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Video processing with serverless computing: a measurement study

Published: 21 June 2019 Publication History

Abstract

The growing demand for video processing and the advantages in scalability and cost reduction brought by the emerging serverless computing have attracted significant attention in serverless computing powered video processing. However, how to implement and configure serverless functions to optimize the performance and cost of video processing applications remains unclear. In this paper, we explore the configuration and implementation schemes of typical video processing functions deployed to the serverless platforms and quantify their influence on the execution duration and monetary cost from a developer's perspective. Our measurement reveals that memory configuration is non-trivial. Dynamic profiling of workloads is necessary to find the best memory configuration. Moreover, compared with calling external video processing APIs, implementing these services locally in serverless functions can be competitive. We also find that the performance of video processing applications could be affected by the underlying infrastructure. Our work provides guidelines for further function-level optimization and complements the existing measurement studies for both serverless computing and video processing.

References

[1]
I. E. Akkus, R. Chen, I. Rimac, M. Stein, K. Satzke, A. Beck, P. Aditya, and V. Hilt. 2018. SAND: Towards High-Performance Serverless Computing. In USENIX ATC.
[2]
L. Ao, L. Izhikevich, G. M. Voelker, and G. Porter. 2018. Sprocket: A Serverless Video Processing Framework. In ACM SoCC.
[3]
Cisco. 2018. Cisco Visual Networking Index: Forecast and Trends, 2017--2022. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html
[4]
S. Fouladi, R. S. Wahby, B. Shacklett, K. Balasubramaniam, W. Zeng, R. Bhalerao, A. Sivaraman, G. Porter, and K. Winstein. 2017. Encoding, Fast and Slow: Low-Latency Video Processing Using Thousands of Tiny Threads. In USENIX NSDI.
[5]
G. Gao and Y. Wen. 2016. Morph: A fast and scalable cloud transcoding system. In ACM MM.
[6]
J. M. Hellerstein, J. Faleiro, J. E. Gonzalez, J. Schleier-Smith, V. Sreekanti, A. Tumanov, and C. Wu. 2018. Serverless Computing: One Step Forward, Two Steps Back. arXiv preprint arXiv.1812.03651 (2018).
[7]
E. Jonas, Q. Pu, S. Venkataraman, I. Stoica, and B. Recht. 2017. Occupy the cloud: Distributed computing for the 99%. In ACM SoCC.
[8]
A. Klimovic, Y. Wang, C. Kozyrakis, P. Stuedi, J. Pfefferle, and A. Trivedi. 2018. Understanding ephemeral storage for serverless analytics. In USENIX ATC.
[9]
W. Lloyd, S. Ramesh, S. Chinthalapati, L. Ly, and S. Pallickara. 2018. Serverless computing: An investigation of factors influencing microservice performance. In IEEE IC2E.
[10]
H. Ma, B. Seo, and R. Zimmermann. 2014. Dynamic scheduling on video transcoding for MPEG DASH in the cloud environment. In ACM MMSys.
[11]
R. Olga, D. Jia, S. Hao, K. Jonathan, S. Sanjeev, M. Sean, H. Zhiheng, K. Andrej, K. Aditya, B. Michael, C. B. Alexander, and F. Li. 2015. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115, 3 (2015), 211--252.
[12]
K. Sembiring and A. Beyer. 2013. Dynamic resource allocation for cloud-based media processing. In ACM NOSSDAV.
[13]
L. Wang, M. Li, Y. Zhang, T. Ristenpart, and M. Swift. 2018. Peeking behind the curtains of serverless platforms. In USENIX ATC.
[14]
H. Zhang, G. Ananthanarayanan, P. Bodik, M. Philipose, P. Bahl, and M. J. Freedman. 2017. Live Video Analytics at Scale with Approximation and Delay-Tolerance. In USENIX NSDI.
[15]
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao. 2016. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters 23, 10 (2016), 1499--1503.
[16]
X. Zhu, Y. Wang, J. Dai, L. Yuan, and Y. Wei. 2017. Flow-guided feature aggregation for video object detection. In IEEE ICCV.

Cited By

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  • (2024)Smart Healthcare System in Server-Less Environment: Concepts, Architecture, Challenges, Future DirectionsComputers10.3390/computers1304010513:4(105)Online publication date: 19-Apr-2024
  • (2024)FAAStloop: Optimizing Loop-Based Applications for Serverless ComputingProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698560(943-960)Online publication date: 20-Nov-2024
  • (2024)SPSC: Stream Processing Framework Atop Serverless Computing for Industrial Big DataIEEE Transactions on Cybernetics10.1109/TCYB.2024.340788654:11(6509-6517)Online publication date: Nov-2024
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cover image ACM Conferences
NOSSDAV '19: Proceedings of the 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video
June 2019
86 pages
ISBN:9781450362986
DOI:10.1145/3304112
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 June 2019

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Author Tags

  1. serverless computing
  2. serverless functions
  3. video processing

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  • Research-article

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MMSys '19
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MMSys '19: 10th ACM Multimedia Systems Conference
June 21, 2019
Massachusetts, Amherst

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NOSSDAV '19 Paper Acceptance Rate 12 of 32 submissions, 38%;
Overall Acceptance Rate 118 of 363 submissions, 33%

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Cited By

View all
  • (2024)Smart Healthcare System in Server-Less Environment: Concepts, Architecture, Challenges, Future DirectionsComputers10.3390/computers1304010513:4(105)Online publication date: 19-Apr-2024
  • (2024)FAAStloop: Optimizing Loop-Based Applications for Serverless ComputingProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698560(943-960)Online publication date: 20-Nov-2024
  • (2024)SPSC: Stream Processing Framework Atop Serverless Computing for Industrial Big DataIEEE Transactions on Cybernetics10.1109/TCYB.2024.340788654:11(6509-6517)Online publication date: Nov-2024
  • (2023)The state of art and review on video streamingJournal of High Speed Networks10.3233/JHS-22208729:3(211-236)Online publication date: 1-Jan-2023
  • (2023)The Night Shift: Understanding Performance Variability of Cloud Serverless PlatformsProceedings of the 1st Workshop on SErverless Systems, Applications and MEthodologies10.1145/3592533.3592808(27-33)Online publication date: 8-May-2023
  • (2023)Punching Holes in the Cloud: Direct Communication Between Serverless FunctionsServerless Computing: Principles and Paradigms10.1007/978-3-031-26633-1_2(15-41)Online publication date: 12-May-2023
  • (2023)Workflow aware analytical model to predict performance and cost of serverless executionConcurrency and Computation: Practice and Experience10.1002/cpe.774335:22Online publication date: 20-Apr-2023
  • (2022)Serverless Architecture for Healthcare Management SystemsHandbook of Research on Mathematical Modeling for Smart Healthcare Systems10.4018/978-1-6684-4580-8.ch011(203-227)Online publication date: 24-Jun-2022
  • (2022)Scalable and Cost-effective Serverless Architecture for Information Extraction WorkflowsProceedings of the 2nd Workshop on High Performance Serverless Computing10.1145/3526060.3535458(15-23)Online publication date: 30-Jun-2022
  • (2022)CharmSeeker: Automated Pipeline Configuration for Serverless Video ProcessingIEEE/ACM Transactions on Networking10.1109/TNET.2022.318323130:6(2730-2743)Online publication date: Dec-2022
  • Show More Cited By

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