Artificial Intelligence-Enabled Carbon Footprint Prediction and Optimization for Sustainable Data Centers
Abstract
The exponential growth of cloud services and artificial intelligence workloads has significantly increased energy consumption in modern data centers, raising concerns regarding environmental sustainability. This paper proposes an AI-enabled framework for predicting and optimizing carbon emissions generated by large-scale data centers. Machine learning models analyze workload characteristics, server utilization, cooling system performance, and renewable energy availability to estimate carbon emissions in real time. A multi-objective optimization algorithm dynamically allocates computational resources to minimize energy consumption while maintaining service-level agreements. Experimental evaluation demonstrates considerable reductions in energy usage, cooling costs, and carbon emissions compared with traditional resource management strategies. The proposed framework provides an intelligent solution for developing environmentally sustainable and energy-efficient cloud computing infrastructures.
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