Paper Info
Title
Stochastic modeling of dynamic right-sizing for energy-efficiency in cloud data centers
Abstract
Large data centers are usually built to support increasing computational and data storage demand of growing global business and industry, which consume an enormous amount of energy, at a huge cost to both business and the environment. However, much of that energy is wasted to maintain excess service capacity during periods of low load. In this paper, we investigate the problem of \"right-sizing\" data center for energy-efficiency through virtualization which allows consolidation of workloads into smaller number of servers while dynamically powering off the idle ones. In view of the dynamic nature of data centers, we propose a stochastic model based on Queueing theory to capture the main characteristics. Solving this model, we notice that there exists a tradeoff between the energy consumption and performance. We hereby develop a BFGS based algorithm to optimize the tradeoff by searching for the optimal system parameter values for the data center operators to \"right-size\" the data centers. We implement our Stochastic Right-sizing Model (SRM) and deploy it in the real-world cloud data center. Experiments with two real-world workload traces show that SRM can significantly reduce the energy consumption while maintaining high performance. A Stochastic Right-sizing Model(SRM) based on Queueing theory is proposed.A BFGS based algorithm is proposed to achieve energy-efficiency.SRM is implemented with open-source cloud platform OpenStack.
Year
DOI
Venue
2015
10.1016/j.future.2014.09.012
Future Generation Computer Systems
Keywords
Field
DocType
Cloud computing,Data center,Virtualization,Energy-efficiency,Queueing theory
Virtualization,Efficient energy use,Computer science,Server,Real-time computing,Queueing theory,Stochastic modelling,Energy consumption,Data center,Cloud computing,Distributed computing
Journal
Volume
Issue
ISSN
48
C
0167-739X
Citations 
PageRank 
References 
4
0.40
26
Authors
7
Name
Order
Citations
PageRank
Dian Shen1105.22
Junzhou Luo21257153.97
Fang Dong320235.44
Fei Xiang4192.76
Wei Wang5819.54
Guoqing Jin673.58
Weidong Li713613.50