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Utility-function-driven energy-efficient cooling in data centers

Authors:

Jeffrey O. Kephart,

Jonathan Lenchner,

Hendrik HamannAuthors Info & Claims

ICAC '10: Proceedings of the 7th international conference on Autonomic computing

Pages 61 - 70

https://doi.org/10.1145/1809049.1809058

Published: 07 June 2010 Publication History

Abstract

The sharp rise in energy usage in data centers, fueled by increased IT workload and high server density, and coupled with a concomitant increase in the cost and volatility of the energy supply, have triggered urgent calls to improve data center energy efficiency. In response, researchers have developed energy-aware IT systems that slow or shut down servers without sacrificing performance objectives. Several authors have shown that utility functions are a natural and advantageous framework for self-management of servers to joint power and performance objectives. We demonstrate that utility functions are a similarly powerful framework for flexibly managing entire data centers to joint power and temperature objectives. After showing how utility functions can capture a wide range of objectives and tradeoffs that an operator might wish to specify, we illustrate the resulting range in behavior and energy savings using experimental results from a real data center that is cooled by two computer room air-conditioning (CRAC) units equipped with variable-speed fan drives.

References

[1]

ASHRAE Publication. 2008 ASHRAE environment guidelines for datacom equipment: Expanding the recommended environmental envelope. Technical report, American Society of Heating, Regrigerating and Air-Conditioning Engineers, Inc., 2008.

[2]

R. Ayoub and T. Rosing. Cool and save: cooling aware dynamic workload scheduling in multi-socket cpu systems. In Proceedings of ASPDAC 2010, 2010.

Digital Library

[3]

C. E. Bash, C. D. Patel, and R. K. Sharma. Dynamic thermal management of air cooled data centers. In Proc. of the 10th Int'l Conf. on Thermal and Thermomechanical Phenomena in Electronics Systems (ITHERM), pages 445--452, San Diego, CA, May 2006.

[4]

T. Boucher, D. Auslander, C. Bash, C. Federspiel, and C. Patel. Viability of dynamic cooling control in a data center environment. In Proc. of the 9th Int'l Conf. on Thermal and Thermomechanical Phenomena in Electronics Systems (ITHERM), pages 445--452, Las Vegas, NV, August 2004.

[5]

R. G. Brown and J. Hughes. Skimp on server room air conditioning? At your peril. http://www.openxtra.co.uk/articles/skimp-server-room-ac, 2009.

[6]

J. S. Chase, D. C. Anderson, P. N. Thakar, A. N. Vahdat, and R. P. Doyle. Managing energy and server resources in hosting centers. In Proc. 18th Symposium on Operating Systems Principles (SOSP), 2001.

Digital Library

[7]

D. Chess, G. Pacifici, M. Spreitzer, M. Steinder, A. Tantawi, and I. Whalley. Experience with collaborating managers: Node group manager and provisioning manager. In Proc. 2nd Int'l Conference on Autonomic Computing, 2005.

Digital Library

[8]

G. Cole. Estimating drive reliability in desktop computers and consumer electronics systems. Technical report, Seagate TP-338.1, 2000.

[9]

Gartner Inc. Gartner Says 50 Percent of Data Centers Will Have Insufficient Power and Cooling Capacity by 2008. Press Release, November 29, 2006.

[10]

H. Hamann, T. van Kessel, M. Iyengar, J.-Y. Chung, W. Hirt, M. A. Schappert, A. Claassen, J. M. Cook, W. Min, Y. Amemiya, V. Lopez, J. A. Lacey, and M. O'Boyle. Uncovering energy efficiency opportunities in data centers. IBM Journal of Research and Development, 53(3):10:1--10:12, 2009.

Digital Library

[11]

H. F. Hamann, M. Schappert, M. Iyengar, T. van Kessel, and A. Claassen. Methods and techniques for measuring and improving data center best practices. In Proceedings of 11th Intersociety Conference on Thermomechanical Phenomena in Electronic Systems, pages 1146--1152, May 2008.

