Sustainable Energy Utilization Strategy in Green Cloud Using Deep Reinforcement Learning

Green cloud computing is a novel approach that helps optimize energy use in data centers to reduce their adverse impacts on the environment. To achieve better scheduling efficiency and resource utilization, this work integrates machine learning techniques, particularly reinforcement learning, into the model. The experimental results demonstrate that there is an impressive saving of energy consumption by using PPO and DQN for dynamic resource management. This helps to eliminate unnecessary virtual machine migrations due to workload forecasting with the Gray-Markov Prediction Model. Experimental results indicate that a dataset like Google Cluster Traces, Alibaba Cloud Data, or UK Carbon Intensity Data would be used with up to a 25% increase in energy efficiency. Thus, this study showcases how one may apply reinforcement learning for achieving sustainable cloud computing by ensuring minimal computational overhead at no expense of quality service provision.