Virtualization-based autonomic resource management for multi-tier Web applications in shared data center

  • Authors:

  • Xiaoying Wang;Zhihui Du;Yinong Chen;Sanli Li

  • Affiliations:

  • Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;Computer Science and Engineering Department, Arizona State University, Tempe, AZ 85287, USA;Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2008

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Abstract

As large data centers emerge, which host multiple Web applications, it is critical to isolate different application environments for security reasons and to provision shared resources effectively and efficiently to meet different service quality targets at minimum operational cost. To address this problem, we developed a novel architecture of resource management framework for multi-tier applications based on virtualization mechanisms. Key techniques presented in this paper include (1) establishment of the analytic performance model which employs probabilistic analysis and overload management to deal with non-equilibrium states; (2) a general formulation of the resource management problem which can be solved by incorporating both deterministic and stochastic optimizing algorithms; (3) deployment of virtual servers to partition resource at a much finer level; and (4) investigation of the impact of the failure rate to examine the effect of application isolation. Simulation experiments comparing three resource allocation schemes demonstrate the advantage of our dynamic approach in providing differentiated service qualities, preserving QoS levels in failure scenarios and also improving the overall performance while reducing the resource usage cost.