Published Paper Details:

The exponential growth of cloud computing has led to significant energy consumption and carbon emissions, highlighting the urgent need for sustainable and energy-efficient cloud infrastructures. This study presents GreenCloudSched, a novel task scheduling algorithm designed to optimize energy consumption while maintaining performance efficiency in cloud environments. We conduct a comprehensive comparative analysis between GreenCloudSched and several state-of-the-art scheduling algorithms, including Energy-Efficient Dynamic Scheduling (EEDS), Power-Aware Best Fit Decreasing (PABFD), and CloudSim's default heuristics. The evaluation metrics focus on energy consumption, task execution time, CPU utilization, and system throughput. Experimental results demonstrate that GreenCloudSched outperforms existing approaches by achieving substantial reductions in energy usage and improving overall resource utilization without compromising quality of service (QoS). This research contributes to the advancement of green cloud computing by offering a scalable and adaptable scheduling solution for next-generation sustainable cloud infrastructures.