Optimizing Cloud Computing Performance Through Integration of a Threshold-Based Load Balancing Algorithm With Multiple Service Broker Policies

Abstract

The triumph of cloud computing hinges upon the adept instantiation of infrastructure and the judicious utilization of available resources. Load balancing, a pivotal facet, substantiates the fulfillment of these imperatives, thereby augmenting the performance of the cloud environment for its users. Our research introduces a load balancing algorithm grounded in threshold principles devised to ensure equitable distribution of workloads among nodes. The main objective of the algorithm is to preclude the overburdening of virtual machines (VMs) within the cloud with tasks or their idleness due to task allocation deficiencies in the presence of active tasks. The threshold values embedded in our algorithm ascertain the judicious deployment of VMs, forestalling both task overload and idle states arising from task allocation inadequacies. Simulation outcomes manifest that our threshold-based algorithm markedly enhances response time for tasks/requests and data processing duration within datacenters, outperforming extant algorithms such as First Come First Serve, Round Robin, and the Equally Spread Current Execution Load Balancing algorithm. Our threshold algorithm attains superior results to alternative load balancing algorithms when coupled with an optimized response time service broker policy.