一句话：提出 LasRCA 框架，以 LLM 标注反馈迭代增强小分类器，实现云原生系统的单样本故障根因分析。
微服务与云原生系统的故障根因分析（RCA）是保障可靠性的关键，但资源实体异构、可观测数据规模庞大，人工规则式分析负担沉重；已有自动化方法又依赖大量故障标签，难以深入定位故障。针对标签稀缺问题，论文考察每种故障类型仅有一个样本（one-shot）的极端场景，提出 LasRCA 框架：训练阶段先以单样本故障样例训练小分类器，再由该分类器迭代挑选高混淆样本，交由大语言模型驱动的故障标注给出类型反馈，并用这些样本重训分类器；推理阶段令 LLM 与小分类器协同完成联合 RCA，在效果与成本之间取得折中。在具有异构资源实体和常见故障类型的公开数据集上的实验验证了 LasRCA 在 one-shot 根因分析中的有效性。
Failure root cause analysis (RCA), which systematically identifies underlying faults, is essential for ensuring the reliability of widely adopted microservice-based applications and cloud-native systems. However, manual analysis by simple rules faces significant burdens due to the heterogeneous nature of resource entities and the massive amount of observability data. Furthermore, existing approaches for automating RCA struggle to perform in-depth fault analysis without extensive fault labels. To address the scarcity of fault labels, we examine an extreme RCA scenario where each fault type has only one example (one-shot). We propose LasRCA, a framework for one-hot RCA in cloud-native systems that leverages the collaboration of the large language model (LLM) and the small classifier. In the training stage, LasRCA initially trains a small classifier based on one-shot fault examples. The small classifier then iteratively selects high-confusion samples and receives feedback on their fault types from LLM-driven fault labeling. These samples are applied to retrain the small classifier. In the inference stage, LasRCA performs a joint RCA through the collaboration of the LLM and small classifier, achieving a trade-off between effectiveness and cost. Experiment results on public datasets with heterogeneous nature and prevalent fault types show the effectiveness of LasRCA in one-shot RCA.
DOI 原文 ·
@inproceedings{paperbot8294,
title = {The Potential of One-Shot Failure Root Cause Analysis: Collaboration of the Large Language Model and Small Classifier},
author = {Yongqi Han and Qingfeng Du and Ying Huang and Jiaqi Wu and Fulong Tian and Cheng He},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695475}
}