[12]

J. O. Kephart, H. Chan, R. Das, D. W. Levine, G. Tesauro, F. L. R. III, and C. Lefurgy. Coordinating multiple autonomic managers to achieve specified power-performance tradeoffs. In Proc. 4th Int'l Conf. on Autonomic Computing, pages 24--33, 2007.

Digital Library

[13]

J. O. Kephart and D. M. Chess. The vision of autonomic computing. Computer, 36(1):41--52, 2003.

Digital Library

[14]

J. O. Kephart and R. Das. Achieving self-management via utility functions. IEEE Internet Computing, 11:40--48, 2007.

Digital Library

[15]

B. Khargharia, S. Hariri, and M. S. Yousif. Autonomic power and performance management for computing systems. In Proc. Third Int'l Conference on Autonomic Computing, pages 145--154, 2006.

Digital Library

[16]

J. G. Koomey. Estimating total power consumption by servers in the U.S. and the world. http://enterprise.amd.com/Downloads/svrpwrusecompletefinal.pdf, 2007.

[17]

V. Kumar, B. Cooper, and K. Schwan. Distributed stream management using utility-driven self-adaptive middleware. In Proc. 2nd Int'l Conference on Autonomic Computing, pages 3--14, 2005.

Digital Library

[18]

D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang. Power and performance management of virtualized computing environments via lookahead control. In Proc. Fifth Int'l Conference on Autonomic Computing, pages 3--12, 2008.

Digital Library

[19]

J. Moore, J. Chase, and P. Ranganathan. Making scheduling "cool": Temperature-aware workload placement in data centers. In Proc. 2005 USENIX Annual Technical Conference (USENIX '05), 2005.

Digital Library

[20]

R. Nathuji, C. Isci, and E. Gorbatov. Exploiting platform heterogeneity for power efficient data centers. In Proc. Fourth Int'l Conference on Autonomic Computing, pages 5--14, Washington, DC, USA, 2007. IEEE Computer Society.

Digital Library

[21]

L. Parolini, B. Sinopoli, and B. H. Krogh. Reducing data center energy consumption via coordinated cooling and load management. HotPower S08: Workshop on Power Aware Computing and Systems, December 2008.

Digital Library

[22]

C. Patel, C. Bash, and C. Belady. Computational fluid dynamics modeling of high compute density data centers to assure system inlet air specifications. Proc. ASME Int'l Electronic Packaging Technical Conference and Exhibition, 2001.

[23]

C. Patel, C. Bash, R. Sharma, A. Beitelmal, and R. Friedrich. Smart cooling of datacenters. Proc. IPACK'03 -- The PacificRim/ASME Int'l Electronics Packaging Tech. Conference and Exhibition, July 2003.

[24]

E. Pinheiro, W.-D. Weber, and L. A. Barroso. Failure trends in a large disk drive population. In Proc. of the 5th USENIX Conference on File and Storage Technologies (FAST07), pages 17--29, 2007.

Digital Library

[25]

N. Rasmussen. Electrical efficiency modeling of data centers, document 113 version 1, 2006.

[26]

R. Sharma, C. Bash, C. Patel, R. Friedrich, and J. Chase. Balance of power: Dynamic thermal management for internet data centers. IEEE Internet Computing, 9(1):42--49, January 2005.

Digital Library

[27]

H. W. Stanford III. HVAC Water Chillers and Cooling towers: Fundamentals, Application, and Operation. Dekker Mechanical Engineering, 2003.

[28]

G. Tesauro, R. Das, W. E. Walsh, and J. O. Kephart. Utility-function-driven resource allocation in autonomic systems. In 2nd Int'l Conference on Autonomic Computing, 2005.

Digital Library

[29]

W. E. Walsh, G. Tesauro, J. O. Kephart, and R. Das. Utility functions in autonomic systems. In First Int'l Conference on Autonomic Computing, 2004.

