Deep machine learning-based power usage effectiveness prediction for sustainable cloud infrastructures

Abstract

The expansion of online services, the advent of big data, and the development of Internet of Things (IoT) technology have led to an exponential growth in the number of data centers (DC). Within this new digital systems, DCs are the keystone. This poses major environmental problems through high energy consumption. The energy consumption growth of the DC’s systems are expected to account for 3.2% of global carbon emissions by 2025. This remarkable growth in the energy consumption of DCs represents today a major challenge for energy efficiency. To meet these challenges, Power Usage Effectiveness (PUE) can be used to measure and optimize DC energy efficiency. Thus, accurate PUE prediction is challenging. Moreover, the non-linearity of the different DCs subsystems interactions and their inter-dependencies make the prediction of PUE more difficult since it is affected by complex factors such as workload variation, in addition to weather conditions and humidity, etc.In this paper, we present three machine learning models, MLP, Resilient Backpropagation-based DNN and Attention-based LSTM, to predict PUE values. The performance of these models has been evaluated using two datasets gathered from two case studies of real DCs. For both DC cases, experimental results showed that the attention-based LSTM model outperforms resilient backpropagation-based DNN and MLP models in terms of prediction errors and PUE.