Pendulum: Network-Compute Joint Scheduling for Efficient and Accurate MEC Live Video Analytics

Abstract

We present Pendulum, a live video analytics system with a novel network-compute joint scheduling in mobile edge computing (MEC) architecture. In practical scenarios, resource bottleneck frequently alternates across network (video streaming) and compute (DNN inference) stages due to independent fluctuations of wireless channel and scene content. However, prior single-stage scheduling systems suffer from throughput/accuracy fluctuation and resource wastage due to over-provisioning. To overcome the limitations, we newly leverage the interplay between video bitrate and DNN complexity to design an end-to-end system composed of (i) a resource-efficient network-compute demand curve profiler and (ii) a joint resource scheduler. Evaluation with various videos and state-of-the-art DNNs show that Pendulum achieves up to 0.64 mIoU gain and 1.29 × higher throughput than state-of-the-art baselines. Pendulum also achieves near-optimal multi-user resource scheduling performance with minimal search overhead, achieving 25% cost reduction compared to the network-compute decoupled scheduling.