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Journal of Electrical and Computer Engineering
Volume 2015, Article ID 143071, 9 pages
http://dx.doi.org/10.1155/2015/143071
Research Article

Regression Cloud Models and Their Applications in Energy Consumption of Data Center

Handan College, Handan, Hebei 056005, China

Received 13 August 2015; Accepted 27 September 2015

Academic Editor: James Nightingale

Copyright © 2015 Yanshuang Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As cloud data center consumes more and more energy, both researchers and engineers aim to minimize energy consumption while keeping its services available. A good energy model can reflect the relationships between running tasks and the energy consumed by hardware and can be further used to schedule tasks for saving energy. In this paper, we analyzed linear and nonlinear regression energy model based on performance counters and system utilization and proposed a support vector regression energy model. For performance counters, we gave a general linear regression framework and compared three linear regression models. For system utilization, we compared our support vector regression model with linear regression and three nonlinear regression models. The experiments show that linear regression model is good enough to model performance counters, nonlinear regression is better than linear regression model for modeling system utilization, and support vector regression model is better than polynomial and exponential regression models.