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
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