) miss rate.