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Optimal machine placement based on improved genetic algorithm in cloud computing

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Abstract

In cloud computing, virtual machine placement (VMP) is an important process that identifies the most appropriate physical machine to host the virtual machines (VMs). Nevertheless, determining how to place VMs within the data center to provide high availability and good performance is a difficult challenge for cloud providers. In this paper, with the goal of optimizing the availability and the energy consumption of the cloud data center, an improved genetic algorithm (I-GA) is proposed to solve VMP problem. This new algorithm presents a virtual hierarchy architecture model to combine with the genetic algorithm. The model is able to achieve a near-optimal solution in resolving the availability and energy consumption concerns by innovating the initial population generation step of the I-GA. Finite element analysis is applied as background and CloudSim is used as the experiment simulation. The simulated results demonstrate the significant improvement of the data center’s energy efficiency and the successful maintenance of its high availability. The results are also highly competitive compared to the benchmark results of other VMP algorithms.

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Acknowledgements

This work is supported by National Natural Science Foundations of China (No. 61976193), the Science and Technology Key Research Planning Project of Zhejiang Province, China (No. 2021C03136), and Zhejiang Natural Science Foundation, China (No. LY19F020034).

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Jiawei Lu, Wei Zhao, Haotian Zhu, Jie Li, Zhenbo Cheng & Gang Xiao

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  1. Jiawei Lu
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  2. Wei Zhao
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Correspondence to Gang Xiao.

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Lu, J., Zhao, W., Zhu, H. et al. Optimal machine placement based on improved genetic algorithm in cloud computing. J Supercomput 78, 3448–3476 (2022). https://doi.org/10.1007/s11227-021-03953-8

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