Resource management framework for collaborative computing systems over multiple virtual machines

  • Authors:

  • Xiaojun Chen;Jing Zhang;Junhuai Li

  • Affiliations:

  • School of computer science and engineering, Xi'an University of Technology, Xi'an, China and State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China;School of computer science and engineering, Xi'an University of Technology, Xi'an, China and State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China;School of computer science and engineering, Xi'an University of Technology, Xi'an, China and State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China

  • Venue:
  • Service Oriented Computing and Applications
  • Year:
  • 2011

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Abstract

A resource management framework for collaborative computing systems over multiple virtual machines (CCSMVM) is presented to increase the performance of computing systems by improving the resource utilization, which has constructed a scalable computing environment for resource on-demand utilization. We design a resource management framework based on the advantages of some components in grid computing platform, virtualized platform and cloud computing platform to reduce computing systems overheads and maintain workloads balancing with the supporting of virtual appliance, Xen API, applications virtualization and so on. The content of collaborate computing, the basis of virtualized resource management and some key technologies including resource planning, resource allocation, resource adjustment and resource release and collaborative computing scheduling are designed in detail. A prototype is designed, and some experiments have verified the correctness and feasibility of our prototype. System evaluations show that the time in resource allocation and resource release is proportional to the quantity of virtual machines, but not the time in the virtual machines migrations. CCSMVM has higher CPU utilization and better performance than other systems, such as Eucalyptus 2.0, Globus4.0, et al. It is concluded that CCSMVM can accelerate the execution of systems by improving average CPU utilization from the results of comparative analysis with other systems, so it is better than others. Our study on resource management framework has some significance to the optimization of the performance in virtual computing systems.