Volume 9 Number 2
April 2012
Article Contents
Xiao-Jun Chen, Jing Zhang, Jun-Huai Li and Xiang Li. Resource Reconstruction Algorithms for On-demand Allocation in Virtual Computing Resource Pool. International Journal of Automation and Computing, vol. 9, no. 2, pp. 142-154, 2012. doi: 10.1007/s11633-012-0627-3
Cite as: Xiao-Jun Chen, Jing Zhang, Jun-Huai Li and Xiang Li. Resource Reconstruction Algorithms for On-demand Allocation in Virtual Computing Resource Pool. International Journal of Automation and Computing, vol. 9, no. 2, pp. 142-154, 2012. doi: 10.1007/s11633-012-0627-3

Resource Reconstruction Algorithms for On-demand Allocation in Virtual Computing Resource Pool

  • Received: 2011-03-13
Fund Project:

This work was supported by National High Technology Re-search and Development Program of China (863 Program) (No. 2007AA010305) and the Excellent Doctor Degree Dissertation Fund of Xi0an University of Technology (No. 102-211007).

  • Resource reconstruction algorithms are studied in this paper to solve the problem of resource on-demand allocation and improve the efficiency of resource utilization in virtual computing resource pool. Based on the idea of resource virtualization and the analysis of the resource status transition, the resource allocation process and the necessity of resource reconstruction are presented. Resource reconstruction algorithms are designed to determine the resource reconstruction types, and it is shown that they can achieve the goal of resource on-demand allocation through three methodologies: resource combination, resource split, and resource random adjustment. The effects that the resource users have on the resource reconstruction results, the deviation between resources and requirements, and the uniformity of resource distribution are studied by three experiments. The experiments show that resource reconstruction has a close relationship with resource requirements, but it is not the same with current distribution of resources. The algorithms can complete the resource adjustment with a lower cost and form the logic resources to match the demands of resource users easily.
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Resource Reconstruction Algorithms for On-demand Allocation in Virtual Computing Resource Pool

Fund Project:

This work was supported by National High Technology Re-search and Development Program of China (863 Program) (No. 2007AA010305) and the Excellent Doctor Degree Dissertation Fund of Xi0an University of Technology (No. 102-211007).

Abstract: Resource reconstruction algorithms are studied in this paper to solve the problem of resource on-demand allocation and improve the efficiency of resource utilization in virtual computing resource pool. Based on the idea of resource virtualization and the analysis of the resource status transition, the resource allocation process and the necessity of resource reconstruction are presented. Resource reconstruction algorithms are designed to determine the resource reconstruction types, and it is shown that they can achieve the goal of resource on-demand allocation through three methodologies: resource combination, resource split, and resource random adjustment. The effects that the resource users have on the resource reconstruction results, the deviation between resources and requirements, and the uniformity of resource distribution are studied by three experiments. The experiments show that resource reconstruction has a close relationship with resource requirements, but it is not the same with current distribution of resources. The algorithms can complete the resource adjustment with a lower cost and form the logic resources to match the demands of resource users easily.

Xiao-Jun Chen, Jing Zhang, Jun-Huai Li and Xiang Li. Resource Reconstruction Algorithms for On-demand Allocation in Virtual Computing Resource Pool. International Journal of Automation and Computing, vol. 9, no. 2, pp. 142-154, 2012. doi: 10.1007/s11633-012-0627-3
Citation: Xiao-Jun Chen, Jing Zhang, Jun-Huai Li and Xiang Li. Resource Reconstruction Algorithms for On-demand Allocation in Virtual Computing Resource Pool. International Journal of Automation and Computing, vol. 9, no. 2, pp. 142-154, 2012. doi: 10.1007/s11633-012-0627-3
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