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

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Design Technologies for Green and Sustainable Computing Systems

Abstract

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.

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Notes

  1. 1.

    This work is based on an earlier work: Implementing the Data Center Energy Productivity Metric, in Journal on Emerging Technologies in Computing Systems, (Volume 8, Issue 4, Article 30, October 2012) ©ACM 2012. http://doi.acm.org/10.1145/2367736.2367741

  2. 2.

    Due to nonlinearity, we do not view productivity as an intensive property, as proposed by Kamil et al. [20]. However, we agree with their bias discussion: productivity can be biased by scale.

  3. 3.

    Our use of the terms accuracy, error, and precision are consistent with those used by the International Vocabulary of Metrology [19].

  4. 4.

    Figures 4.1–4.3 illustrate these fluctuations.

  5. 5.

    Similar concepts have been proposed by other organizations such as the Uptime Institute.

  6. 6.

    Provided the definition of useful work and the scope of energy consumption is consistent for all systems under comparison.

  7. 7.

    This capability did not actually exist in the ESDC. Instead, the water temperature for liquid cooling was regulated via a separate heat exchanger meant to simulate the cooling that would be provided by cooling towers.

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Acknowledgements

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

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Authors and Affiliations

  1. Pacific Northwest National Laboratory (PNNL), Richland, USA

    Landon H. Sego, Andrés Márquez, Kevin Fox, William I. Gustafson Jr. & Christopher J. Mundy

  2. Advanced Micro Devices, Inc., Sunnyvale, USA

    Andrew Rawson

  3. Hewlett-Packard Company, Palo Alto, USA

    Tahir Cader

Authors
  1. Landon H. Sego
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  2. Andrés Márquez
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  3. Andrew Rawson
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  4. Tahir Cader
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  5. Kevin Fox
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  6. William I. Gustafson Jr.
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  7. Christopher J. Mundy
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Corresponding author

Correspondence to Landon H. Sego .

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Editors and Affiliations

  1. School of EECS, Washington State University, Pullman, USA

    Partha Pratim Pande

  2. Department of Computer Engineering, Rochester Institute of Technology, Rochester, USA

    Amlan Ganguly

  3. ECE, Duke University, Durham, USA

    Krishnendu Chakrabarty

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Sego, L.H. et al. (2013). Implementing the Data Center Energy Productivity Metric in a High-Performance Computing Data Center. In: Pande, P., Ganguly, A., Chakrabarty, K. (eds) Design Technologies for Green and Sustainable Computing Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4975-1_4

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  • DOI: https://doi.org/10.1007/978-1-4614-4975-1_4

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