A stochastic approach to analysis of energy-aware DVS-enabled cloud datacenters

Yun Ni Xia, Meng Chu Zhou, Xin Luo, Shan Chen Pang, Qing Sheng Zhu

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


With the increasing call for green cloud, reducing energy consumption has been an important requirement for cloud resource providers not only to reduce operating costs, but also to improve system reliability. Dynamic voltage scaling (DVS) has been a key technique in exploiting the hardware characteristics of cloud datacenters to save energy by lowering the supply voltage and operating frequency. This paper presents a novel stochastic framework for energy efficiency and performance analysis of DVS-enabled cloud. This framework uses virtual machine request arrival rate, failure rate, repair rate, and service rate of datacenter servers as model inputs. Based on a queuing-network-based analysis, this paper gives analytic solutions of three metrics. The proposed framework can be used to help the design and optimization of energy-aware high performance cloud systems.

Original languageEnglish (US)
Article number6870491
Pages (from-to)73-83
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number1
StatePublished - Jan 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Cloud
  • Dynamic voltage scaling (DVS)
  • Energy efficiency
  • Performance


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