WSO: An Intelligent Metaheuristic Driven High Performance Task Scheduling in Cloud Computing
Praveen Kumar Gopagoni, Santhosh Kumar Medishetti, Sarikonda Sree Hari Raju, K Chandana, M. S. Sai Reddy, M. S. Sai Reddy
Abstract
Existing task scheduling algorithms in cloud computing often struggle to balance execution time, energy consumption, and operational cost while maintaining high resource utilization. Most traditional methods fail to adapt effectively to dynamic workloads, leading to inefficiencies in large-scale environments. To address these limitations, this research introduces the War Strategy Optimization (WSO) algorithm, which leverages strategic principles inspired by battlefield tactics to achieve intelligent task-to-VM mapping. Unlike traditional algorithms, WSO dynamically adjusts its exploration and exploitation phases to suit the workload characteristics, ensuring optimal resource allocation. It significantly minimizes makespan by scheduling tasks more efficiently, reduces energy consumption through power-aware resource distribution, and lowers operational costs by minimizing idle and underutilized resources. Additionally, WSO enhances resource utilization by maintaining a balance between task demand and available VM capacity. Experimental evaluation using the HPC2N workload in CloudSim confirms the superiority of WSO, achieving a 22.8% reduction in makespan, 19.4% less energy consumption, 17.9% cost savings, and a 23.6% increase in resource utilization. Thus, WSO effectively overcomes the limitations of existing approaches, offering a scalable and adaptive solution for cloud environments.