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AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies· AtumAI: 自动化生成数据中心控制面策略的框架

The efficiency of a datacenter rests on its control plane policies. Designing these policies is increasingly hard: the hardware-software stack grows fast, the design space is vast and interdependent, and prototyping a single policy takes months. Agentic AI promises to automate this search. Off the shelf, however, it falls short on three fronts. It is not formal: with no structured, searchable statement of the problem, the search has little structure to exploit and hard constraints are not guaranteed. It is not transferable: each task is solved from scratch, so nothing learned on one task carries to the next. Finally, it is not systematic: relying on the LLM as the sole source of candidates, it explores a narrow slice of the design space and settles into local optima. We introduce AtumAI, a

AI 解读论文

自动化框架提高数据中心控制面策略生成效率

核心方法
提出AtumAI框架,结合形式化问题定义、知识迁移和系统性探索策略
适合谁读
研究者 / 工程师
要解决的问题
数据中心控制面策略设计复杂、耗时且容易陷入局部最优
关键实验
未提供
主要贡献
AtumAI框架能够更高效、更系统地生成高质数据中心控制面策略
意义与局限
为数据中心自动化管理提供新工具,但需进一步验证其实际效果
领域:cs.AI作者:Qiushi Lin、Chaojie Zhang、Íñigo Goiri
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