AI-DRIVEN DYNAMIC VM CONSOLIDATION AND RENEWABLE-AWARE SCHEDULING FOR REDUCING CARBON FOOTPRINT IN CLOUD DATA CENTERS

Abstract

The surging energy requirements and the impact on the environment of the cloud data center suggests the necessity of establishing intelligent resource management plans to facilitate sustainability. The proposed hybrid framework which incorporates hybrid dynamic virtual machine (VM) consolidation based on the idea of artificial intelligence (AI) with the concept of renewable-aware differences in this paper to decrease energy and carbon emission in cloud computing environment. The consolidation module employs reinforcement learning to dynamically locate and move VMs based on real time workload and server utilization patterns. Concurrently, a renewable-sensitive scheduler predicts availability of solar energy and wind energy and executes tasks around green energy highlights. The framework is tested with the help of CloudSim Plus, actual Google Cluster traces, and synthetic renewable traces. The findings indicated that there was a decrease of energy of 37 percent and carbon emission decrease of 46.7 percent over the traditional models with a high SLA compliance along with minimizing the VM migration overhead. The given solution has a great potential to become an energy-efficient and environmentally-friendly cloud infrastructure that does not worsen performances.