Framework for Resource Utilization-Based Dynamic Migration of VMs in OpenStack Clouds 


Vol. 42,  No. 8, pp. 1562-1572, Aug.  2017
10.7840/kics.2017.42.8.1562


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  Abstract

Virtualization technology provides several vital features in cloud computing. One such feature is live migration of virtual machines (VMs). It is a process of transferring the complete state of a VM between physical hosts without interruption of any service. This capability is being widely used for the purpose of reconfiguration and fault tolerance, energy efficiency, load balancing, and system maintenance. Most of existing studies make decision on which defective VMs should be transferred to which suitable physical machines (PMs) in terms of resource utilization. However, there are very few implementations available for dynamic VM migration. To that end, in this paper, we propose a framework for dynamic VM migration in OpenStack clouds. The framework implements the proposed overload detection, VM selection, and VM allocation algorithms for dynamic VM migration in OpenStack. Furthermore, with the help of experiments, we show that the proposed algorithms outperform the algorithms that are considered for the purpose of evaluation.

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  Cite this article

[IEEE Style]

A. Muhammad and W. Song, "Framework for Resource Utilization-Based Dynamic Migration of VMs in OpenStack Clouds," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 8, pp. 1562-1572, 2017. DOI: 10.7840/kics.2017.42.8.1562.

[ACM Style]

Afaq Muhammad and Wang-Cheol Song. 2017. Framework for Resource Utilization-Based Dynamic Migration of VMs in OpenStack Clouds. The Journal of Korean Institute of Communications and Information Sciences, 42, 8, (2017), 1562-1572. DOI: 10.7840/kics.2017.42.8.1562.

[KICS Style]

Afaq Muhammad and Wang-Cheol Song, "Framework for Resource Utilization-Based Dynamic Migration of VMs in OpenStack Clouds," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 8, pp. 1562-1572, 8. 2017. (https://doi.org/10.7840/kics.2017.42.8.1562)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880