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The Scheduling Strategy of Virtual Machine Migration Based on the Gray Forecasting Model

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Frontiers in Algorithmics (FAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9711))

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Abstract

In stage of Infrastructures Providers (IP) provides cloud service for Service Providers (SP), in order to maximize the profits of IP, saving energy and reducing consumption is taken into consideration. As a new application mode of virtualization technology, virtual machine migration is of great practical meaning. We present a forecast model based on gray and credibility ant colony scheduling algorithm for virtual machine migration scheduling policy. The model can estimate the future utilization of a period of virtual machine CPU node. In determining whether the virtual machine should be moved out, the dual-threshold mechanism is set up to avoid frequent migration shocks caused by the transient oscillation of CPU resource utilization. Positioning probability is defined to improve the convergence speed when target node is selected. The experiments show that the algorithm can effectively avoid the frequent migration of virtual machine, which is a result of the shock caused by the change in CPU utilization, reduce energy consumption, and improve IP gains.

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Acknowledgements

Supported by Natural Science Foundation of Shandong Province (ZR2013FM031); Supported by State Key Lab of High Performance Server and Storage (2014HSSA03).

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Authors and Affiliations

  1. School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, China

    He Hong & Cao Boyan

Authors
  1. He Hong
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  2. Cao Boyan
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Corresponding author

Correspondence to He Hong .

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Editors and Affiliations

  1. Shandong University, Jinan, China

    Daming Zhu

  2. MS EC-31, University of Texas at Dallas, Richardson, Texas, USA

    Sergey Bereg

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© 2016 Springer International Publishing Switzerland

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Hong, H., Boyan, C. (2016). The Scheduling Strategy of Virtual Machine Migration Based on the Gray Forecasting Model. In: Zhu, D., Bereg, S. (eds) Frontiers in Algorithmics. FAW 2016. Lecture Notes in Computer Science(), vol 9711. Springer, Cham. https://doi.org/10.1007/978-3-319-39817-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-39817-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39816-7

  • Online ISBN: 978-3-319-39817-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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