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SpinSmart: Exploring Optimal Server Fan Speeds to Improve Overall System Energy Consumption

Authors:

Arun Vishwanath,

Guru Venkataramani,

Suresh SubramaniamAuthors Info & Claims

e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems

Pages 474 - 481

https://doi.org/10.1145/3396851.3402655

Published: 18 June 2020 Publication History

Abstract

Cost of data centers has risen sharply in the past few years. Today, it represents about 3% of total US energy consumption with projections to increase further in the coming years. In this paper, we focus on the server infrastructure and observe that workload consolidation techniques, which maximize power efficiency of server systems, do not automatically optimize the overall system power efficiency especially when compute engines and the corresponding on-board cooling systems are considered holistically. We design SpinSmart, a framework that explores optimal server fan speeds to minimize the overall system energy consumption. We explore core capping strategies that estimate the desired number of CPU cores to be used at any given time to minimize combined CPU+fan power. Our experimental results show that we are able to achieve 1) energy savings of up to 10% of total energy and 80% of cooling energy when compared to workload consolidation without core capping strategy; 2) cooling energy savings up to 42% when compared to the strategy that randomly assigns jobs to all the servers and cores.

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Cited By

Lin JLin WHuang HLin WLi K(2024)Thermal Modeling and Thermal-Aware Energy Saving Methods for Cloud Data Centers: A ReviewIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33463329:3(571-590)Online publication date: May-2024
https://doi.org/10.1109/TSUSC.2023.3346332
Lin WLin JPeng ZHuang HLin WLi K(2024)A systematic review of green-aware management techniques for sustainable data centerSustainable Computing: Informatics and Systems10.1016/j.suscom.2024.10098942(100989)Online publication date: Apr-2024
https://doi.org/10.1016/j.suscom.2024.100989
Kim HAn JKim Y(2024)Embedded Core-Based Machine Learning Design for Baseboard Management ControllerAdvances in Computer Science and Ubiquitous Computing10.1007/978-981-97-2447-5_60(383-388)Online publication date: 29-Sep-2024
https://doi.org/10.1007/978-981-97-2447-5_60

Index Terms

SpinSmart: Exploring Optimal Server Fan Speeds to Improve Overall System Energy Consumption
1. Hardware
  1. Power and energy
    1. Thermal issues
      1. Temperature control
      2. Temperature optimization

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cover image ACM Other conferences

e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems

June 2020

601 pages

ISBN:9781450380096

DOI:10.1145/3396851

Copyright © 2020 ACM.

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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SIGEnergy: ACM Special Interest Group on Energy Systems and Informatics

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Association for Computing Machinery

New York, NY, United States

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Published: 18 June 2020

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National Science Foundation

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e-Energy '20

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SIGEnergy

e-Energy '20: The Eleventh ACM International Conference on Future Energy Systems

June 22 - 26, 2020

Virtual Event, Australia

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e-Energy '20 Paper Acceptance Rate 77 of 173 submissions, 45%;

Overall Acceptance Rate 160 of 446 submissions, 36%

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Cited By

Lin JLin WHuang HLin WLi K(2024)Thermal Modeling and Thermal-Aware Energy Saving Methods for Cloud Data Centers: A ReviewIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33463329:3(571-590)Online publication date: May-2024
https://doi.org/10.1109/TSUSC.2023.3346332
Lin WLin JPeng ZHuang HLin WLi K(2024)A systematic review of green-aware management techniques for sustainable data centerSustainable Computing: Informatics and Systems10.1016/j.suscom.2024.10098942(100989)Online publication date: Apr-2024
https://doi.org/10.1016/j.suscom.2024.100989
Kim HAn JKim Y(2024)Embedded Core-Based Machine Learning Design for Baseboard Management ControllerAdvances in Computer Science and Ubiquitous Computing10.1007/978-981-97-2447-5_60(383-388)Online publication date: 29-Sep-2024
https://doi.org/10.1007/978-981-97-2447-5_60
Cao ZZhou XHu HWang ZWen Y(2022)Toward a Systematic Survey for Carbon Neutral Data CentersIEEE Communications Surveys & Tutorials10.1109/COMST.2022.316127524:2(895-936)Online publication date: Oct-2023
https://doi.org/10.1109/COMST.2022.3161275

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