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Energy efficient resource controller for Apache Storm
The University of Sydney, AUS.ORCID iD: 0000-0002-7851-9377
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9194-010X
The University of Sydney, AUS.ORCID iD: 0000-0002-3090-1059
RMIT University, AUS.ORCID iD: 0000-0002-1235-9673
2023 (English)In: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634, Vol. 35, no 17, article id e6799Article in journal (Refereed) Published
Abstract [en]

Apache Storm is a distributed processing engine that can reliably process unbounded streams of data for real-time applications. While recent research activities mostly focused on devising a resource allocation and task scheduling algorithm to satisfy high performance or low latency requirements of Storm applications across a distributed and multi-core system, finding a solution that can optimize the energy consumption of running applications remains an important research question to be further explored. In this article, we present a controlling strategy for CPU throttling that continuously optimize the level of consumed energy of a Storm platform by adjusting the voltage and frequency of the CPU cores while running the assigned tasks under latency constraints defined by the end-users. The experimental results running over a Storm cluster with 4 physical nodes (total 24 cores) validates the effectiveness of proposed solution when running multiple compute-intensive operations. In particular, the proposed controller can keep the latency of analytic tasks, in terms of 99th latency percentile, within the quality of service requirement specified by the end-user while reducing the total energy consumption by 18% on average across the entire Storm platform.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023. Vol. 35, no 17, article id e6799
Keywords [en]
data stream processing engines, energy-aware resource allocation algorithm, performance evaluation of computer systems
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-88072DOI: 10.1002/cpe.6799ISI: 000736445900001Scopus ID: 2-s2.0-85122132757OAI: oai:DiVA.org:kau-88072DiVA, id: diva2:1627593
Funder
Knowledge Foundation
Note

Australian Research Council, Grant/AwardNumbers: DP190103710, DP200100005

Available from: 2022-01-13 Created: 2022-01-13 Last updated: 2023-12-11Bibliographically approved

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Taheri, Javid

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