# BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection

arXiv: https://arxiv.org/abs/2405.09330 · HTML: https://mlsanity.com/paper/2405.09330/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2024.

## Built on by (most cited first, 22 of 22)

- [LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis](https://mlsanity.com/paper/2406.05375/) (2024)
- [Robust Root Cause Diagnosis using In-Distribution Interventions](https://mlsanity.com/paper/2505.00930/) (2025)
- [Root Cause Analysis of Outliers in Unknown Cyclic Graphs](https://mlsanity.com/paper/2510.06995/) (2025)
- [GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks](https://mlsanity.com/paper/2504.00058/) (2025)
- [Root Cause Analysis for Microservice Systems via Cascaded Conditional Learning with Hypergraphs](https://mlsanity.com/paper/2511.17566/) (2025)
- [Online Multi-modal Root Cause Identification in Microservice Systems](https://mlsanity.com/paper/2410.10021/) (2024)
- [OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies](https://mlsanity.com/paper/2510.13561/) (2025)
- [Bridging Temporal and Textual Modalities: A Multimodal Framework for Automated Cloud Failure Root Cause Analysis](https://mlsanity.com/paper/2601.04709/) (2026)
- [EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems](https://mlsanity.com/paper/2605.00936/) (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)
- [What a Pooled Leaderboard Does Not Say: Auditing the Scope and Provenance of Method Comparisons in Microservice RCA Benchmarks](https://mlsanity.com/paper/2606.29159/) (2026)
- [Root Cause Analysis of Measurement and Mechanistic Anomalies](https://mlsanity.com/paper/2601.23026/) (2026)
- [PRIM: Meta-Learned Bayesian Root Cause Analysis](https://mlsanity.com/paper/2605.08786/) (2026)
- [StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis](https://mlsanity.com/paper/2606.05636/) (2026)
- [Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks](https://mlsanity.com/paper/2606.13532/) (2026)
- [OrEdge: Efficient Multi-Modal Anomaly Detection in Distributed Software Systems via Orthogonal-Domain Learning](https://mlsanity.com/paper/2608.00309/) (2026)
- [From General Agents to RCA Experts: A Self-Evolving Harness for Root Cause Analysis](https://mlsanity.com/paper/2608.25661/) (2026)
- [Does Graph Structure Earn Its Place in Microservice Root-Cause Analysis? A Controlled Study on RCAEval, and What the Benchmark Was Really Measuring](https://mlsanity.com/paper/2609.27069/) (2026)
- [Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition](https://mlsanity.com/paper/2609.36686/) (2026)
- [Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models](https://mlsanity.com/paper/2609.36771/) (2026)
- [SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systems](https://mlsanity.com/paper/2610.04893/) (2026)

## Abstract (arXiv)

Detecting failures and identifying their root causes promptly and accurately is crucial for ensuring the availability of microservice systems. A typical failure troubleshooting pipeline for microservices consists of two phases: anomaly detection and root cause analysis. While various existing works on root cause analysis require accurate anomaly detection, there is no guarantee of accurate estimation with anomaly detection techniques. Inaccurate anomaly detection results can significantly affect the root cause localization results. To address this challenge, we propose BARO, an end-to-end approach that integrates anomaly detection and root cause analysis for effectively troubleshooting failures in microservice systems. BARO leverages the Multivariate Bayesian Online Change Point Detection technique to model the dependency within multivariate time-series metrics data, enabling it to detect anomalies more accurately. BARO also incorporates a novel nonparametric statistical hypothesis testing technique for robustly identifying root causes, which is less sensitive to the accuracy of anomaly detection compared to existing works. Our comprehensive experiments conducted on three popular benchmark microservice systems demonstrate that BARO consistently outperforms state-of-the-art approaches in both anomaly detection and root cause analysis.