A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty

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 Effect...

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Bibliographic Details
Published inIEEE transactions on sustainable computing Vol. 2; no. 2; pp. 140 - 153
Main Authors Hossain, Mohammad Shahadat, Rahaman, Saifur, Ah-Lian Kor, Andersson, Karl, Pattinson, Colin
Format Journal Article
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Belief Rule Based Expert System
Best practices
Cooling
Datacenter
Distribuerade datorsystem
Energy consumption
Energy Efficiency
Expert systems
Pervasive Mobile Computing
Predictive Modeling
Servers
Temperature measurement
Virtualization
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Summary: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.
ISSN:2377-3782
2377-3790
2377-3782
2377-3790
DOI:10.1109/TSUSC.2017.2697768