Digital Library

Cited By

Wan JDuan YGui XLiu CLi LMa Z(2023)SafeCool: Safe and Energy-Efficient Cooling Management in Data Centers With Model-Based Reinforcement LearningIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32345457:6(1621-1635)Online publication date: Dec-2023
https://doi.org/10.1109/TETCI.2023.3234545
Goudarzi PHosseinpour MGoudarzi RLloret J(2022)Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement LearningFuture Internet10.3390/fi1412036814:12(368)Online publication date: 8-Dec-2022
https://doi.org/10.3390/fi14120368
Sun JWang SWang JTolbert L(2022)Dynamic Model and Converter-Based Emulator of a Data Center Power Distribution SystemIEEE Transactions on Power Electronics10.1109/TPEL.2022.314635437:7(8420-8432)Online publication date: Jul-2022
https://doi.org/10.1109/TPEL.2022.3146354
Show More Cited By

Index Terms

Utility-function-driven energy-efficient cooling in data centers
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. System management
        Centralization / decentralization
        Quality assurance
        Technology audits

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Information & Contributors

Information

Published In

ICAC '10: Proceedings of the 7th international conference on Autonomic computing

June 2010

246 pages

ISBN:9781450300742

DOI:10.1145/1809049

General Chair:
Manish Parashar
Rutgers University, USA
,
Program Chairs:
Renato Figueiredo
University of Florida, USA
,
Emre Kıcıman
Microsoft Research, USA

Copyright © 2010 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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SIGARCH: ACM Special Interest Group on Computer Architecture

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University of Arizona: University of Arizona

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2010

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ICAC '10: 7th International Conference on Autonomic Computing

June 7 - 11, 2010

DC, Washington, USA

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Cited By

Wan JDuan YGui XLiu CLi LMa Z(2023)SafeCool: Safe and Energy-Efficient Cooling Management in Data Centers With Model-Based Reinforcement LearningIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32345457:6(1621-1635)Online publication date: Dec-2023
https://doi.org/10.1109/TETCI.2023.3234545
Goudarzi PHosseinpour MGoudarzi RLloret J(2022)Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement LearningFuture Internet10.3390/fi1412036814:12(368)Online publication date: 8-Dec-2022
https://doi.org/10.3390/fi14120368
Sun JWang SWang JTolbert L(2022)Dynamic Model and Converter-Based Emulator of a Data Center Power Distribution SystemIEEE Transactions on Power Electronics10.1109/TPEL.2022.314635437:7(8420-8432)Online publication date: Jul-2022
https://doi.org/10.1109/TPEL.2022.3146354
Conficoni CBartolini ATilli ACavazzoni CBenini L(2021)HPC Cooling: A Flexible Modeling Tool for Effective Design and ManagementIEEE Transactions on Sustainable Computing10.1109/TSUSC.2018.28095746:3(441-455)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TSUSC.2018.2809574
Sun JWang SWang JTolbert L(2021)Development of a Converter-Based Data Center Power Emulator2021 IEEE Applied Power Electronics Conference and Exposition (APEC)10.1109/APEC42165.2021.9487043(126-133)Online publication date: 14-Jun-2021
https://doi.org/10.1109/APEC42165.2021.9487043
Ahmed KBollen MAlvarez M(2021)A Review of Data Centers Energy Consumption and Reliability ModelingIEEE Access10.1109/ACCESS.2021.31250929(152536-152563)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3125092
Zhang QMeng ZHong XZhan YLiu JDong JBai TNiu JDeen M(2021)A survey on data center cooling systemsJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2021.102253119:COnline publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.sysarc.2021.102253
Polonelli TBrunelli DBartolini ABenini L(2019)A LoRaWAN Wireless Sensor Network for Data Center Temperature MonitoringApplications in Electronics Pervading Industry, Environment and Society10.1007/978-3-030-11973-7_20(169-177)Online publication date: 11-May-2019
https://doi.org/10.1007/978-3-030-11973-7_20
Stamatescu IPloix SFagarasan IStamatescu G(2018)Data Center Server Energy Consumption Optimization Algorithm2018 26th Mediterranean Conference on Control and Automation (MED)10.1109/MED.2018.8442890(813-818)Online publication date: Jun-2018
https://doi.org/10.1109/MED.2018.8442890
Mosa APaton N(2016)Optimizing virtual machine placement for energy and SLA in clouds using utility functionsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-016-0067-75:1(1-17)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1186/s13677-016-0067-7
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