Optimized Architecture for Efficient VM Allocation and Migration in Cloud Environments
DOI:
https://doi.org/10.22399/ijcesen.1466Keywords:
Cloud Computing, Decentralized Management, Energy Efficiency, Load Balancing, Machine Learning, Predictive Analytics, Sustainable ComputingAbstract
In today’s IT landscape, the increasing reliance on cloud computing has made effective virtual machine (VM) allocation and migration essential for maximizing resource utilization and lowering operational costs. This study delves into architectural strategies that enhance VM management within cloud environments. It explores methods such as predictive models that harness machine learning to forecast resource needs, optimize load distribution, and reduce downtime during live migrations. By analyzing the connections between resource provisioning, workload patterns, and migration tactics, the research highlights key performance indicators for assessing migration success. Moreover, it reviews modern frameworks and technologies aimed at reducing energy consumption while boosting overall system performance. The proposed architecture is designed to streamline VM management and promote sustainable resource allocation, addressing the twin challenges of efficiency and environmental impact in cloud computing. Ultimately, the insights presented here are intended to help organizations adopt more flexible and effective cloud infrastructure solutions.
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