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A Virtual Machine Placement Strategy Based on Virtual Machine Selection and Integration

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1 College of Information Science, Zhejiang shuren university, Hangzhou, 310015, China
2 School of Beihua Institute of Aerospace Technology, University of Langfang, Langfang, 065201, China

* Corresponding Author:Denghui Zhang. Email: email

Journal on Internet of Things 2021, 3(4), 149-157. https://doi.org/10.32604/jiot.2021.016936

Abstract

Cloud data centers face the largest energy consumption. In order to save energy consumption in cloud data centers, cloud service providers adopt a virtual machine migration strategy. In this paper, we propose an efficient virtual machine placement strategy (VMP-SI) based on virtual machine selection and integration. Our proposed VMP-SI strategy divides the migration process into three phases: physical host state detection, virtual machine selection and virtual machine placement. The local regression robust (LRR) algorithm and minimum migration time (MMT) policy are individual used in the first and section phase, respectively. Then we design a virtual machine migration strategy that integrates the process of virtual machine selection and placement, which can ensure a satisfactory utilization efficiency of the hardware resources of the active physical host. Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.

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APA Style
Zhang, D., Yin, G. (2021). A virtual machine placement strategy based on virtual machine selection and integration. Journal on Internet of Things, 3(4), 149-157. https://doi.org/10.32604/jiot.2021.016936
Vancouver Style
Zhang D, Yin G. A virtual machine placement strategy based on virtual machine selection and integration. J Internet Things . 2021;3(4):149-157 https://doi.org/10.32604/jiot.2021.016936
IEEE Style
D. Zhang and G. Yin, “A Virtual Machine Placement Strategy Based on Virtual Machine Selection and Integration,” J. Internet Things , vol. 3, no. 4, pp. 149-157, 2021. https://doi.org/10.32604/jiot.2021.016936



cc Copyright © 2021 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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