Application-Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center

Jing Bi, Haitao Yuan, Wei Tan, Meng Chu Zhou, Yushun Fan, Jia Zhang, Jianqiang Li

Research output: Contribution to journalArticlepeer-review

76 Scopus citations

Abstract

A key factor of win-win cloud economy is how to trade off between the application performance from customers and the profit of cloud providers. Current researches on cloud resource allocation do not sufficiently address the issues of minimizing energy cost and maximizing revenue for various applications running in virtualized cloud data centers (VCDCs). This paper presents a new approach to optimize the profit of VCDC based on the service-level agreements (SLAs) between service providers and customers. A precise model of the external and internal request arrival rates is proposed for virtual machines at different service classes. An analytic probabilistic model is then developed for non-steady VCDC states. In addition, a smart controller is developed for fine-grained resource provisioning and sharing among multiple applications. Furthermore, a novel dynamic hybrid metaheuristic algorithm is developed for the formulated profit maximization problem, based on simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. The advantage of the proposed approach is validated with trace-driven simulations.

Original languageEnglish (US)
Pages (from-to)1172-1184
Number of pages13
JournalIEEE Transactions on Automation Science and Engineering
Volume14
Issue number2
DOIs
StatePublished - Apr 2017

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Data center
  • dynamic resource provisioning
  • heuristic algorithm
  • optimization

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