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Resource reconstruction algorithms for on-demand allocation in virtual computing resource pool

Authors:

Xiang LiAuthors Info & Claims

International Journal of Automation and Computing, Volume 9, Issue 2

Pages 142 - 154

https://doi.org/10.1007/s11633-012-0627-3

Published: 01 April 2012 Publication History

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.

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Cited By

Akbaripour HHoushmand M(2018)Service composition and optimal selection in cloud manufacturing: landscape analysis and optimization by a hybrid imperialist competitive and local search algorithmNeural Computing and Applications10.1007/s00521-018-3721-932:15(10873-10894)Online publication date: 17-Sep-2018
https://dl.acm.org/doi/10.1007/s00521-018-3721-9
Liang QWang YZhang Y(2013)Resource Virtualization Model Using Hybrid-graph Representation and Converging Algorithm for Cloud ComputingInternational Journal of Automation and Computing10.1007/s11633-013-0758-110:6(597-606)Online publication date: 1-Dec-2013
https://dl.acm.org/doi/10.1007/s11633-013-0758-1

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Published In

cover image International Journal of Automation and Computing

International Journal of Automation and Computing Volume 9, Issue 2

April 2012

112 pages

ISSN:1476-8186

Issue’s Table of Contents

Copyright © Copyright © 2012 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 April 2012

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Cited By

Akbaripour HHoushmand M(2018)Service composition and optimal selection in cloud manufacturing: landscape analysis and optimization by a hybrid imperialist competitive and local search algorithmNeural Computing and Applications10.1007/s00521-018-3721-932:15(10873-10894)Online publication date: 17-Sep-2018
https://dl.acm.org/doi/10.1007/s00521-018-3721-9
Liang QWang YZhang Y(2013)Resource Virtualization Model Using Hybrid-graph Representation and Converging Algorithm for Cloud ComputingInternational Journal of Automation and Computing10.1007/s11633-013-0758-110:6(597-606)Online publication date: 1-Dec-2013
https://dl.acm.org/doi/10.1007/s11633-013-0758-1

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