Virtual Machine Load Balancing Model Framework for Cloud Computing
E. Suganthi, F. Kurus Malai Selvi · Engineering Technology & Applied Science Research · 2025
Cloud Computing (CC) is a comprehensive paradigm that enables individuals and businesses to acquire necessary services on demand. CC provides numerous services, including archiving, distribution platforms, and easy access to online services. Implementing CC necessitates overcoming various difficulties, such as resource identification, protection, scheduling, and Load Balancing (LB). This study examines LB, which distributes workloads across cloud systems to ensure fair resource allocation and prevent Virtual Machines (VMs) from becoming over- or under-loaded. An effective LB solution is essential to maximize VM resource utilization while ensuring high user satisfaction. This study develops the VM LB model framework for CC, which includes a state and random model, a Weight Factor (WF) and priority-based model, and a two-stage optimal model. These models efficiently allocate the VM to the Physical Machine (PM) using Cloudsim. The PlanetLab workload evaluates the performance of the models in terms of Energy Consumption (EC) and Service Level Agreement Violation (SLAV). The experimental results indicate that the proposed model improves Service Level Agreement (SLA) compliance and energy efficiency.