Developing a Carbon Neutral Cloud Infrastructure with Intelligent Power Management in Green Cloud AI Using Reinforcement Learning

The use of cloud computing has increased rapidly, which has greatly damaged the power consumption in facilities and increased operational costs and carbon emissions. The traditional power management methods for control of energy consumption in cloud infrastructure do not adapt dynamically and thus lead to wastage of resources. In response to this challenge, this research aims at implementing RL in Green Cloud AI to achieve an efficient smart power management to make the cloud completely carbon neutral. RL also allows to make adaptive decisions regarding the important energy-saving strategies based on the effective real-life experience in the cloud environment. Here it proactively moves the virtual machines, assigns workloads and allocates power in a way that can minimize energy usage while preserving the service quality. Furthermore, RL models can easily manage the distribution of the load between renewable and non-renewable electricity sources to support sustainable cloud functions. The paper aims at discussing the application of Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) to optimize the cloud resource allocation so as to enhance the energy efficiency and minimize carbon traces. Computer simulations prove that incorporating power control with the help of the RL algorithm leads to effective saving of energy and reduced usage of fossil fuels. Thus, with the usage of efficient RL-based policies, the research is beneficial for the advancement of environmentally sustainable cloud computing consistent with the sustainable development goals. As such, the proposed system may be used to create a yet more extensive solution for attaining the carbon-neutral cloud infrastructure in future data centres.