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arXiv Machine Learning By Hanzhao Wang, Jingxuan Wu, Yumeng Li, Yu Pan, Guanting Chen

LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations

Read the original on arXiv Machine Learning →

The paper presents an LLM-based predictive scheduling system that forecasts execution time and energy consumption from source code, aiming to improve data center sustainability. By integrating these predictions into a real-time GPU allocation algorithm, the system reduces both energy use and queuing delays. In a collaboration with a data center, the approach achieved a 32% drop in energy consumption and a 30% reduction in waiting time.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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