Authors: Dr. Vikas Mongia
Abstract: Cloud computing has become a fundamental technology for delivering scalable and on-demand computing resources. However, the rapid growth of cloud data centers has signifi-cantly increased energy consumption, operational costs, and carbon emissions. Virtual Ma-chine (VM) consolidation is a widely adopted technique for improving resource utilization and reducing energy consumption by migrating VMs from underutilized servers and switch-ing idle hosts into low-power states. Multi-Criteria Decision-Making (MCDM) techniques have emerged as effective approaches for VM consolidation because they can simultane-ously consider multiple conflicting criteria such as CPU utilization, memory usage, network bandwidth, energy consumption, migration cost, and Quality of Service (QoS). This paper critically analyzes MCDM-based energy-efficient VM consolidation approaches, discusses their strengths and limitations, identifies current challenges, explores emerging opportuni-ties, and highlights future research directions. The study reveals that while MCDM methods improve decision accuracy and energy efficiency, issues such as scalability, dynamic work-load adaptation, uncertainty management, and integration with artificial intelligence remain significant research challenges.
DOI: http://doi.org/10.5281/zenodo.20947666
