research-article

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

Authors:

Jiangchuan Liu,

Zhi WangAuthors Info & Claims

MMSys '21: Proceedings of the 12th ACM Multimedia Systems Conference

Pages 80 - 93

https://doi.org/10.1145/3458305.3463377

Published: 15 July 2021 Publication History

Abstract

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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https://doi.org/10.1186/s40537-024-00939-0
Gao GDong YWang RZhou X(2024)EdgeVision: Towards Collaborative Video Analytics on Distributed Edges for Performance MaximizationIEEE Transactions on Multimedia10.1109/TMM.2024.338567826(9083-9094)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3385678
Zhao KZhou ZJiao LCai SXu FChen X(2024)Taming Serverless Cold Start of Cloud Model Inference With Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2023.334816523:8(8111-8128)Online publication date: Aug-2024
https://doi.org/10.1109/TMC.2023.3348165
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Index Terms

Towards cloud-edge collaborative online video analytics with fine-grained serverless pipelines
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
2. Networks
  1. Network services
    1. Cloud computing

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cover image ACM Conferences

MMSys '21: Proceedings of the 12th ACM Multimedia Systems Conference

June 2021

254 pages

ISBN:9781450384346

DOI:10.1145/3458305

General Chairs:
Özgü Alay
University of Oslo and Simula Metropolitan
,
Cheng-Hsin Hsu
National Tsing Hua University
,
Ali C. Begen
Ozyegin University and Networked Media

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Published: 15 July 2021

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Shenzhen Science and Technology Program
SJTU Explore-X Research
Key Area R&D Program of Guangdong Province

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MMSys '21

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SIGMM

MMSys '21: 12th ACM Multimedia Systems Conference

September 28 - October 1, 2021

Istanbul, Turkey

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MMSys '21 Paper Acceptance Rate 18 of 55 submissions, 33%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Shojaee Rad ZGhobaei-Arani M(2024)Data pipeline approaches in serverless computing: a taxonomy, review, and research trendsJournal of Big Data10.1186/s40537-024-00939-011:1Online publication date: 11-Jun-2024
https://doi.org/10.1186/s40537-024-00939-0
Gao GDong YWang RZhou X(2024)EdgeVision: Towards Collaborative Video Analytics on Distributed Edges for Performance MaximizationIEEE Transactions on Multimedia10.1109/TMM.2024.338567826(9083-9094)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3385678
Zhao KZhou ZJiao LCai SXu FChen X(2024)Taming Serverless Cold Start of Cloud Model Inference With Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2023.334816523:8(8111-8128)Online publication date: Aug-2024
https://doi.org/10.1109/TMC.2023.3348165
Rastegar SShafiei HKhonsari A(2024)EneX: An Energy-Aware Execution Scheduler for Serverless ComputingIEEE Transactions on Industrial Informatics10.1109/TII.2023.329098520:2(2342-2353)Online publication date: Feb-2024
https://doi.org/10.1109/TII.2023.3290985
Singh MRachuri SCao BSharma ABhumireddy VBronzino FDas SGandhi AJain S(2024)OVIDA: Orchestrator for Video Analytics on Disaggregated Architecture2024 IEEE/ACM Symposium on Edge Computing (SEC)10.1109/SEC62691.2024.00019(135-148)Online publication date: 4-Dec-2024
https://doi.org/10.1109/SEC62691.2024.00019
Dou ZWang F(2024)Application of Reinforcement Learning Algorithms in Intelligent Sports Training Video Tracking2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)10.1109/ICICNIS64247.2024.10823338(948-953)Online publication date: 17-Dec-2024
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Peng HZhan YLi PXia Y(2024)Tangram: High-Resolution Video Analytics on Serverless Platform with SLO-Aware Batching2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00066(645-655)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00066
Liang YZhang SWu J(2024)SplitStreamJournal of Network and Computer Applications10.1016/j.jnca.2024.103866225:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.jnca.2024.103866
Mirampalli SWankar RSrirama S(2024)Evaluating NiFi and MQTT based serverless data pipelines in fog computing environmentsFuture Generation Computer Systems10.1016/j.future.2023.09.014150:C(341-353)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.future.2023.09.014
Zhang MCao JSahni YChen QJiang SYang L(2023)Blockchain-based Collaborative Edge Intelligence for Trustworthy and Real-Time Video SurveillanceIEEE Transactions on Industrial Informatics10.1109/TII.2022.320339719:2(1623-1633)Online publication date: Feb-2023
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