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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.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0003-0244-3561
Vise andre og tillknytning
2017 (engelsk)Inngår i: IEEE Transactions on Sustainable Computing, ISSN 2377-3782, Vol. 2, nr 2, s. 140-153Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
IEEE, 2017. Vol. 2, nr 2, s. 140-153
Emneord [en]
Predictive Modeling, Datacenter, Energy Efficiency, Belief Rule Based Expert System
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-63197DOI: 10.1109/TSUSC.2017.2697768OAI: oai:DiVA.org:ltu-63197DiVA, id: diva2:1092043
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Swedish Research Council, 2014-4251EU, Horizon 2020, 2013-0231Tilgjengelig fra: 2017-04-29 Laget: 2017-04-29 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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Hossain, Mohammad ShahadatAndersson, Karl

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