Carbon Footprint Reduction for Sustainable Data Centers in Real-Time

arXiv:2403.14092v1 Announce Type: new
Abstract: As machine learning workloads significantly increase 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 …

abstract arxiv availability carbon carbon footprint consumption cooling corporations cs.ai cs.lg cs.ma cs.sy data data centers eess.sy emissions energy governments low machine machine learning paradigm power power consumption real-time shift sustainable type workloads