Multi-objective optimization based efficient resource scheduling scheme for infrastructure as a service cloud computing

Abstract

Resource scheduling in Infrastructure as a Service (IaaS) cloud computing faces critical challenges such as inefficient task allocation, prolonged makespan, unbalanced resource utilization, and elevated operational costs due to dynamic workloads and complex multi-objective constraints. Traditional scheduling algorithms often struggle with scalability, real-time adaptability, and efficient provisioning. To overcome these issues, this research introduces a novel evolutionary Multi-Objective-based K-means clustering Hybrid White-Faced Success Capuchin (MOK-HWFSC) algorithm. This hybrid model combines K-means clustering for task grouping, Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective trade-off optimization, and Hybrid White-Faced Success Capuchin optimization (HWFSC) for adaptive and heuristic-based task scheduling. The HWFSC component integrates white-faced capuchin optimization with success-based optimization to enhance convergence and search efficiency, thereby enabling balanced load distribution and improved scheduling accuracy. CloudSim serves as the simulation platform for evaluating the proposed model, providing a controlled and repeatable environment for performance testing. Experimental results demonstrate that MOK-HWFSC achieves superior performance, attaining resource utilization of 78 % at 300 tasks and 85 % at 600 tasks, outperforming benchmark models. Additionally, the model has significantly low computational overhead, with task scheduling processes completed in 20 ms for 300 tasks and 35 ms for 600 tasks, compared to 45 ms and 55 ms in existing methods. Overall, MOK-HWFSC enhances cloud resource scheduling by optimizing task distribution, minimizing makespan, improving energy efficiency, and ensuring scalable, cost-effective deployment in dynamic IaaS environments.