Green AI: Optimizing Energy Efficiency of Workloads for Sustainable Data Centers

In the rapidly evolving landscape of artificial intelligence, the energy demands of AI workloads, particularly those involving deep learning and large-scale data processing, pose significant challenges for data centers. This paper presents a comprehensive solution for energy-aware scheduling of AI workloads, aimed at optimizing energy usage, reducing operational costs, and minimizing environmental impact. By dynamically managing AI workloads in response to real-time energy availability from power grid, the proposed system ensures optimal resource utilization and performance-per-watt ratios. The system's architecture integrates key components such as AI workload information collection, energy profiling, workload categorization, and dynamic scheduling, enabling efficient management of computational resources. This approach not only enhances the performance and reliability of AI systems but also leads to substantial cost savings and better scalability as AI applications grow in complexity. As AI continues to integrate into diverse sectors, the demand for efficient and sustainable AI operations will only increase. Energy-aware scheduling emerges as a pivotal strategy to meet this demand, ensuring that AI advancements are both economically viable and environmentally responsible. By adopting these innovative scheduling techniques, organizations can achieve a balance between high performance and energy efficiency, driving forward with enhanced efficiency and sustainability in the competitive AI landscape.