Paper
MetaRCA: A Generalizable Root Cause Analysis Framework for Cloud-Native Systems Powered by Meta Causal Knowledge
Authors
Shuai Liang, Pengfei Chen, Bozhe Tian, Gou Tan, Maohong Xu, Youjun Qu, Yahui Zhao, Yiduo Shang, Chongkang Tan
Abstract
The dynamics and complexity of cloud-native systems present significant challenges for Root Cause Analysis (RCA). While causality-based RCA methods have shown significant progress in recent years, their practical adoption is fundamentally limited by three intertwined challenges: poor scalability against system complexity, brittle generalization across different system topologies, and inadequate integration of domain knowledge. These limitations create a vicious cycle, hindering the development of robust and efficient RCA solutions. This paper introduces MetaRCA, a generalizable RCA framework for cloud-native systems. MetaRCA first constructs a Meta Causal Graph (MCG) offline, a reusable knowledge base defined at the metadata level. To build the MCG, we propose an evidence-driven algorithm that systematically fuses knowledge from Large Language Models (LLMs), historical fault reports, and observability data. When a fault occurs, MetaRCA performs a lightweight online inference by dynamically instantiating the MCG into a localized graph based on the current context, and then leverages real-time data to weight and prune causal links for precise root cause localization. Evaluated on 252 public and 59 production failures, MetaRCA demonstrates state-of-the-art performance. It surpasses the strongest baseline by 29 percentage points in service-level and 48 percentage points in metric-level accuracy. This performance advantage widens as system complexity increases, with its overhead scaling near-linearly. Crucially, MetaRCA shows robust cross-system generalization, maintaining over 80% accuracy across diverse systems.
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.02032v1</id>\n <title>MetaRCA: A Generalizable Root Cause Analysis Framework for Cloud-Native Systems Powered by Meta Causal Knowledge</title>\n <updated>2026-03-02T16:16:22Z</updated>\n <link href='https://arxiv.org/abs/2603.02032v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.02032v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>The dynamics and complexity of cloud-native systems present significant challenges for Root Cause Analysis (RCA). While causality-based RCA methods have shown significant progress in recent years, their practical adoption is fundamentally limited by three intertwined challenges: poor scalability against system complexity, brittle generalization across different system topologies, and inadequate integration of domain knowledge. These limitations create a vicious cycle, hindering the development of robust and efficient RCA solutions. This paper introduces MetaRCA, a generalizable RCA framework for cloud-native systems. MetaRCA first constructs a Meta Causal Graph (MCG) offline, a reusable knowledge base defined at the metadata level. To build the MCG, we propose an evidence-driven algorithm that systematically fuses knowledge from Large Language Models (LLMs), historical fault reports, and observability data. When a fault occurs, MetaRCA performs a lightweight online inference by dynamically instantiating the MCG into a localized graph based on the current context, and then leverages real-time data to weight and prune causal links for precise root cause localization. Evaluated on 252 public and 59 production failures, MetaRCA demonstrates state-of-the-art performance. It surpasses the strongest baseline by 29 percentage points in service-level and 48 percentage points in metric-level accuracy. This performance advantage widens as system complexity increases, with its overhead scaling near-linearly. Crucially, MetaRCA shows robust cross-system generalization, maintaining over 80% accuracy across diverse systems.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.SE'/>\n <published>2026-03-02T16:16:22Z</published>\n <arxiv:comment>Accepted to FSE 2026;22pages,8 figures</arxiv:comment>\n <arxiv:primary_category term='cs.SE'/>\n <author>\n <name>Shuai Liang</name>\n </author>\n <author>\n <name>Pengfei Chen</name>\n </author>\n <author>\n <name>Bozhe Tian</name>\n </author>\n <author>\n <name>Gou Tan</name>\n </author>\n <author>\n <name>Maohong Xu</name>\n </author>\n <author>\n <name>Youjun Qu</name>\n </author>\n <author>\n <name>Yahui Zhao</name>\n </author>\n <author>\n <name>Yiduo Shang</name>\n </author>\n <author>\n <name>Chongkang Tan</name>\n </author>\n </entry>"
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