提出跨控件与负载的能效优化框架，解决数据中中心AI自耗难题。
Artificial Intelligence for Energy Optimization in Data Centers
- 构建耦合控制策略与负载需求的弹性框架，显式建模能耗关系
- 发现现有研究普遍忽略水耗与隐含碳，方法对比无法有效排序
- 提供可验证的多维度报告模板，适合可持续性评估与实证研究
数据中心正被人工智能不断优化，同时也日益受其负载所累。现有文献将二者视为独立问题：控制研究将工作负载视为外生到达过程，可持续性研究则将基础设施视为固定乘数。我们通过既定协议筛选约194篇论文，编码其中63篇，并报告编码结果。28项以控制为核心的研究所中，18项仅在仿真中验证，5项达到物理硬件或生产环境；无一考虑取水消耗，也无一计入隐含碳排放。四种技术路径报告的节能区间几乎完全重叠，表明该领域当前无法对方法进行有效排序。我们识别出十项常见缺口，按后果与可解性评分，并提出CLEAR-DC框架：通过显式弹性项连接控制策略与负载需求，输出净效益而非直接收益，并生成涵盖能源、碳、水、隐含份额及验证场所的标准化记录。该框架为架构与方法论提议，非训练系统；我们实证捍卫的贡献在于语料分析与由此衍生的报告范式。编码表、统计结果与所有成果文件见：https://github.com/Kimalice/AI-for-Energy-Optimization-in-Data-Centers-Closing-the-Optimizer-Load-Loop
原文摘要 · Abstract (English)
Data centers are increasingly optimized by artificial intelligence and, at the same time, increasingly loaded by it. The literature treats these as two unrelated problems: control studies model workload as an exogenous arrival process, while sustainability studies model infrastructure as a fixed multiplier. We screen roughly 194 papers retrieved through a documented protocol, code 63 of them, and report what the coding shows. Of 28 primary control-oriented studies, 18 are validated in simulation alone and 5 reach physical hardware or a production facility; none account for water withdrawal, and none account for embodied carbon. Reported savings intervals across four technique families overlap almost completely, which means the field cannot presently rank its own methods. Ten recurring gaps are scored for consequence and tractability, and we set out CLEAR-DC, a framework coupling a control-policy branch to a workload-demand branch through an explicit elasticity term, reads out net rather than direct benefit, and emits a schema-conformant record covering energy, carbon, water, embodied share and validation venue. The framework is an architectural and methodological proposal, not a trained system; the contribution we defend empirically is the corpus analysis and the reporting schema derived from it. Coding sheet, derived statistics and all result artifacts: https://github.com/Kimalice/AI-for-Energy-Optimization-in-Data-Centers-Closing-the-Optimizer-Load-Loop
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