Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Optimal power allocation in server farms

Published: 15 June 2009 Publication History

Abstract

Server farms today consume more than 1.5% of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to distribute available power among servers in a server farm so as to get maximum performance.
By giving more power to a server, one can get higher server frequency (speed). Hence it is commonly believed that, for a given power budget, performance can be maximized by operating servers at their highest power levels. However, it is also conceivable that one might prefer to run servers at their lowest power levels, which allows more servers to be turned on for a given power budget. To fully understand the effect of power allocation on performance in a server farm with a fixed power budget, we introduce a queueing theoretic model, which allows us to predict the optimal power allocation in a variety of scenarios. Results are verified via extensive experiments on an IBM BladeCenter.
We find that the optimal power allocation varies for different scenarios. In particular, it is not always optimal to run servers at their maximum power levels. There are scenarios where it might be optimal to run servers at their lowest power levels or at some intermediate power levels. Our analysis shows that the optimal power allocation is non-obvious and depends on many factors such as the power-to-frequency relationship in the processors, the arrival rate of jobs, the maximum server frequency, the lowest attainable server frequency and the server farm configuration. Furthermore, our theoretical model allows us to explore more general settings than we can implement, including arbitrarily large server farms and different power-to-frequency curves. Importantly, we show that the optimal power allocation can significantly improve server farm performance, by a factor of typically 1.4 and as much as a factor of 5 in some cases.

References

[1]
Lesswatts.org: Race to idle. http://www.lesswatts.org/projects/applications-power-management/race-to-idle.php.
[2]
Intel: Nehalem. http://intel.wingateweb.com/US08/published/sessions/NGMS001/SF08_NGMS001_100t.pdf.
[3]
U.S. Environmental Protection Agency. Epa report on server and data center energy efficiency. 2007.
[4]
National Electrical Contractors Association. Data centers -- meeting today's demand. 2007.
[5]
Jeffrey S. Chase, Darrell C. Anderson, Prachi N. Thakar, and Amin M. Vahdat. Managing energy and server resources in hosting centers. In Proceedings of the Eighteenth ACM Symposium on Operating Systems Principles (SOSP), pages 103--116, 2001.
[6]
Intel Corp. Intel Core2 Duo Mobile Processor Datasheet: Table 20. http://download.intel.com/design/mobile/datashts/32012001.pdf, 2008.
[7]
M. Elnozahy, M. Kistler, and R. Rajamony. Energy conservation policies for web servers. In USITS, 2003.
[8]
Xiaobo Fan, Wolf-Dietrich Weber, and Luiz Andre Barroso. Power provisioning for a warehouse-sized computer. pages 13--23, 2007.
[9]
Wes Felter, Karthick Rajamani, Tom Keller, and Cosmin Rusu. A performance-conserving approach for reducing peak power consumption in server systems. In ICS '05: Proceedings of the 19th annual International Conference on Supercomputing, pages 293--302, New York, NY, USA, 2005. ACM.
[10]
Mark E. Femal and Vincent W. Freeh. Boosting Data Center Performance Through Non-Uniform Power Allocation. In ICAC '05: Proceedings of the Second International Conference on Automatic Computing, pages 250--261, Washington, DC, 2005.
[11]
M.S. Floyd, S. Ghiasi, T.W. Keller, K. Rajamani, F.L. Rawson, J.C. Rubio, and M. S. Ware. System Power Management Support in the IBM POWER6 Microprocessor. IBM Journal of Research and Development, 51:733--746, 2007.
[12]
Anshul Gandhi, Mor Harcol-Balter, Rajarshi Das, and Charles Lefurgy. Optimal power allocation in server farms. Technical Report CMU-CS-09-113, 2009.
[13]
Intel Corp. Intel Math Kernel Library 10.0 -- LINPACK. http://www.intel.com/cd/software/products/asmo-na/eng/266857.htm.
[14]
Raj Jain. phThe Art of Computer Systems Performance Analysis: techniques for experimental design, measurement, simulation, and modeling. pages 563--567. Wiley, 1991.
[15]
Radim Kolar. Web bench. http://home.tiscali.cz:8080/cz210552/webbench.html.
[16]
Kleinrock L. Queueing Systems, Volume 2. Wiley-Interscience, New York, 1976.
[17]
Charles Lefurgy, Xiaorui Wang, and Malcolm Ware. Power capping: a prelude to power shifting. Cluster Computing, November 2007.
[18]
J.D. McCalpin. Stream: Sustainable memory bandwidth in high performance computers. http://www.cs.virginia.edu/stream/.
[19]
David Mosberger and Tai Jin. httperf--A Tool for Measuring Web Server Performance. ACM Sigmetrics: Performance Evaluation Review, 26:31--37, 1998.
[20]
Vivek Pandey, W. Jiang, Y. Zhou, and R. Bianchini. DMA-Aware Memory Energy Management. HPCA '06: The 12th International Symposium on High-Performance Computer Architecture, pages 133--144, 11-15 Feb. 2006.
[21]
Ramya Raghavendra, Parthasarathy Ranganathan, Vanish Talwar, Zhikui Wang, and Xiaoyun Zhu. No "Power" Struggles: Coordinated Multi-Level Power Management for the Data Center. In ASPLOS XIII: Proceedings of the 13th international conference on Architectural support for programming languages and operating systems, pages 48--59, 2008.
[22]
K. Rajamani, H. Hanson, J.C. Rubio, S. Ghiasi, and F.L. Rawson. Online power and performance estimation for dynamic power management. Research Report RC-24007, July 2006.
[23]
Salvatore Sanfilippo. WBox HTTP testing tool (Version 4). http://www.hping.org/wbox/, 2007.
[24]
X Wang and M Chen. Cluster-level Feedback Power Control for Performance Optimization. 14th IEEE International Symposium on High-Performance Computer Architecture (HPCA 2008), February 2008.
[25]
Zhikui Wang, Xiaoyun Zhu, Cliff McCarthy, Partha Ranganathan, and Vanish Talwar. Feedback Control Algorithms for Power Management of Servers. In Third International Workshop on Feedback Control Implementation and Design in Computing Systems and Networks (FeBid), Annapolis, MD, June 2008.

