用多模态数据让大模型精准定位云原生系统故障根源
TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systems
- 构建工具辅助的LLM代理，融合多源观测数据
- 在两个基准数据集上表现优于现有SOTA方法
- 适合运维工程师快速诊断复杂系统故障
在云原生系统中实现基于大语言模型（LLM）的根因分析（RCA）已成为现代软件运维的关键课题。然而，现有基于LLM的方法面临三大挑战：多模态输入约束、上下文窗口限制以及动态依赖图。为此，我们提出一种基于多模态观测数据的工具辅助LLM代理，名为TAMO，包含多模态对齐工具、根因定位工具和故障类型分类工具。TAMO将多模态观测数据统一为时间对齐的表示，保证跨模态特征一致性；在此基础上，调用专用的根因定位与故障类型分类工具，在系统上下文中进一步识别根因与故障类型。该方法克服了LLM处理实时原始观测数据和动态服务依赖的局限性，通过结构化提示设计引导模型生成符合系统上下文的修复策略。在两个基准数据集上的实验表明，TAMO在性能上达到甚至超过现有最先进（SOTA）方法。
原文摘要 · Abstract (English)
Implementing large language models (LLMs)-driven root cause analysis (RCA) in cloud-native systems has become a key topic of modern software operations and maintenance. However, existing LLM-based approaches face three key challenges: multi-modality input constraint, context window limitation, and dynamic dependence graph. To address these issues, we propose a tool-assisted LLM agent with multi-modality observation data for fine-grained RCA, namely TAMO, including multimodality alignment tool, root cause localization tool, and fault types classification tool. In detail, TAMO unifies multi-modal observation data into time-aligned representations for cross-modal feature consistency. Based on the unified representations, TAMO then invokes its specialized root cause localization tool and fault types classification tool for further identifying root cause and fault type underlying system context. This approach overcomes the limitations of LLMs in processing real-time raw observational data and dynamic service dependencies, guiding the model to generate repair strategies that align with system context through structured prompt design. Experiments on two benchmark datasets demonstrate that TAMO outperforms state-of-the-art (SOTA) approaches with comparable performance.
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