Implementing the Data Center Energy Productivity Metric in a High-Performance Computing Data Center

  • Landon H. Sego
  • Andrés Márquez
  • Andrew Rawson
  • Tahir Cader
  • Kevin Fox
  • William I. GustafsonJr.
  • Christopher J. Mundy


As data centers proliferate in size and number, improving energy efficiency and productivity has become an economic and environmental imperative. Making these improvements requires metrics that are robust, interpretable, and practical. We examine the properties of a number of proposed metrics of energy efficiency and productivity. In particular, we focus on the Data Center Energy Productivity (DCeP) metric, which is the ratio of useful work produced by the data center to the energy consumed performing that work. We investigated DCeP as the principal outcome of a designed experiment using a highly instrumented, high-performance computing (HPC) data center. We found that DCeP was successful in clearly distinguishing different operational states in the data center, thereby validating its utility as a metric for identifying configurations of hardware and software that would improve energy productivity. We also discuss some of the challenges and benefits associated with implementing the DCeP metric, and we examine the efficacy of the metric in making comparisons within a data center and among data centers.


Energy Efficiency Data Center Load Balance Cool Tower Pacific Northwest National Laboratory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by the U.S. Department of Energy under DE-Award Numbers 47128, 55430, and SC0005365.


  1. 1.
    Amdahl GM (1967) Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the spring joint computer conference, AFIPS ’67 (Spring), Atlantic City, 18–20 Apr 1967. Association for Computing Machinery, New York, pp 483–485Google Scholar
  2. 2.
    ASHRAE (2011) ASHRAE TC 9.9: 2011 thermal guidelines for data processing environments-expanded data center classes and usage guidance. Technical report, American Society of Heating, Refrigerating and Air- Conditioning Engineers.
  3. 3.
    Baer M, Mundy CJ, Chang TM, Tao FM, Dang LX (2010) Interpreting vibrational sum-frequency spectra of sulfur dioxide at the air/water interface: a comprehensive molecular dynamics study. J Phys Chem B 114(21):7245–7249CrossRefGoogle Scholar
  4. 4.
    Berger JO (1985) Statistical decision theory and Bayesian analysis, 2nd edn. Springer, New YorkCrossRefzbMATHGoogle Scholar
  5. 5.
    CP2K (2011) CP2K developers home page.
  6. 6.
    Dean A, Voss D (1999) Design and analysis of experiments. Springer, New YorkCrossRefzbMATHGoogle Scholar
  7. 7.
    Edwards W, Miles R, von Winterfeldt D (2007) Advances in decision analysis: from foundations to applications. Cambridge University Press, Cambridge/New YorkCrossRefGoogle Scholar
  8. 8.
    EPA (2007) Report to Congress on server and data center energy efficiency, public law 109-431. Technical report, United States Environmental Protection Agency.
  9. 9.
    EPA (2010) ENERGY STAR computer server specification Draft 1 Version 2.0. Technical report, United States Environmental Protection Agency.
  10. 10.
    Feng W, Scogland T (2009) The Green500 list: year one. In: Proceedings of the 2009 IEEE international symposium on parallel & distributed processing, IPDPS ’09, Rome. pp 1–7Google Scholar
  11. 11.
    Ge R, Feng X, Cameron KW (2009) Modeling and evaluating energy-performance efficiency of parallel processing on multicore based power aware systems. In: Proceedings of the 2009 IEEE international symposium on parallel & distributed processing, IPDPS ’09, Rome. pp 1–8Google Scholar
  12. 12.
    Ge R, Feng X, Song S, Chang HC, Li D, Cameron K (2010) Powerpack: energy profiling and analysis of high-performance systems and applications. IEEE Trans Parallel Distrib Syst 21(5):658–671CrossRefGoogle Scholar
