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Productive Efficiency of Energy-Aware Data Centers

Author

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  • Damián Fernández-Cerero

    (Department of Computer Languages and Systems, University of Seville, 41012 Sevilla, Spain)

  • Alejandro Fernández-Montes

    (Department of Computer Languages and Systems, University of Seville, 41012 Sevilla, Spain)

  • Francisco Velasco

    (Department of Applied Economy I, University of Seville, 41018 Sevilla, Spain)

Abstract

Information technologies must be made aware of the sustainability of cost reduction. Data centers may reach energy consumption levels comparable to many industrial facilities and small-sized towns. Therefore, innovative and transparent energy policies should be applied to improve energy consumption and deliver the best performance. This paper compares, analyzes and evaluates various energy efficiency policies, which shut down underutilized machines, on an extensive set of data-center environments. Data envelopment analysis (DEA) is then conducted for the detection of the best energy efficiency policy and data-center characterization for each case. This analysis evaluates energy consumption and performance indicators for natural DEA and constant returns to scale (CRS). We identify the best energy policies and scheduling strategies for high and low data-center demands and for medium-sized and large data-centers; moreover, this work enables data-center managers to detect inefficiencies and to implement further corrective actions.

Suggested Citation

  • Damián Fernández-Cerero & Alejandro Fernández-Montes & Francisco Velasco, 2018. "Productive Efficiency of Energy-Aware Data Centers," Energies, MDPI, vol. 11(8), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2053-:d:162499
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    References listed on IDEAS

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    1. Kyungmin Kim & Minseok Song, 2022. "Energy-Saving SSD Cache Management for Video Servers with Heterogeneous HDDs," Energies, MDPI, vol. 15(10), pages 1-16, May.
    2. Anastasiia Grishina & Marta Chinnici & Ah-Lian Kor & Eric Rondeau & Jean-Philippe Georges, 2020. "A Machine Learning Solution for Data Center Thermal Characteristics Analysis," Energies, MDPI, vol. 13(17), pages 1-13, August.

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