# A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends

arXiv: https://arxiv.org/abs/2408.00803 · HTML: https://mlsanity.com/paper/2408.00803/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 125 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 21 of 21)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Automatic Root Cause Analysis via Large Language Models for Cloud Incidents](https://mlsanity.com/paper/2305.15778/) (2023)
- [Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition](https://mlsanity.com/paper/2206.05871/) (2022)
- [Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Survey](https://mlsanity.com/paper/2105.12378/) (2021)
- [MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems](https://mlsanity.com/paper/2103.01782/) (2021)
- [CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms](https://mlsanity.com/paper/2111.03753/) (2021)
- [Robust Failure Diagnosis of Microservice System through Multimodal Data](https://mlsanity.com/paper/2302.10512/) (2023)
- [Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems](https://mlsanity.com/paper/2207.09021/) (2022)
- [Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization](https://mlsanity.com/paper/2302.01987/) (2023)
- [DeCaf: Diagnosing and Triaging Performance Issues in Large-Scale Cloud Services](https://mlsanity.com/paper/1910.05339/) (2019)
- [Survey on Models and Techniques for Root-Cause Analysis](https://mlsanity.com/paper/1701.08546/) (2017)
- [Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings](https://mlsanity.com/paper/2108.00344/) (2021)
- [AutoTSG: Learning and Synthesis for Incident Troubleshooting](https://mlsanity.com/paper/2205.13457/) (2022)
- [Applications of statistical causal inference in software engineering](https://mlsanity.com/paper/2211.11482/) (2022)
- [CausIL: Causal Graph for Instance Level Microservice Data](https://mlsanity.com/paper/2303.00554/) (2023)
- [Localizing Faults in Cloud Systems](https://mlsanity.com/paper/1803.00356/) (2018)
- [Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps](https://mlsanity.com/paper/2204.11598/) (2022)
- [SoftNER: Mining Knowledge Graphs From Cloud Incidents](https://mlsanity.com/paper/2101.05961/) (2021)
- [Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment](https://mlsanity.com/paper/1911.01225/) (2019)
- [Spectrum-Based Log Diagnosis](https://mlsanity.com/paper/2008.06948/) (2020)
- [BALANCE: Bayesian Linear Attribution for Root Cause Localization](https://mlsanity.com/paper/2301.13572/) (2023)

## Built on by (most cited first, 10 of 10)

- [SDVDiag: A Modular Platform for the Diagnosis of Connected Vehicle Functions](https://mlsanity.com/paper/2507.19403/) (2025)
- [AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis](https://mlsanity.com/paper/2603.03378/) (2026)
- [SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functions](https://mlsanity.com/paper/2604.03391/) (2026)
- [TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices](https://mlsanity.com/paper/2605.15611/) (2026)
- [PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis](https://mlsanity.com/paper/2606.00582/) (2026)
- [Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models](https://mlsanity.com/paper/2509.02535/) (2025)
- [Agentic Diagnostic Reasoning over Telecom and Datacenter Infrastructure](https://mlsanity.com/paper/2601.07342/) (2026)
- [LATS-RCA: Language Agent Tree Search for Root Cause Analysis in Microservices](https://mlsanity.com/paper/2605.03505/) (2026)
- [SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles](https://mlsanity.com/paper/2606.15559/) (2026)
- [A Topology-Aware, Memory-Centric Architecture that Separates Root-Cause Derivation from Root-Cause Explanation](https://mlsanity.com/paper/2606.20758/) (2026)

## Abstract (arXiv)

The complex dependencies and propagative faults inherent in microservices, characterized by a dense network of interconnected services, pose significant challenges in identifying the underlying causes of issues. Prompt identification and resolution of disruptive problems are crucial to ensure rapid recovery and maintain system stability. Numerous methodologies have emerged to address this challenge, primarily focusing on diagnosing failures through symptomatic data. This survey aims to provide a comprehensive, structured review of root cause analysis (RCA) techniques within microservices, exploring methodologies that include metrics, traces, logs, and multi-model data. It delves deeper into the methodologies, challenges, and future trends within microservices architectures. Positioned at the forefront of AI and automation advancements, it offers guidance for future research directions.