用大模型预测代码能耗与执行时间，优化数据中心调度
LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations
- 用大模型从代码预测执行时长和能耗，无需大量训练数据
- 实测实现32%节能和30%等待时间减少
- 适合关注AI基建可持续性的运维与架构团队
AI工作负载（尤其是大语言模型）的持续增长，引发数据中心能源与资源消耗的担忧。本文提出一种基于大模型的预测调度系统，通过大模型从源代码预测执行时间与能耗，并可扩展至水耗、碳排放等可持续性指标（若数据中心具备相关数据追踪能力）。随后采用实时调度算法分配GPU资源，以兼顾能效与排队延迟。该方法推理速度快、跨任务类型泛化能力强、训练数据需求少，具有实际部署价值。与数据中心合作验证表明，系统实现32%的能源消耗降低和30%的等待时间减少。
原文摘要 · Abstract (English)
The growing demand for AI-driven workloads, particularly from Large Language Models (LLMs), has raised concerns about the significant energy and resource consumption in data centers. This work introduces a novel LLM-based predictive scheduling system designed to enhance operational efficiency while reducing the environmental impact of data centers. Our system utilizes an LLM to predict key metrics such as execution time and energy consumption from source code, and it has the potential to extend to other sustainability-focused metrics like water usage for cooling and carbon emissions, provided the data center can track such data. The predictive model is followed by a real-time scheduling algorithm that allocates GPU resources, aiming to improve sustainability by optimizing both energy consumption and queuing delays. With fast inference times, the ability to generalize across diverse task types, and minimal data requirements for training, our approach offers a practical solution for data center scheduling. This framework demonstrates strong potential for advancing sustainability objectives in AI-driven infrastructure. Through our collaboration with a data center, we achieved a 32% reduction in energy consumption and a 30% decrease in waiting time.
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