用图谱+大模型，两分钟内精准定位Kubernetes故障根源。
Simplifying Root Cause Analysis in Kubernetes with StateGraph and LLM
- 构建状态图与元图，捕捉资源时空关系和连接逻辑。
- 平均2分钟识别根因，精确率高达0.90，发现新类型故障。
- 适合运维工程师、云平台团队快速排查复杂系统问题。
Kubernetes作为复杂的分布式系统，依赖多个控制器通过状态协调机制管理集群。然而，在动态云环境中，意外故障、网络中断和异步问题导致状态不一致，引发运维中断与经济损失，亟需高效可靠的根因分析（RCA）。尽管大语言模型（LLM）为RCA带来新可能，但现有方法受限于事件多样性、上下文复杂性和事件多态性。本文提出SynergyRCA，结合图数据库检索增强与专家提示优化的LLM。该工具构建StateGraph以捕获资源的时空关系，MetaGraph刻画实体连接结构。当发生故障时，LLM预测最相关资源，再通过查询两图获取上下文信息支持诊断。我们在两个生产级Kubernetes集群数据集上评估，结果表明，SynergyRCA能高效精准识别多种根因，包括新型故障，平均响应时间约2分钟，精确率达约0.90。
原文摘要 · Abstract (English)
Kubernetes, a notably complex and distributed system, utilizes an array of controllers to uphold cluster management logic through state reconciliation. Nevertheless, maintaining state consistency presents significant challenges due to unexpected failures, network disruptions, and asynchronous issues, especially within dynamic cloud environments. These challenges result in operational disruptions and economic losses, underscoring the necessity for robust root cause analysis (RCA) to enhance Kubernetes reliability. The development of large language models (LLMs) presents a promising direction for RCA. However, existing methodologies encounter several obstacles, including the diverse and evolving nature of Kubernetes incidents, the intricate context of incidents, and the polymorphic nature of these incidents. In this paper, we introduce SynergyRCA, an innovative tool that leverages LLMs with retrieval augmentation from graph databases and enhancement with expert prompts. SynergyRCA constructs a StateGraph to capture spatial and temporal relationships and utilizes a MetaGraph to outline entity connections. Upon the occurrence of an incident, an LLM predicts the most pertinent resource, and SynergyRCA queries the MetaGraph and StateGraph to deliver context-specific insights for RCA. We evaluate SynergyRCA using datasets from two production Kubernetes clusters, highlighting its capacity to identify numerous root causes, including novel ones, with high efficiency and precision. SynergyRCA demonstrates the ability to identify root causes in an average time of about two minutes and achieves an impressive precision of approximately 0.90.
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