Analyzing the power consumption behavior of a large scale data center

Analyzing the power consumption behavior of a large scale data center The aim of this paper is to illustrate the use of application and system level logs to better understand scientific data center behavior and energy-spending. Analyzing a data center log of 900 nodes (Sandy Bridge and Haswell), we study node power consumption and describe approaches to estimate and forecast it. Our results include methods to cluster nodes based on different vmstat and RAPL measurements as well as Gaussian and GAM models for estimating the plug power consumption. We also analyze failed jobs and find that non-successfully terminated jobs consume around 40% of computing time. While the actual numbers are likely to vary in different data centers at different times, the purpose of the paper is to share ideas of what can be found by statistical and machine learning analysis of large amount of log data. Keywords RAPL · Energy modeling · Energy efficiency · Data center log analysis 1 Introduction Intel’s Running Average Power Limit (RAPL) is one such power measurement tool, which has been useful in According to a recent report by Lawrence Berkeley National power measurement and modeling research [8,11,17]. RAPL Laboratory [16] the data centers in United States consumed reports the real time power consumption of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computer Science - Research and Development Springer Journals

Analyzing the power consumption behavior of a large scale data center

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Computer Science, general; Computer Hardware; Computer Systems Organization and Communication Networks; Software Engineering/Programming and Operating Systems; Data Structures, Cryptology and Information Theory; Theory of Computation
ISSN
1865-2034
eISSN
1865-2042
D.O.I.
10.1007/s00450-018-0394-7
Publisher site
See Article on Publisher Site

Abstract

The aim of this paper is to illustrate the use of application and system level logs to better understand scientific data center behavior and energy-spending. Analyzing a data center log of 900 nodes (Sandy Bridge and Haswell), we study node power consumption and describe approaches to estimate and forecast it. Our results include methods to cluster nodes based on different vmstat and RAPL measurements as well as Gaussian and GAM models for estimating the plug power consumption. We also analyze failed jobs and find that non-successfully terminated jobs consume around 40% of computing time. While the actual numbers are likely to vary in different data centers at different times, the purpose of the paper is to share ideas of what can be found by statistical and machine learning analysis of large amount of log data. Keywords RAPL · Energy modeling · Energy efficiency · Data center log analysis 1 Introduction Intel’s Running Average Power Limit (RAPL) is one such power measurement tool, which has been useful in According to a recent report by Lawrence Berkeley National power measurement and modeling research [8,11,17]. RAPL Laboratory [16] the data centers in United States consumed reports the real time power consumption of

Journal

Computer Science - Research and DevelopmentSpringer Journals

Published: May 29, 2018

References

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