Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis
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Abstract (arXiv)
Widely adopted for their scalability and flexibility, modern microservice systems present unique failure diagnosis challenges due to their independent deployment and dynamic interactions. This complexity can lead to cascading failures that negatively impact operational efficiency and user experience. Recognizing the critical role of fault diagnosis in improving the stability and reliability of microservice systems, researchers have conducted extensive studies and achieved a number of significant results. This survey provides an exhaustive review of 98 scientific papers from 2003 to the present, including a thorough examination and elucidation of the fundamental concepts, system architecture, and problem statement. It also includes a qualitative analysis of the dimensions, providing an in-depth discussion of current best practices and future directions, aiming to further its development and application. In addition, this survey compiles publicly available datasets, toolkits, and evaluation metrics to facilitate the selection and validation of techniques for practitioners.
Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). First posted on arXiv 27 Jun 2024.
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Citing papers per year: 2025: 7; 2026: 5.
12 papers.
- 2025A Survey of AIOps in the Era of Large Language Modelscited by 19
- 2025Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thoughtcited by 13
- 2025Root Cause Analysis for Microservice Systems via Cascaded Conditional Learning with Hypergraphscited by 2
- 2025Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?cited by 1
- 2025ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC Systemcited by 1
- 2025A Feature Engineering Approach for Business Impact-Oriented Failure Detection in Distributed Instant Payment Systemscited by 1
- 2026SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functionscited by 1
- 2026UModel: An Agent-Ready Observability Data Modeling Method at Scalecited by 1
- 2026A Taxonomy of Runtime Faults in Model Context Protocol Serverscited by 1
- 2025MONO2REST: Identifying and Exposing Microservices: a Reusable RESTification Approachcited by 0
- 2026Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysiscited by 0
- 2026From General Agents to RCA Experts: A Self-Evolving Harness for Root Cause Analysiscited by 0
- 2026From General Agents to RCA Experts: A Self-Evolving Harness for Root Cause Analysis26 Aug 2026
- 2026Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysis21 Aug 2026
- 2026A Taxonomy of Runtime Faults in Model Context Protocol Servers3 Jun 2026
- 2026UModel: An Agent-Ready Observability Data Modeling Method at Scale3 Jun 2026
- 2026SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functions3 Apr 2026
- 2025Root Cause Analysis for Microservice Systems via Cascaded Conditional Learning with Hypergraphs14 Nov 2025
- 2025Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought28 Aug 2025
- 2025A Feature Engineering Approach for Business Impact-Oriented Failure Detection in Distributed Instant Payment Systems7 Aug 2025
- 2025A Survey of AIOps in the Era of Large Language Models23 Jun 2025
- 2025ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC System17 Jun 2025
- 2025MONO2REST: Identifying and Exposing Microservices: a Reusable RESTification Approach27 Mar 2025
- 2025Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?6 Jan 2025