Title :
A system for online power prediction in virtualized environments using gaussian mixture models
Author :
Dhiman, Gaurav ; Mihic, Kresimir ; Rosing, Tajana
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of California, San Diego, La Jolla, CA, USA
Abstract :
In this paper we present a system for online power prediction in vir-tualized environments. It is based on Gaussian mixture models that use architectural metrics of the physical and virtual machines (VM) collected dynamically by our system to predict both the physical machine and per VM level power consumption. A real implementation of our system shows that it can achieve average prediction error of less than 10%, outperforming state of the art regression based approaches at negligible runtime overhead.
Keywords :
Gaussian processes; computer architecture; power aware computing; virtual machines; Gaussian mixture models; architectural metrics; online power prediction; physical machine; virtual machine; virtualized environment; Cooling; Costs; Energy consumption; Energy management; Power system modeling; Predictive models; Runtime; Virtual machining; Virtual manufacturing; Voice mail; Gaussian Mixture Models; Power; Regression; Virtualization; Workload Characterization;
Conference_Titel :
Design Automation Conference (DAC), 2010 47th ACM/IEEE
Conference_Location :
Anaheim, CA
Print_ISBN :
978-1-4244-6677-1