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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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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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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DOI: https://doi.org/10.1007/s42979-024-03215-3