# Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis

arXiv: https://arxiv.org/abs/2407.01710 · HTML: https://mlsanity.com/paper/2407.01710/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2024.

## Built on by (most cited first, 12 of 12)

- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought](https://mlsanity.com/paper/2508.20370/) (2025)
- [Root Cause Analysis for Microservice Systems via Cascaded Conditional Learning with Hypergraphs](https://mlsanity.com/paper/2511.17566/) (2025)
- [Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?](https://mlsanity.com/paper/2501.02766/) (2025)
- [ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC System](https://mlsanity.com/paper/2506.20673/) (2025)
- [A Feature Engineering Approach for Business Impact-Oriented Failure Detection in Distributed Instant Payment Systems](https://mlsanity.com/paper/2510.21710/) (2025)
- [SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functions](https://mlsanity.com/paper/2604.03391/) (2026)
- [UModel: An Agent-Ready Observability Data Modeling Method at Scale](https://mlsanity.com/paper/2606.04799/) (2026)
- [A Taxonomy of Runtime Faults in Model Context Protocol Servers](https://mlsanity.com/paper/2606.05339/) (2026)
- [MONO2REST: Identifying and Exposing Microservices: a Reusable RESTification Approach](https://mlsanity.com/paper/2503.21522/) (2025)
- [Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysis](https://mlsanity.com/paper/2608.21310/) (2026)
- [From General Agents to RCA Experts: A Self-Evolving Harness for Root Cause Analysis](https://mlsanity.com/paper/2608.25661/) (2026)

## 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.