Data center management, the foundation of contemporary cloud computing, has made energy saving a top priority. Among other difficulties, the placement of virtual machines (VMs) has a major impact on data center resource and energy usage. Assigning VMs to physical machines (PMs) is a challenging NP-hard problem, especially in large-scale infrastructures where it is computationally infeasible to find an ideal solution. To solve the VM placement problem, the proposed study formulates it as a restricted optimization problem with the goal of preserving performance while lowering energy consumption. The explosive growth of data centers has resulted in higher energy consumption and higher carbon dioxide (CO2) emissions, which are a primary cause of climate change. Globally, governments, energy-focused organizations, and business executives have taken notice of this expanding environmental impact. This study provides a comprehensive analysis of data center energy consumption patterns, environmental effects, and trends in energy consumption. It also suggests doable energy-saving measures, such as installing energy-efficient infrastructure and upgrading air conditioning systems. The paper also presents an improved genetic algorithm–based method that is tailored for energy-conscious VM deployment, successfully striking a balance between computing economy and convergence accuracy. The suggested solution highly increased data centers' energy efficiency by incorporating this strategy within a profile-based virtual resource management model. Additionally, policy suggestions for sustainable data center management are delineated, advancing the more general objective of ecologically conscious cloud computing. Experimental results demonstrate that the proposed method achieves up to 50% reduction in execution time, 48% fewer generations for convergence, and approximately 7% reduction in energy consumption compared to traditional first fit decreasing (FFD) methods. Additionally, the integration of task classification improves energy efficiency by up to 15% and reduces the number of active PMs. These findings highlight the effectiveness of the proposed framework in enabling scalable, energy-efficient, and environmentally sustainable cloud data center management.
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