Energy modeling based on cloud data center

Energy efficiency of cloud data centers has received significant attention recently as data centers often consume significant resources in operation. Most of the existing energy-saving algorithms focus on resource consolidation for energy efficiency. Accurate energy consumption model is the basis for these algorithms. This paper proposes an accurate energy model to predict energy consumption of single machine. In most of the existing cloud computing energy studies, linear models are used to describe the relationship between energy consumption and resource utilizations. However, with the changes in computer architecture, the relationship between energy and resource utilizations may not be linear. In fact, this paper explored a variety of regression analysis methods to estimate the energy consumption accurately while using low computational overhead. Initially, multiple linear regression models are used, but they often do not produce good enough results. Afterwards, this paper chooses three non-linear models and finally settled with the polynomial regression with Lasso as it produces the best estimation. Experimental results show that in adoption of energy model presented in this paper, the prediction accuracy can reach more than 95%.