Towards cloud-edge collaborative online video analytics with fine-grained serverless pipelines

The ever-growing deployment scale of surveillance cameras and the users' increasing appetite for real-time queries have urged online video analytics. Synergizing the virtually unlimited cloud resources with agile edge processing would deliver an ideal online video analytics system; yet, given the complex interaction and dependency within and across video query pipelines, it is easier said than done. This paper starts with a measurement study to acquire a deep understanding of video query pipelines on real-world camera streams. We identify the potentials and practical challenges towards cloud-edge collaborative video analytics. We then argue that the newly emerged serverless computing paradigm is the key to achieve fine-grained resource partitioning with minimum dependency. We accordingly propose CEVAS, a Cloud-Edge collaborative Video Analytics system empowered by fine-grained Serverless pipelines. It builds flexible serverless-based infrastructures to facilitate fine-grained and adaptive partitioning of cloud-edge workloads for multiple concurrent query pipelines. With the optimized design of individual modules and their integration, CEVAS achieves real-time responses to highly dynamic input workloads. We have developed a prototype of CEVAS over Amazon Web Services (AWS) and conducted extensive experiments with real-world video streams and queries. The results show that by judiciously coordinating the fine-grained serverless resources in the cloud and at the edge, CEVAS reduces 86.9% cloud expenditure and 74.4% data transfer overhead of a pure cloud scheme and improves the analysis throughput of a pure edge scheme by up to 20.6%. Thanks to the fine-grained video content-aware forecasting, CEVAS is also more adaptive than the state-of-the-art cloud-edge collaborative scheme.