Cited By

View all
  • (2025)An Integrated Charging and Computation Scheduling of Electric Vehicles in Edge Computing SystemIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.349597326:1(916-934)Online publication date: 1-Jan-2025
  • (2025)Job assignment in machine learning inference systems with accuracy constraintsPerformance Evaluation10.1016/j.peva.2024.102463167(102463)Online publication date: Mar-2025
  • (2024)Energy and carbon-aware distributed machine learning tasks scheduling scheme for the multi-renewable energy-based edge-cloud continuumScience and Technology for Energy Transition10.2516/stet/202407679(82)Online publication date: 15-Oct-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM SIGMETRICS Performance Evaluation Review
ACM SIGMETRICS Performance Evaluation Review  Volume 37, Issue 1
SIGMETRICS '09
June 2009
320 pages
ISSN:0163-5999
DOI:10.1145/2492101
Issue’s Table of Contents
  • cover image ACM Conferences
    SIGMETRICS '09: Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
    June 2009
    336 pages
    ISBN:9781605585116
    DOI:10.1145/1555349
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 June 2009
Published in SIGMETRICS Volume 37, Issue 1

Check for updates

Author Tags

  1. data center
  2. power management
  3. power-to-frequency
  4. response time
  5. server farm

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)68
  • Downloads (Last 6 weeks)3
Reflects downloads up to 22 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)An Integrated Charging and Computation Scheduling of Electric Vehicles in Edge Computing SystemIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.349597326:1(916-934)Online publication date: 1-Jan-2025
  • (2025)Job assignment in machine learning inference systems with accuracy constraintsPerformance Evaluation10.1016/j.peva.2024.102463167(102463)Online publication date: Mar-2025
  • (2024)Energy and carbon-aware distributed machine learning tasks scheduling scheme for the multi-renewable energy-based edge-cloud continuumScience and Technology for Energy Transition10.2516/stet/202407679(82)Online publication date: 15-Oct-2024
  • (2024)Prediction Model for Physical Machine Selection for Virtual Machine PlacementProceedings of the 2024 Sixteenth International Conference on Contemporary Computing10.1145/3675888.3676069(343-348)Online publication date: 8-Aug-2024
  • (2023)EESaver: Saving Energy Dynamically for Green Multi-Access Edge ComputingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.327761934:7(2155-2166)Online publication date: 1-Jul-2023
  • (2023)Erlang loss systems with shortest idle server first service discipline: Maintenance considerationsIISE Transactions10.1080/24725854.2022.214990655:10(1008-1021)Online publication date: 4-Jan-2023
  • (2023)Research and Technologies for next-generation high-temperature data centers – State-of-the-arts and future perspectivesRenewable and Sustainable Energy Reviews10.1016/j.rser.2022.112991171(112991)Online publication date: Jan-2023
  • (2023)A multi-objective cloud energy optimizer algorithm for federated environmentsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.12.007174:C(81-99)Online publication date: 1-Apr-2023
  • (2022)Cloud Servers: Resource Optimization Using Different Energy Saving TechniquesSensors10.3390/s2221838422:21(8384)Online publication date: 1-Nov-2022
  • (2022)Optimal Routing to Parallel Servers With Unknown Utilities—Multi-Armed Bandit With QueuesIEEE/ACM Transactions on Networking10.1109/TNET.2022.322713631:5(1997-2012)Online publication date: 8-Dec-2022
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media