LAPSE:2018.0575
Published Article
LAPSE:2018.0575
Productive Efficiency of Energy-Aware Data Centers
September 21, 2018
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.
Keywords
cloud computing, data envelopment analysis, efficiency, energy policies, return-to-scale
Suggested Citation
Fernández-Cerero D, Fernández-Montes A, Velasco F. Productive Efficiency of Energy-Aware Data Centers. (2018). LAPSE:2018.0575
Author Affiliations
Fernández-Cerero D: Department of Computer Languages and Systems, University of Seville, 41012 Sevilla, Spain [ORCID]
Fernández-Montes A: Department of Computer Languages and Systems, University of Seville, 41012 Sevilla, Spain [ORCID]
Velasco F: Department of Applied Economy I, University of Seville, 41018 Sevilla, Spain [ORCID]
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Journal Name
Energies
Volume
11
Issue
8
Article Number
E2053
Year
2018
Publication Date
2018-08-08
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en11082053, Publication Type: Journal Article
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LAPSE:2018.0575
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doi:10.3390/en11082053
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Sep 21, 2018
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CC BY 4.0
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[v1] (Original Submission)
Sep 21, 2018
 
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Sep 21, 2018
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Original Submitter
Calvin Tsay
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