Abstract
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.
Highlights
By 2020, ICT industries will account for 3.5% of global carbon emissions, which are predicted to grow by up to 14% by 2040 [1]
AIC and Bayesian Information Criterium (BIC) are used to compare the complexity between fmincon-based Belief Rule-Based Expert System (BRBES) optimization, parameter optimization (PO), and structure optimization (SO) using BRBES-based adaptive Differential Evolution (BRBaDE) for disjunctive and conjunctive Belief Rule Base (BRB), Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS)
It was presented that PO and SO using BRBaDE helped a disjunctive BRB-based BRBES to optimize its learning parameters and the structure of BRB, which, in turn, helped to achieve a more accurate prediction of the Power Usage Effectiveness (PUE) of data centers
Summary
By 2020, ICT industries will account for 3.5% of global carbon emissions, which are predicted to grow by up to 14% by 2040 [1]. Data centers are becoming a predominant ICT industry due to the rapid growth of Big Data applications, the Internet of Things (IoT), 5G, autonomous systems, Blockchain, and artificial intelligence (AI) [2,3]. It has been predicted that demand for data centers will rise exponentially by 2025, which would make data centers consume 33% of the total global ICT electricity consumption [4]. 30% of the total world’s energy and, produce only 5.5% of the global carbon footprint due to the adaptation of efficient energy sources and technologies. Data centers will produce 340 metric megatons of CO2 per year by 2030 [5].
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