A Neighborhood Inspired Multiverse Scheduler for Energy and Makespan Optimized Task Scheduling for Green Cloud Computing Systems

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.