用于数据中心能源优化的人工智能

中文摘要 AI 辅助

数据中心正越来越多地借助人工智能进行优化，但同时也因人工智能的应用而承受着日益增长的负载。现有文献将这两个问题视为互不相关的：控制研究将工作负载建模为外生到达过程，而可持续性研究则将基础设施建模为固定乘数。我们通过既定协议检索了约194篇论文，对其中63篇进行了编码，并报告编码结果：在28项以控制为导向的主要研究中，18项仅在仿真中得到验证，5项涉及物理硬件或生产设施；没有一项研究考虑取水问题，也没有一项研究考虑隐含碳。四种技术类别所报告的节能区间几乎完全重叠，这意味着该领域目前无法对自身方法进行排名。我们对10个反复出现的缺口就其重要性和可处理性进行了评分，并提出了CLEAR-DC框架，该框架通过显式弹性项将控制策略分支与工作负载需求分支耦合，输出净效益而非直接效益，并生成符合模式的记录，涵盖能源、碳、水、隐含份额和验证场所。该框架是一项架构和方法论提案，而非经过训练的系统；我们通过实证论证的贡献在于语料库分析及由此衍生的报告模式。编码表、衍生统计数据和所有结果工件可在此处获取：this https URL

英文摘要

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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11 pages, 6 figures, 7 tables