Performance characterization of video analytics workloads in heterogeneous edge infrastructures

Abstract

SummaryPowered by deep learning, video analytic applications process millions of camera feeds in real‐time to extract meaningful information from their surroundings. And this number grows by the minute. To avoid saturating the backhaul network and provide lower latencies, a distributed and heterogeneous edge cloud is postulated as a key enabler for widespread video analytics. This article provides a complete characterization of end‐to‐end video analytics across a set of hardware platforms and different neural network architectures. Each platform is selected to fill a different gap in a distributed, shared, and heterogeneous infrastructure. Moreover, we analyze how performance scales on each of these platforms with respect to the amount of resources dedicated to video analytics. Finally, we extract the key conclusions of the characterization to build an experimental model to estimate performance and cost of end‐to‐end video analytics in different edge scenarios. Our experiments show that managing video analytics workloads efficiently requires awareness of both, the platforms in which these are executed, and the full end‐to‐end pipeline. To the best of our knowledge, this is the first work that provides a complete characterization of end‐to‐end video analytics in heterogeneous edge platforms.