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Capacity Management of Hyperscale Data Centers Using Predictive Modelling

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Pervasive and Mobile Computing Laboratory, Luleå University of Technology, 93187 Skellefteå, Sweden
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Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh
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School of Computing, Creative Technologies and Engineering Leeds Beckett University, Leeds LS1 3HE, UK
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Author to whom correspondence should be addressed.
Energies 2019, 12(18), 3438; https://doi.org/10.3390/en12183438
Received: 31 July 2019 / Revised: 28 August 2019 / Accepted: 3 September 2019 / Published: 6 September 2019
Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient. View Full-Text
Keywords: learning; differential evolution; belief rule-based expert systems; predictive modelling; data center learning; differential evolution; belief rule-based expert systems; predictive modelling; data center
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Islam, R.U.; Ruci, X.; Hossain, M.S.; Andersson, K.; Kor, A.-L. Capacity Management of Hyperscale Data Centers Using Predictive Modelling. Energies 2019, 12, 3438.

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