research-article

Towards an Adaptive Multi-Power-Source Datacenter

Authors:

Tao LiAuthors Info & Claims

ICS '16: Proceedings of the 2016 International Conference on Supercomputing

Article No.: 11, Pages 1 - 11

https://doi.org/10.1145/2925426.2926276

Published: 01 June 2016 Publication History

Abstract

Big data and cloud computing are accelerating the capacity growth of datacenters all over the world. Their energy costs and environmental issues have pushed datacenter operators to explore and integrate alternative energy sources, such as various renewable energy supplies and energy storage devices. Designing datacenters powered by multi-power supplies in the smart grid environment is becoming a promising trend in the next few decades. However, gracefully provisioning various power sources and efficiently manage them in datacenter is a significant challenge.

In this paper, we explore an unconventional fine-grained power distribution architecture for multi-source powered datacenters. We thoroughly investigate how to deliver and manage multiple power sources from the power generation plant outside of the datacenter to datacenter inside. We then propose a novel Power Switch Network (PSN) for datacenters. PSN is a reconfigurable multi-power-source distribution architecture which enables datacenter to distribute various power sources with a fine-grained manner. Moreover, a tailored machine learning based power sources management framework is proposed for PSN to dynamically select different power sources and optimize user-demanded performance metrics. Compared with the conventional single-switch system, evaluation results show that PSN could improve solar energy utilization by 39.6%, reduce utility power cost by 11.1% and improve workload performance by 33.8%, meanwhile enhancing battery lifetime by 9.3%. We expect that our work could provide valuable guidelines for the emerging multi-power-source datacenter to improve their efficiency, sustainability and economy.

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Cited By

Acun BLee BKazhamiaka FMaeng KGupta UChakkaravarthy MBrooks DWu CAamodt TJerger NSwift M(2023)Carbon Explorer: A Holistic Framework for Designing Carbon Aware DatacentersProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3575693.3575754(118-132)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3575693.3575754
Ramadan AArdebili ALongo AFicarella A(2023)Advancing Resilience in Green Energy Systems: Comprehensive Review of AI-based Data-driven Solutions for Security and Safety2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386721(4002-4010)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386721
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https://doi.org/10.1109/TSUSC.2018.2886382
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cover image ACM Conferences

ICS '16: Proceedings of the 2016 International Conference on Supercomputing

June 2016

547 pages

ISBN:9781450343619

DOI:10.1145/2925426

Copyright © 2016 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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Published: 01 June 2016

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ICS '16: 2016 International Conference on Supercomputing

June 1 - 3, 2016

Istanbul, Turkey

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Cited By

Acun BLee BKazhamiaka FMaeng KGupta UChakkaravarthy MBrooks DWu CAamodt TJerger NSwift M(2023)Carbon Explorer: A Holistic Framework for Designing Carbon Aware DatacentersProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3575693.3575754(118-132)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3575693.3575754
Ramadan AArdebili ALongo AFicarella A(2023)Advancing Resilience in Green Energy Systems: Comprehensive Review of AI-based Data-driven Solutions for Security and Safety2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386721(4002-4010)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386721
Liu LSun HLi CLi TXin JZheng N(2021)Exploring Highly Dependable and Efficient Datacenter Power System Using Hybrid and Hierarchical Energy BuffersIEEE Transactions on Sustainable Computing10.1109/TSUSC.2018.28863826:3(412-426)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TSUSC.2018.2886382
Liu LSun HGao PZheng NLi T(2019)REcache: Efficient Sustainable Energy Management Circuits and Policies for Computing Systems2019 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS.2019.8702515(1-5)Online publication date: May-2019
https://doi.org/10.1109/ISCAS.2019.8702515
Liu WLin JWang Z(2019)USCA: A Unified Systolic Convolution Array Architecture for Accelerating Sparse Neural Network2019 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS.2019.8702132(1-5)Online publication date: May-2019
https://doi.org/10.1109/ISCAS.2019.8702132
Liu LSun HLi CLi TXin JZheng N(2017)Managing Battery Aging for High Energy Availability in Green DatacentersIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2017.271277828:12(3521-3536)Online publication date: 1-Dec-2017
https://doi.org/10.1109/TPDS.2017.2712778
Hu YLi CLiu LLi T(2016)HOPEProceedings of the 2016 International Conference on Supercomputing10.1145/2925426.2926257(1-12)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1145/2925426.2926257

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