Power consumption estimation in data centers using machine learning techniques

Abstract

Large data centers consume large amounts of electricity. Estimating the energy consumption in a data center can be of great importance to data centers administrators in order to know the energy-consuming tasks and take actions for reducing the total energy consumption. Smart workflow mechanisms can be built to reduce the energy consumption of data centers significantly. In this paper, we are investigating the factors that affect the energy consumption of scientific applications in data centers. We also use eight machine learning methods to estimate the energy consumption of multi-threaded scientific applications. Extensive computational results on a computer with 20 cores show that the CPU usage is the most important parameter in the power consumed by an application. However, better results can be obtained when the CPU utilization is combined with other parameters. We generate various regression models that predict the energy consumption of an application with an average accuracy of 99%. Simpler models with one and two parameters can achieve comparable accuracy with more complex models. We also compare various machine learning methods for their ability to obtain accurate predictions using as few parameters as possible.