arXiv：cs.LG· Yixing Li, Mark Fenton, Matthew Kaufeler, Ka Ming Leung, Xin Ai, Zhiyu Zeng·· 3 小时前AI 评分39
AI 驱动的热感知数据中心容量规划框架
AI-driven Thermal-aware Data Center Capacity Planning
AI 导读
该研究提出一套 AI 驱动的热感知数据中心容量规划框架，可在数秒内完成真实数据中心的容量规划。其内置 AI 模型学习机架功率、服务器功率、服务器布局、HVAC 设置等关键参数，能在毫秒级给出温度预测，相比高保真 CFD 仿真在未见过的数据中心设计上实现 10000X 加速且保持高精度。该框架可帮助设计与运维人员即时优化工作负载分布和 HVAC 冷却效率。
正文
Abstract:The emerging of large language models (LLMs) has posed significant challenges to the thermal management of data center. Intense GPU computation for LLMs results in localized hotspots. Moreover, spiking thermal loads during training and inference bursts make real-time cooling response more difficult to predict and control. Thermal-aware capacity planning of data center requires massive expensive high-fidelity CFD simulations. AI models can perform real-time prediction for unseen designs. However, existing works either have large prediction error, or have over-simplified assumptions for data center operations. This work presents an AI-driven framework that can perform thermal-aware capacity planning for a real-world data center in seconds. The embedded AI model learns from numerous key parameters (rack power, server power, server placement, HVAC settings etc.), and provides temperature prediction within milliseconds. This AI model is tested against high-fidelity CFD simulations, and results show that for unseen data center designs, model can achieve high accuracy with 10000X speedup. Driven by the AI model, the authors design the thermal-aware capacity planning framework. This framework can help data center designers and operators instantaneously optimize both workload distribution and HVAC cooling efficiency.
| Comments: | Presented at DesignCon 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.02442 [cs.LG] |
| (or arXiv:2610.02442v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02442 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yixing Li [view email]
[v1]
Thu, 1 Oct 2026 20:09:25 UTC (1,472 KB)
来源：arXiv：cs.LG · arxiv.org