Research on fault diagnosis and root cause analysis based on full stack observability

2025-09-08

AI Summary

To address the frequent cascading failures and challenging root cause localization in cloud environments and hyperscale data centers, this paper proposes KylinRCA—a unified framework integrating temporal causal discovery with cross-modal graph learning to jointly model multi-source observability data (metrics, logs, and traces). It constructs failure propagation chains via dynamic causal inference and employs hierarchical graph neural networks with attention mechanisms for global root cause localization and failure type classification. Furthermore, it introduces a mask-based explanation method to generate auditable evidence chains, balancing accuracy and interpretability. Extensive experiments across diverse real-world scenarios demonstrate that KylinRCA significantly outperforms state-of-the-art approaches in both precision and recall, while providing transparent, verifiable reasoning. This work establishes a novel, efficient, reliable, and auditable paradigm for root cause analysis in AIOps.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

With the rapid development of cloud computing and ultra-large-scale data centers, the scale and complexity of systems have increased significantly, leading to frequent faults that often show cascading propagation. How to achieve efficient, accurate, and interpretable Root Cause Analysis (RCA) based on observability data (metrics, logs, traces) has become a core issue in AIOps. This paper reviews two mainstream research threads in top conferences and journals over the past five years: FaultInsight[1] focusing on dynamic causal discovery and HolisticRCA[2] focusing on multi-modal/cross-level fusion, and analyzes the advantages and disadvantages of existing methods. A KylinRCA framework integrating the ideas of both is proposed, which depicts the propagation chain through temporal causal discovery, realizes global root cause localization and type identification through cross-modal graph learning, and outputs auditable evidence chains combined with mask-based explanation methods. A multi-dimensional experimental scheme is designed, evaluation indicators are clarified, and engineering challenges are discussed, providing an effective solution for fault diagnosis under full-stack observability.

Problem

• Achieving efficient and interpretable root cause analysis
• Handling fault propagation in complex cloud systems
• Integrating multi-modal observability data for diagnosis

Innovation

• Dynamic causal discovery for propagation chains
• Cross-modal graph learning for root cause
• Mask-based explanation methods for evidence