Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
career value
181K/year
🤖 AI Summary
To address the real-time low-carbon operational demands of data centers, this paper proposes DC-CFR, a multi-agent reinforcement learning (MARL) framework that jointly optimizes carbon emissions, energy consumption, and electricity cost under dynamic weather conditions and time-varying grid carbon intensity. The method integrates renewable-energy-aware IT workload scheduling, coordinated cooling system control, and dynamic uninterruptible power supply (UPS) battery storage management. Evaluated across multiple geographic regions using real-world traces, DC-CFR achieves average annual reductions of 14.5% in carbon emissions, 14.4% in energy consumption, and 13.7% in energy cost compared to the ASHRAE baseline controller. Its core contribution is the first real-time MARL control paradigm for holistic, stack-wide co-optimization of energy efficiency, carbon footprint, and economic cost—overcoming the limitations of conventional static-threshold control strategies—and providing a deployable intelligent decision-making foundation for green data centers.
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📝 Abstract
As machine learning workloads are significantly increasing energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the power grid, and leveraging battery storage from the uninterrupted power supply in data centers, using collaborative agents. The complex association between these optimization strategies and their dependencies on variable external factors like weather and the power grid carbon intensity makes this a hard problem. Currently, a real-time controller to optimize all these goals simultaneously in a dynamic real-world setting is lacking. We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework that optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions. DC-CFR significantly outperformed the industry-standard ASHRAE controller with a considerable reduction in carbon emissions (14.5%), energy usage (14.4%), and energy cost (13.7%) when evaluated over one year across multiple geographical regions.
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