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Multi-directional CPU resource control in edge computing

Published: 24 November 2020 Publication History

Abstract

Edge computing promises a considerable reduction in latency and data volume by placing edge frameworks near the data source. Stream processing framework is a critical use case for edge computing, presenting in-situ data processing and low latency. However, careful CPU resource control is more important than cloud computing to provide effective multi-tenancy in resource-constrained edge computing. In this work, we investigate practical ways of controlling CPU resources of stream processing frameworks. Evaluation results from the different stream processor parallelism and cgroup parameters give a clear direction of designing an essential controller for stream processing frameworks in edge computing.

References

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Shohin Aheleroff, Xun Xu, Yuqian Lu, Mauricio Aristizabal, Juan Pablo Velásquez, Benjamin Joa, and Yesid Valencia. 2020. IoT-enabled smart appliances under industry 4.0: A case study. Advanced Engineering Informatics 43 (2020), 101043.
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Anaconda. 2018. Dask.distributed: Lightweight library for distributed computing in Python. https://distributed.dask.org/en/latest/.
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Y. K. Kim, M. R. HoseinyFarahabady, Y. C. Lee, and A. Y. Zomaya. 2020. Automated Fine-Grained CPU Cap Control in Serverless Computing Platform. IEEE Transactions on Parallel and Distributed Systems 31, 10 (2020), 2289--2301.
[4]
Streamz. 2020. Real-time stream processing for python. https://streamz.readthedocs.io/en/latest/.
[5]
Paul Turner, Bharata B Rao, and Nikhil Rao. 2010. CPU bandwidth control for CFS. In Proceedings of the Linux Symposium. 245--254.

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cover image ACM Conferences
CoNEXT '20: Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies
November 2020
585 pages
ISBN:9781450379489
DOI:10.1145/3386367
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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Association for Computing Machinery

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Publication History

Published: 24 November 2020

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  • Research-article

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  • Australian Research Council Discovery scheme

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CoNEXT '20
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Overall Acceptance Rate 198 of 789 submissions, 25%

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