Abstract

 The exponential growth of cloud computing has enabled unprecedented scalability and efficiency in handling dataintensive applications. However, this progress comes at the cost of significantly increased energy consumption, raising concernsabout sustainability and environmental impact. This research investigates energy-aware scheduling algorithms as a means to reduceenergy usage while maintaining performance in cloud environments. We analyze various state-of-the-art algorithms, evaluate theirsuitability for data-intensive workloads, and propose an adaptive scheduling framework that balances energy efficiency with taskperformance. Experimental results using simulated cloud environments demonstrate that energy-aware scheduling can significantlyreduce power consumption without degrading service quality, making it a viable solution for sustainable cloud computing.Keywords: Scheduling, cloud computing, energy efficiency, machine learning