GreenCloud AI: Framework for Sustainable Distributed Data Management in Cloud Ecosystems

The rapid expansion of cloud computing and data-intensive applications has led to significant environmental concerns due to increasing energy consumption in data centers. This paper introduces the concept of “GreenCloud AI,” a theoretical framework for sustainable and energy-efficient distributed data management in cloud ecosystems. The proposed conceptual model envisions the integration of artificial intelligence techniques to address the challenge of dynamic data placement optimization in heterogeneous cloud storage environments. GreenCloud AI, as a theoretical construct, proposes leveraging machine learning algorithms to predict workload patterns, analyze energy consumption profiles, and inform decision-making processes for data allocation across diverse storage tiers and geographically distributed data centers. By considering factors such as varying energy costs, carbon intensities, renewable energy availability, and regulatory compliance, the framework aims to conceptually achieve a balance between energy efficiency, carbon emission reduction, and system performance. This research contributes to the field of sustainable informatics by outlining potential approaches for energy-efficient data management in cloud ecosystems, providing a foundation for future research and development in creating more environmentally responsible cloud infrastructure.