Stochastic modeling and quality evaluation of infrastructure-as-a-service clouds

Yunni Xia, Mengchu Zhou, Xin Luo, Qingsheng Zhu, Jia Li, Yu Huang

Research output: Contribution to journalArticlepeer-review

111 Scopus citations


Cloud computing is a recently developed new technology for complex systems with massive service sharing, which is different from the resource sharing of the grid computing systems. In a cloud environment, service requests from users go through numerous provider-specific steps from the instant it is submitted to when the requested service is fully delivered. Quality modeling and analysis of clouds are not easy tasks because of the complexity of the automated provisioning mechanism and dynamically changing cloud environment. This work proposes an analytical model-based approach for quality evaluation of Infrastructure-as-a-Service cloud by considering expected request completion time, rejection probability, and system overhead rate as key quality metrics. It also features with the modeling of different warm-up and cool-down strategies of machines and the ability to identify the optimal balance between system overhead and performance. To validate the correctness of the proposed model, we obtain simulative quality-of-service (QoS) data and conduct a confidence interval analysis. The result can be used to help design and optimize industrial cloud computing systems.

Original languageEnglish (US)
Article number6595652
Pages (from-to)162-170
Number of pages9
JournalIEEE Transactions on Automation Science and Engineering
Issue number1
StatePublished - Jan 1 2015

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


  • Cloud computing
  • infrastructure-as-a-service (IaaS)
  • modeling and analysis
  • quality-of-service (QoS)


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