Optimizing Energy Efficiency in Cloud Data Centers Using AI-Driven Workload Migration

This research paper presents an AI-driven approach for optimizing energy efficiency in cloud data centers through intelligent workload migration. The proposed model dynamically reallocates workloads based on server utilization to minimize energy consumption while maintaining service-level agreements (SLAs). The system uses workload simulation, heuristic-based migration policies, and a realistic power consumption model to analyze energy reduction. Results demonstrate that AI-assisted workload balancing can achieve significant energy savings and improve data center sustainability. Future work includes reinforcement learning integration for adaptive migration decision-making.