Simplifying Root Cause Analysis in Kubernetes with StateGraph and LLM
Journal:
arXiv
Published Date:
Jun 3, 2025
Abstract
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.
