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Intelligent thermal management in M2DC system

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Emmanuel SenechalAuthors Info & Claims

e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems

Pages 309 - 315

https://doi.org/10.1145/3077839.3084029

Published: 16 May 2017 Publication History

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Abstract

Thermal management and cooling are essential parts that have significant influence on the energy efficiency of data centers as cost of cooling can exceed 50% of the whole energy data center energy consumption. Optimized thermal management of data center also affects reliability and availability of a data center due to prevention of creation of the so called hot spots. In this paper, we present a model and optimisation method for thermal management a server platform, developed within M2DC project, equipped with a high number of heterogeneous hardware. We also show how the management of the individual servers and chassis influences efficiency of the whole data center. First, we present how this affects the commonly used PUE metric and how this approach can be misleading in evaluation of the data center effectiveness. Secondly, we show how intelligent fan management may influence energy used for cooling, change of IT systems energy consumption and the overall gain.

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Cited By

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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
Fornaciari WAgosta GAtienza DBrandolese CCammoun LCremona LCilardo AFarres AFlich JHernandez CKulchewski MLibutti SMartínez JMassari GOleksiak APupykina AReghenzani FTornero RZanella MZapater MZoni DMudge TPnevmatikatos D(2018)Reliable power and time-constraints-aware predictive management of heterogeneous exascale systemsProceedings of the 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation10.1145/3229631.3239368(187-194)Online publication date: 15-Jul-2018
https://dl.acm.org/doi/10.1145/3229631.3239368
Oleksiak ACiesielczyk TKierzynka MPiatek W(2018)Minimising energy costs of data centers using high dense heterogeneous systems and intelligent resource managementProceedings of the Ninth International Conference on Future Energy Systems10.1145/3208903.3213777(499-505)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3208903.3213777

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Abstract
With the exponential increase in cooling costs of large-scale data centers, thermal management must be adequately addressed. Recent trends have discovered one of the critical reasons behind the temperature rise turns out to be heat re-circulation ...
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e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems

May 2017

388 pages

ISBN:9781450350365

DOI:10.1145/3077839

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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Published: 16 May 2017

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Horizon 2020

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e-Energy '17

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SIGCOMM

e-Energy '17: The Eighth International Conference on Future Energy Systems

May 16 - 19, 2017

Shatin, Hong Kong

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Overall Acceptance Rate 160 of 446 submissions, 36%

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The 16th ACM International Conference on Future and Sustainable Energy Systems

June 17 - 20, 2025

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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
Fornaciari WAgosta GAtienza DBrandolese CCammoun LCremona LCilardo AFarres AFlich JHernandez CKulchewski MLibutti SMartínez JMassari GOleksiak APupykina AReghenzani FTornero RZanella MZapater MZoni DMudge TPnevmatikatos D(2018)Reliable power and time-constraints-aware predictive management of heterogeneous exascale systemsProceedings of the 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation10.1145/3229631.3239368(187-194)Online publication date: 15-Jul-2018
https://dl.acm.org/doi/10.1145/3229631.3239368
Oleksiak ACiesielczyk TKierzynka MPiatek W(2018)Minimising energy costs of data centers using high dense heterogeneous systems and intelligent resource managementProceedings of the Ninth International Conference on Future Energy Systems10.1145/3208903.3213777(499-505)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3208903.3213777

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Modeling Impact of Power- and Thermal-Aware Fans Management on Data Center Energy Consumption

Minimising energy costs of data centers using high dense heterogeneous systems and intelligent resource management

Towards thermal-aware Hadoop clusters

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Modeling Impact of Power- and Thermal-Aware Fans Management on Data Center Energy Consumption

Minimising energy costs of data centers using high dense heterogeneous systems and intelligent resource management

Towards thermal-aware Hadoop clusters

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