  13. 13.
    Goicoechea A, Hansen DR, Duckstein L (1982) Multiobjective decision analysis with engineering and business applications. Wiley, New YorkGoogle Scholar
  14. 14.
    Greenhill D (2005) SWaP: space, watts, and power. Technical report, Sun Microsystems.
  15. 15.
    Gustafson JL (1988) Reevaluating Amdahl’s law. Commun ACM 31(5):532–533CrossRefGoogle Scholar
  16. 16.
    Hewlett-Packard Company: HP Data Center Smart Grid.
  17. 17.
    Hewlett-Packard Company: HP Insight Control.
  18. 18.
  19. 19.
    JCGM (2008) International vocabulary of metrology – basic and general concepts and associated terms (VIM). Joint Committee for Guides in Metrology.
  20. 20.
    Kamil S, Shalf J, Strohmaier E (2008) Power efficiency in high performance computing. In: IEEE international symposium on parallel and distributed processing, IPDPS ’08, Miami, pp 1–8Google Scholar
  21. 21.
    Keeney RL, Raiffa H (1976) Decisions with multiple objectives: preferences and value tradeoffs. Wiley, New YorkGoogle Scholar
  22. 22.
    Ma J, Fan Z, Huang L (1999) A subjective and objective integrated approach to determine attribute weights. Eur J Oper Res 112:397–404CrossRefzbMATHGoogle Scholar
  23. 23.
    R Development Core Team (2011) R: a language and environment for statistical computing.
  24. 24.
    Sego LH, Márquez A, Rawson A, Cader T, Fox K, Gustafson WI Jr, Mundy CJ (2012) Implementing the data center energy productivity metric. ACM J Emerg Technol Comput Syst 8(4):1–22 (Article 30)Google Scholar
  25. 25.
    Sisk DR, Khaleel MA, Márquez A, Hatley D, Cader T, Schmidt R (2009) Real-time data center energy efficiency at Pacific Northwest National Laboratory. ASHRAE Trans 115(Part I): 242–253Google Scholar
  26. 26.
    Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang XY, Wang W, Powers JG (2008) A description of the advanced research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR, National Center for Atmospheric Research.
  27. 27.
    Standard Performance Evaluation Corporation (2008) SPECpower_ssj2008 Benchmark.
  28. 28.
    Stanley JR, Brill KG, Koomey J (2007) Four metrics define data center “greenness”. Technical report, The Uptime Institute.Google Scholar
  29. 29.
    TGG (2008) A framework for data center energy productivity. Technical report 13, The Green Grid.
  30. 30.
    TGG (2008) Green grid data center power efficiency metrics: PUE and DCIE. Technical report 6, The Green Grid.
  31. 31.
    TGG (2009) Proxy proposals for measuring data center productivity. Technical report 17, The Green Grid.
  32. 32.
    The Green 500:
  33. 33.
    VandeVondele J, Krack M, Mohamed F, Parrinello M, Chassaing T, Hutter J (2005) Quickstep: fast and accurate density functional calculations using a mixed gaussian and plane waves approach. Comput Phys Commun 167(2):103–128CrossRefGoogle Scholar
  34. 34.
    Wang L, Khan SU (2011) Review of performance metrics for green data centers: a taxonomy study. J Supercomput 1–18.Google Scholar
  35. 35.
    Wang YM, Luo Y (2010) Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Math Comput Model 51(1–2):1–12MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Wang YM, Parkan C (2005) Multiple attribute decision making based on fuzzy preference information on alternatives: ranking and weighting. Fuzzy Sets Syst 153(3):331–346MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Landon H. Sego
    • 1
  • Andrés Márquez
    • 1
  • Andrew Rawson
    • 2
  • Tahir Cader
    • 3
  • Kevin Fox
    • 1
  • William I. GustafsonJr.
    • 1
  • Christopher J. Mundy
    • 1
  1. 1.Pacific Northwest National Laboratory (PNNL)RichlandUSA
  2. 2.Advanced Micro Devices, Inc.SunnyvaleUSA
  3. 3.Hewlett-Packard CompanyPalo AltoUSA

Personalised recommendations