Journal
MMSYS '21: PROCEEDINGS OF THE 2021 MULTIMEDIA SYSTEMS CONFERENCE
Volume -, Issue -, Pages 80-93Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3458305.3463377
Keywords
Video Analytics; Serverless Computing; Cloud-Edge Collaboration
Categories
Funding
- Industry Canada Technology Demonstration Program (TDP) Grant
- Key Area R&D Program of Guangdong Province [2018B030338001]
- SJTU Explore-X Research Grant
- Shenzhen Science and Technology Program [RCYX20200714114523079]
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This paper dives deep into the measurement study of video query pipelines, identifies the potentials and challenges of cloud-edge collaborative video analytics, and proposes serverless computing as a key solution for fine-grained resource partitioning. With the development of the CEVAS system and experiments conducted on Amazon Web Services (AWS), real-time responses to multiple concurrent query pipelines are achieved effectively.
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.
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