基于大语言模型的可持续数据中心运行预测决策

中文摘要 AI 辅助

人工智能驱动的工作负载需求不断增长，尤其是大语言模型（LLM）带来的需求，引发了对数据中心巨大能源与资源消耗的担忧。本研究提出一种新型基于LLM的预测调度系统，旨在提升数据中心运行效率并降低环境影响。该系统利用LLM从源代码预测执行时间、能源消耗等关键指标，若数据中心可追踪相关数据，还可扩展至冷却用水、碳排放等其他可持续性指标。预测模型后接实时调度算法，用于分配GPU资源，通过优化能源消耗与排队延迟提升可持续性。该方法推理速度快、可泛化至各类任务类型、训练数据需求少，为数据中心调度提供实用解决方案，对推进人工智能驱动基础设施的可持续目标具有重要潜力。通过与某数据中心合作，本研究实现了32%的能源消耗降低与30%的等待时间减少。

英文摘要

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.

发表机构

• The University of Sydney Business School, The University of Sydney（悉尼大学商学院，悉尼大学）
• University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
• Purdue University（普渡大学）

机构由 AI 辅助整理，请以论文原文为准。