Publication

Abstract : In the digital era, cloud computing is vital for scalable and efficient infrastructure, but its growing energy consumption raises serious environmental concerns. Green cloud computing strategies, particularly efficient task-scheduling algorithms, are key to addressing this challenge. Task scheduling in cloud computing is NP-hard due to the complexity of managing numerous tasks, resources, and optimization metrics. To address this, we propose a novel task scheduling algorithm named NIMS (Neighborhood Inspired Multiverse Scheduler), designed to optimize two often conflicting metrics: makespan and energy consumption. NIMS improves the performance of the original MVO (Multiverse Optimizer) by incorporating a novel fitness-based neighborhood search strategy during solution updates. This enhancement improves the quality of solutions, particularly when the standard update mechanism of MVO underperforms. By promoting a more effective exploration of the solution space, our approach significantly enhances the algorithm’s convergence rate. Further, we performed a comprehensive performance evaluation of the proposed NIMS algorithm against seven advanced algorithms: EMVO, IMOMVO, OPSO, LJFPPSO, TSIGA, FPGWO, and MVO, using the CloudSim toolkit under various test scenarios, leveraging three widely adopted real-world benchmark datasets. Our extensive simulations and experiments exhibit that the proposed NIMS algorithm significantly outperforms the competing algorithms across five key performance metrics: makespan, energy consumption, throughput, load imbalance, and average resource utilization.