Weight factor and priority-based virtual machine load balancing model for cloud computing

Cloud computing enables individuals and businesses to buy services as needed. Numerous services are available through the paradigm, including online services that are easily accessible, platforms for deploying applications, and storage. One major problem in the cloud is load balancing (LB), making it difficult to guarantee application performance to the Quality of Service (QoS) measurement and adhere to the Service Level Agreement (SLA) document as cloud providers require of businesses. Equitable workload distribution among servers is a challenge for cloud providers. By effectively using virtual machines' (VMs) resources, an effective load-balancing approach should maximize and guarantee high user satisfaction. This research paper proposes an efficient load-balancing model for cloud computing using a weight factor and priority-based approach. This approach efficiently allocates the VM to the Physical Machine (PM). The main objective of this approach is to maintain QoS while reducing power usage, resource waste, and migration overhead. Based on the resources (CPU, RAM, Bandwidth), the PM current condition is computed using the suggested PM load identification algorithm based on the resource weight factor. The priority-based VM allocation model determines the ideal solution for selecting the suitable PM for the VM. The recommended method is simulated using the Cloudsim toolbox, and performance in terms of EC and SLA breaches is assessed using the PlanetLab workload. Ultimately, the experimental findings demonstrate that the suggested algorithm significantly improves SLAV and energy usage compared to existing approaches.