Implementing the data center energy productivity metric

Authors:
Landon H. Sego;Andrés Márquez;Andrew Rawson;Tahir Cader;Kevin Fox;William I. Gustafson, Jr.;Christopher J. Mundy
Affiliations:
Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Advanced Micro Devices, Inc.;Hewlett-Packard Company;Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA
Venue:
ACM Journal on Emerging Technologies in Computing Systems (JETC)
Year:
2012

Citing 6
Cited 0

Reevaluating Amdahl's law

Communications of the ACM
Validity of the single processor approach to achieving large scale computing capabilities

AFIPS '67 (Spring) Proceedings of the April 18-20, 1967, spring joint computer conference
The Green500 List: Year one

IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Modeling and evaluating energy-performance efficiency of parallel processing on multicore based power aware systems

IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications

IEEE Transactions on Parallel and Distributed Systems
Review of performance metrics for green data centers: a taxonomy study

The Journal of Supercomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

As data centers proliferate in size and number, the endeavor to improve their energy efficiency and productivity is becoming increasingly important. We discuss the properties of a number of the 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 describe our approach for using DCeP as the principal outcome of a designed experiment using a highly instrumented, high-performance computing 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 (or even maximize) 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.