Entropy‐Aware <scp>VM</scp> Selection and Placement in Cloud Data Centers

ABSTRACT The increase in popularity and demand for cloud services has caused a huge growth of cloud data centers, and this has caused the challenge of energy management in data centers. Virtual Machine (VM) consolidation is a critical process aimed at optimizing resource utilization and minimizing energy usage. VM consolidation with the turnoff of underloaded hosts and reducing the load of overloaded hosts establishes a balance between energy consumption and SLA violations. In fact, the consolidation process includes three sub‐problems: determining overloaded and underloaded hosts, VM selection in overloaded hosts, and finding a new destination for VMs that will be migrated (VM placement). This paper introduces an entropy‐based approach to VM selection and placement to improve efficiency in cloud data centers. Entropy is a quantifiable characteristic often linked to disorder, randomness, or unpredictability. By leveraging entropy as a measure of workload distribution and uncertainty, the proposed method effectively predicts future resource demands, enabling informed decisions that enhance energy efficiency and reduce SLA violations. A key advantage of this approach is the significant reduction in the number of VM migrations, which decreases overhead and minimizes potential service disruptions. Experimental results demonstrate that our entropy‐based method outperforms the VM consolidation process in terms of energy consumption, SLA compliance, and system stability. The findings suggest that this approach offers a more sustainable and cost‐effective solution for managing cloud resources, contributing to the development of efficient and reliable cloud computing environments.