大模型
arxiv.org ·
ETCInfer：面向 AI 数据中心 LLM 推理的节能热感知冷却联合调度器
该论文研究 LLM 推理中 GPU 服务与机房冷却的联合控制问题：提高环境温度设定点可降低冷却能耗与碳排放，但会压缩热余量、引发 GPU 降频并导致 SLO 违规。作者提出 ETCInfer，通过遥测校准 GPU 发热、机箱散热、CRAC 功耗及 prefill/decode 延迟关系，构建紧凑的物理信息控制模型以估计隐藏热状态与降频时间，并将设定点—频率—微批大小联合控制建模为部分可观测马尔可夫决策过程，设计基于学习的控制器 ETCAdapter。评估显示，在真实轨迹仿真与验证实验中，总作业能耗最多降低 33.1%，热降频暴露最多降低 92.9%，在环境温度高达 48°C 时 SLO 违规率仍低于 0.7%。
AI推荐理由 arXiv 论文，摘要明确给出方法（物理信息控制模型 + POMDP + ETCAdapter 学习控制器）与量化结果（总能耗最多降 33.1%、热降频暴露最多降 92.9%、SLO 违规率低于 0.7%、环境温度可达 48°C），信息密度高、事实具体。局限在于结果来自真实轨迹仿真与验证实验，并非大规模生产部署，且论文为预印本、尚未经同行评审，实际落地效果仍需验证。对 AI 数据中心能效与推理调度方向有参考价值。
78
正文
Computer Science > Distributed, Parallel, and Cluster Computing
arXiv:2609.15230 (cs)
[Submitted on 14 Sep 2026]
Title:ETCInfer: An Energy-efficient Thermal-aware Cooling-joint Scheduler for LLM Inference in AI Datacenters
Authors:Rui Lu, Rui Ge, Huanghuang Liang, Xiaobo Zhou, Dan Wang
View a PDF of the paper titled ETCInfer: An Energy-efficient Thermal-aware Cooling-joint Scheduler for LLM Inference in AI Datacenters, by Rui Lu and 4 other authors
View PDF
HTML (experimental)
Abstract:Large language model (LLM) inference in AI datacenters creates a coupled control problem between GPU serving and facility cooling. Raising ambient temperature setpoints can reduce cooling energy and carbon, but also shrinks thermal headroom, induces GPU throttling, and leads to Service-Level-Objective (SLO) violations. In this paper, we study joint cooling--computing control for LLM inference: minimizing per-job GPU-plus-cooling energy while satisfying thermal safety and latency SLO constraints. We present ETCInfer, an energy-efficient, thermal-aware scheduler that selects a pre-job Computer Room Air Conditioner (CRAC) setpoint and adapts per-GPU frequency and micro-batch size during execution. ETCInfer builds compact physics-informed control models by calibrating GPU heat generation, chassis heat dissipation, CRAC power, and prefill/decode latency relations from telemetry. These models estimate hidden thermal states and time-to-throttle, enabling the scheduler to evaluate energy, temperature, and latency before applying an action. We formulate this joint setpoint--frequency--micro-batch control problem as a partially observable Markov decision process and design ETCAdapter, a learning-based controller that minimizes per-job energy under thermal safety and SLO constraints. We implement ETCInfer as a coordination layer over typical inference and cluster management stacks. Evaluation across real-trace simulation and validation experiments shows that ETCInfer reduces total job energy by up to 33.1%, thermal throttle exposure by up to 92.9%, and keeps SLO violation rates below 0.7% even at ambient temperatures up to $48^{\circ}\mathrm{C}$.
Comments:
Subjects:
Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF); Systems and Control (eess.SY)
Cite as:
arXiv:2609.15230 [cs.DC]
(or
arXiv:2609.15230v1 [cs.DC] for this version)
https://doi.org/10.48550/arXiv.2609.15230
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Rui Lu [view email]
[v1]
Mon, 14 Sep 2026 08:50:43 UTC (3,324 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled ETCInfer: An Energy-efficient Thermal-aware Cooling-joint Scheduler for LLM Inference in AI Datacenters, by Rui Lu and 4 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.DC
new
|
recent
| 2026-09
Change to browse by:
cs
cs.PF
cs.SY
eess
eess.SY
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? |
Disable MathJax (What is MathJax?)