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International Conference on Computational Science and Its Applications

ICCSA 2019: Computational Science and Its Applications – ICCSA 2019 pp 225–240Cite as

Modeling Energy Consumption Based on Resource Utilization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11619))

Abstract

Power management is an expensive and important issue for large computational infrastructures such as datacenters, large clusters, and computational grids. However, measuring energy consumption of scalable systems may be impractical due to both cost and complexity for deploying power metering devices on a large number of machines. In this paper, we propose the use of information about resource utilization (e.g. processor, memory, disk operations, and network traffic) as proxies for estimating power consumption. We employ machine learning techniques to estimate power consumption using such information which are provided by common operating systems. Experiments with linear regression, regression tree, and multilayer perceptron on data from different hardware resulted into a model with 99.94% of accuracy and 6.32 watts of error in the best case.

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Notes

  1. 1.

    Models were implemented in R (using RSNNS) and source code are available under the GNU General Public License version 3 at https://github.com/lucasvenez/ecm along with the employed dataset.

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Acknowledgement

Authors thank CAPES and RNP for partially supporting this research. Hermes Senger thanks CNPq (Contract Number 305032/2015-1) and FAPESP (Process numbers 2018/00452-2, and 2018/22979-2) for their support.

Author information

Authors and Affiliations

  1. Federal Institute of São Paulo (IFSP), Caraguatatuba, Brazil

    Lucas Venezian Povoa

  2. Aeronautics Institute of Technology (ITA), São José dos Campos, Brazil

    Lucas Venezian Povoa & Cesar Marcondes

  3. Federal University of São Carlos (UFSCar), São Carlos, Brazil

    Hermes Senger

Authors
  1. Lucas Venezian Povoa
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  2. Cesar Marcondes
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  3. Hermes Senger
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Corresponding author

Correspondence to Lucas Venezian Povoa .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Povoa, L.V., Marcondes, C., Senger, H. (2019). Modeling Energy Consumption Based on Resource Utilization. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11619. Springer, Cham. https://doi.org/10.1007/978-3-030-24289-3_18

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  • DOI: https://doi.org/10.1007/978-3-030-24289-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24288-6

  • Online ISBN: 978-3-030-24289-3

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