arXiv：cs.LG· Abi Aryan·· 4 小时前AI 评分32
AID：AI 基础设施动力学框架
AID: A Framework for AI Infrastructure Dynamics
AI 导读
研究者提出 AID（AI Infrastructure Dynamics）框架，用于描述耦合物理、计算、网络与服务流程的 AI 推理基础设施学习问题，支持结构化可变状态、异步观测、多物理时间尺度及随服务变化的需求。该框架给出两项分析结果：当观测无法区分模型时预测误差的下界，以及精确受控状态约简的充分条件，并提出针对缓存表示、工作负载历史、测量可用性与施加动作的验证协议。
正文
Abstract:A useful model of AI inference infrastructure must specify the system state, the information available to an observer, and the decisions the model is intended to support. We introduce AID (AI Infrastructure Dynamics), a framework for describing this learning problem across coupled physical, computational, networking, and serving processes. The formulation allows structured and variable-size state, asynchronous observations, multiple physical timescales, and demand that responds to service. We distinguish representations that support prediction under an existing policy from those that preserve service outcomes under changed actions, and separate both from identifying intervention responses. Two analytical results describe a lower bound on prediction error when available observations cannot distinguish models and a sufficient condition for exact controlled state reduction. These results apply established information and state-abstraction principles to AI infrastructure. We then describe a validation protocol for cache representations, workload histories, measurement availability, and imposed actions.
| Comments: | 14 pages, 3 figures |
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.04801 [cs.DC] |
| (or arXiv:2610.04801v2 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.04801 arXiv-issued DOI via DataCite |
Submission history
From: Abi Aryan [view email]
[v1]
Sat, 3 Oct 2026 23:01:48 UTC (18 KB)
[v2]
Tue, 6 Oct 2026 11:13:12 UTC (18 KB)
来源：arXiv：cs.LG · arxiv.org