Performance-driven resource optimization strategies and allocation policies for virtualized cloud environments

Cloud computing has transformed Information Technology infrastructure by enabling scalable and efficient resource management through virtualization. Moreover, the allocation and migration of VMs are complex processes because of varying workloads, power management, and SLA constraints. Proper allocation policies are very important since they will improve system performance, fragment less, and provide energy-efficient solutions. This review and analytical study investigates the most popular VM allocation policies, i.e., BFD, WFD, MBFD, and MM, assessing their performance effects on resource utilization and system stability. Then, insights into SI-theoretical affairs (ABC, PSO, and the Firefly algorithm) and machine-learning-based decisions for adaptive management are discussed. By optimizing computation time and energy consumption, this study is a guiding strategy for building the future of hybrid AI-driven optimization for sustainable cloud computing.