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A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty
University of Chittagong, Bangladesh.ORCID iD: 0000-0002-7473-8185
Department of Computer Science and Engineering, International Islamic University Chittagong.
School of Computing, Creative Technologies and Engineering, Leeds Beckett University.
LuleƄ University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
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2017 (English)In: IEEE Transactions on Sustainable Computing, ISSN 2377-3782, Vol. 2, no 2, p. 140-153Article in journal (Refereed) Published
Abstract [en]

A rapidly emerging trend in the IT landscape is the uptake of large-scale datacenters moving storage and data processing to providers located far away from the end-users or locally deployed servers. For these large-scale datacenters, power efficiency is a key metric, with the PUE (Power Usage Effectiveness) and DCiE (Data Centre infrastructure Efficiency) being important examples. This article proposes a belief rule based expert system to predict datacenter PUE under uncertainty. The system has been evaluated using real-world data from a data center in the UK. The results would help planning construction of new datacenters and the redesign of existing datacenters making them more power efficient leading to a more sustainable computing environment. In addition, an optimal learning model for the BRBES demonstrated which has been compared with ANN and Genetic Algorithm; and the results are promising.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 2, no 2, p. 140-153
Keywords [en]
Predictive Modeling, Datacenter, Energy Efficiency, Belief Rule Based Expert System
National Category
Computer Sciences Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-63197DOI: 10.1109/TSUSC.2017.2697768OAI: oai:DiVA.org:ltu-63197DiVA, id: diva2:1092043
Projects
PERCCOMBRBWSN
Funder
Swedish Research Council, 2014-4251EU, Horizon 2020, 2013-0231Available from: 2017-04-29 Created: 2017-04-29 Last updated: 2018-01-13Bibliographically approved

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