Published Paper Details:

Cloud computing has emerged as a pivotal technology in modern computing, offering vast resources for computation and storage to users on-demand. However, the substantial energy consumption associated with cloud data centers poses environmental concerns and financial burdens. Addressing this challenge requires the development of energy-efficient mechanisms, particularly in task scheduling, which plays a crucial role in resource allocation and utilization. This paper presents the design and implementation of an energy-efficient task scheduling algorithm tailored for cloud computing environments. The proposed algorithm aims to minimize energy consumption while meeting performance requirements, such as task completion time and resource utilization. Leveraging dynamic voltage and frequency scaling techniques, the algorithm intelligently allocates tasks to virtual machines, considering workload characteristics, resource availability, and energy profiles. To evaluate the effectiveness of the proposed algorithm, extensive experiments are conducted using simulation-based approaches. The results demonstrate significant reductions in energy consumption compared to traditional scheduling approaches, without compromising on performance metrics. Furthermore, sensitivity analysis is performed to assess the algorithm's robustness under varying workload intensities and system configurations. The findings of this study underscore the potential of energy-efficient task scheduling algorithms to mitigate the environmental impact of cloud computing while enhancing cost-effectiveness for service providers and users. The proposed algorithm contributes to the ongoing efforts in green computing and sustainable IT practices, paving the way for more environmentally friendly and economically viable cloud infrastructures.