用弃权机制提升数据中心网络故障根因分析的稳定性
The Abstention Protocol: RCA for Clos Fabrics
- 采用弃权代数代替加权融合，证据模糊时明确放弃判断
- 在大规模云网络中实现稳定且可解释的故障定位，无需重新调参
- 适合需要高可靠性的生产环境故障诊断系统
大型数据中心网络的根因分析（RCA）面临遥测数据噪声大、不完整且异步的问题，基于评分的方法在此条件下表现不稳定甚至错误。我们提出 extsc{CoreSec}，一个生产级RCA系统，将加权融合替换为类似PAM的弃权代数。遥测代理通过控制标志组合，当证据模糊时作出确定性弃权决策。CoreSec结合拓扑感知配置，捕捉Clos架构中的故障传播路径，并在证据累积时单调收敛。该系统已在超大规模环境中部署，无需重调参即可在多种环境下提供稳定、可解释的RCA行为。我们的经验表明，带弃权的结构化组合为真实云网络的自动化故障分析提供了实用基础。
原文摘要 · Abstract (English)
Root cause analysis (RCA) in large datacenter networks is challenging because telemetry is noisy, partial, and asynchronous. Score-based approaches degrade under these conditions, often yielding unstable or incorrect attributions. We present \textsc{CoreSec}, a production RCA system that replaces weighted fusion with a PAM-style abstention algebra. Telemetry agents are composed using control flags that yield deterministic decisions and explicit abstention when evidence is ambiguous. CoreSec combines this algebra with topology-aware configurations that capture failure surfaces across Clos fabrics and converge monotonically as evidence accumulates. Deployed at hyperscale, CoreSec provides stable and explainable RCA behavior across diverse environments without retuning. Our experience shows that structured composition with abstention forms a practical foundation for automated RCA in real-world cloud networks.
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