Energy-Efficient Task Scheduling in Green Cloud Computing

Author(s)	Bhavika Suraj Jain, Dr. H. R. Vyawahare
Country	India
Abstract	This paper presents a cloud-based task scheduling approach that reduces energy consumption by incorporating real-time and historical carbon-intensity data into scheduling decisions. As data centers face rising workloads—especially from AI and ML applications—the need for sustainable resource management has become critical. The proposed framework uses lightweight automation with AWS services and Kubernetes to identify low-carbon execution windows and dynamically shift energy-intensive tasks accordingly. Unlike traditional heuristic, metaheuristic, or machine-learning-heavy schedulers, the method maintains low computational overhead while improving energy efficiency and reducing emissions. The system’s adaptability and cloud-native design make it scalable across major cloud platforms. Overall, the work contributes a practical, environmentally conscious scheduling model aligned with emerging goals in Green Cloud Computing.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-19
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.60977

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International Journal For Multidisciplinary Research