arXiv cs.AI· Madhulatha Mandarapu, Sandeep Kunkunuru·· 13 小时前AI 评分31
开源告警风暴生成器：基于真实骨干拓扑，精确记录根因并对齐 NORIA-O、RFC 8632 与 TMF642
Every Alarm Has a Cause: An Open Generator of Telecom Alarm Storms with Exact Root Causes, Bound to NORIA-O, RFC 8632 and TMF642
AI 导读
研究者发布了一个开源告警风暴生成器，运行在 Internet Topology Zoo 的真实骨干拓扑上，生成时即记录每条告警的精确根因与传播路径，所有字段绑定 NORIA-O、RFC 8632、RFC 8345 或 TMF642 术语，未知术语会导致运行失败。
正文 · 原文
Abstract:Alarm root-cause analysis is studied on data that cannot be shared. Operator logs are confidential, so what the field publishes is a causal graph between alarm types rather than per-incident causes, a fixed scenario set, gated, or never released. Two recent benchmarks carry per-incident root causes at scale; neither states a download location or a licence.
We give a generator rather than a dataset. It runs on real backbone topologies from the Internet Topology Zoo and records the exact root cause and the propagation path of every alarm -- a by-product of generation, not a labelling pass. Every emitted field binds to a term in NORIA-O, RFC 8632, RFC 8345 or TMF642, and an unknown term fails the run. We ship fixed configurations, splits, a scorer and nine baselines.
A one-line rule -- rank the device that raised the earliest critical alarm -- takes top-1 at 0.650 against 0.575 for our own temporal method, and wins 13 of 15 runs over three backbones and five seeds. An LLM given the same view reaches 0.600, the same model allowed to query the graph 0.625, and the rule wins on real operator alarms too, at 0.773: a property of alarm streams, not of our generator. One fault class separates the table: when a site loses power its PoP reports nothing, so every method that ranks alarmed devices scores 0.000 or near it, while the two that walk the topology to a silent candidate take 1.000 and 0.833.
| Comments: | 22 pages, 5 figures, 7 tables. Generator: this https URL (Apache-2.0). The dataset deposit is prepared but not yet published, so this version cites no DOI; a revision will carry it |
| Subjects: | Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) |
| ACM classes: | C.2.3; I.2.4; H.2.8 |
| Cite as: | arXiv:2610.30004 [cs.NI] |
| (or arXiv:2610.30004v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.30004 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sandeep Kunkunuru [view email]
[v1]
Wed, 21 Oct 2026 16:40:00 UTC (49 KB)
来源：arXiv cs.AI · arxiv.org