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Optimizing Cloud Resource Utilization with ANN-Based VM Placement and Prediction

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Abstract

Cloud computing practitioners and the advancement of next-generation data centers face substantial challenges in terms of energy-related costs and environmental sustainability. To optimize this virtual machine (VM) placement is a better technique for minimizing energy while maximizing resource utilization. VM placement techniques often require knowledge of both current and future energy consumption, making it challenging to accurately predict the future demand of cloud applications. A VM placement strategy (MBFD) was employed, and its outcomes were utilized to train the proposed prediction model. The proposed prediction model considers both power consumption and CPU utilization of the physical machines (PMs). The results of the prediction model indicate a 10.96% minimized power consumption and a 6.9% improvement in service level agreement (SLA) violation.

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Data Availability

Used the simulator CLOUDSIM which as its own PLANETLAB data which is used in this research.

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Funding

This research, titled ‘Optimizing Cloud Resource Utilization with ANN-Based VM Placement and Prediction,’ was conducted without external funding or financial support. The study was carried out as part of the author’s independent research efforts, and no specific grants, scholarships, or funding from any external sources were utilized in the execution of this research project.

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All authors of this manuscript, titled ‘Optimizing Cloud Resource Utilization with ANN-Based VM Placement and Prediction,’ have contributed equally to this study. Each author participated in the design, implementation, analysis, and manuscript preparation, and all authors have reviewed and approved the final version for publication.

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Correspondence to Rashmi Sindhu.

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I, the author of this manuscript titled ‘Optimizing Cloud Resource Utilization with ANN-Based VM Placement and Prediction,’ declare that I have no competing interests to disclose. This research and its publication were conducted without any financial, personal, or professional interests that could be perceived as influencing the research or its publication.

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Informed consent was obtained from all human participants involved in this study. Participants were provided with a comprehensive explanation of the study’s objectives, procedures, and potential risks. They voluntarily provided written informed consent before participating in any research activities. The study adhered to ethical guidelines and was conducted with full respect for the rights and well-being of the participants.

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Sindhu, R., Siwach, V., Sehrawat, H. et al. Optimizing Cloud Resource Utilization with ANN-Based VM Placement and Prediction. SN COMPUT. SCI. 5, 907 (2024). https://doi.org/10.1007/s42979-024-03215-3

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