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Virtual Machine Placement Using Adam White Shark Optimization Algorithm in Cloud Computing

SN Computer Science volume 5, Article number: 21 (2024) Cite this article

Abstract

The increasing demand for virtual machine (VM) request is caused due to the increasing number of users. Hence, the VM placement is considered as a critical task for attaining effective resource handling in cloud data centers (DCs). In general, the VM placement procedure deploys the set of VMs onto the set of physical machines (PMs) depending on specific criteria. In this research, the optimal solution for VM placement is computed by hybrid optimization with fitness parameters. Here, the fitness function is computed by combining several objectives including load, power, placement time and migration cost. In addition, VM placement is based on several system factors such as central processing unit (CPU), memory, and bandwidth, million instructions per second (MIPS) and processing elements. Besides, the hybrid optimization technique devised for performing the VM migration in this research is Adam white shark optimization-based VM placement (AWSO_VMP), which is formulated by modifying the white shark optimization (WSO) with the Adam optimizer. Thus, the performance of AWSO_VMP is assessed using load, power consumption and cost of migration, and the attained values of corresponding metrics are 0.133, 0.225 W and 0.116.

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Data Availability

The labeled datasets used to support the findings of this study can be obtained from the corresponding author upon request.

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Acknowledgements

The authors acknowledge the support from REVA University for the facilities provided to carry out the research.

Funding

No funding received for this research.

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Authors and Affiliations

  1. School of Computer Science and Engineering, REVA University, Bengaluru, India

    S. Supreeth

  2. Department of Electronics and Communication Engineering, S J C Institute of Technology, Chikkaballapur, India

    S. Bhargavi

  3. Healthcare IT Leader, SecureKloud Technologies Inc, Chicago, USA

    Rakesh Margam

  4. Government Engineering College, Hassan, Karnataka, India

    H. Annaiah

  5. Nitte Meenakshi Institute of Technology, Bengaluru, India

    Rajesh Nandalike

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  1. S. Supreeth
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  3. Rakesh Margam
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  4. H. Annaiah
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  5. Rajesh Nandalike
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Contributions

SS and SB identified initial problem identification, algorithm write-up, analysis, drafting of the manuscript, and simulation. RM was responsible for the literature survey and helped in the initial review process. AH was responsible for the complexity analysis of the research, evaluation of the research work. RN was responsible for the figures, final formatting and applied for the journal. All the authors worked together to implement and evaluate the integrated system, and approved the final version of the paper.

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Correspondence to S. Supreeth.

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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

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Supreeth, S., Bhargavi, S., Margam, R. et al. Virtual Machine Placement Using Adam White Shark Optimization Algorithm in Cloud Computing. SN COMPUT. SCI. 5, 21 (2024). https://doi.org/10.1007/s42979-023-02341-8

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