# Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data

arXiv: https://arxiv.org/abs/2302.05092 · HTML: https://mlsanity.com/paper/2302.05092/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2023.

## Built on by (most cited first, 21 of 21)

- [TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systems](https://mlsanity.com/paper/2504.20462/) (2025)
- [Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System](https://mlsanity.com/paper/2310.04701/) (2023)
- [Graph of States: Solving Abductive Tasks with Large Language Models](https://mlsanity.com/paper/2603.21250/) (2026)
- [Simplifying Root Cause Analysis in Kubernetes with StateGraph and LLM](https://mlsanity.com/paper/2506.02490/) (2025)
- [CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems](https://mlsanity.com/paper/2406.19711/) (2024)
- [Root Cause Analysis for Microservice Systems via Cascaded Conditional Learning with Hypergraphs](https://mlsanity.com/paper/2511.17566/) (2025)
- [AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study](https://mlsanity.com/paper/2305.16092/) (2023)
- [Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services](https://mlsanity.com/paper/2308.09937/) (2023)
- [Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?](https://mlsanity.com/paper/2501.02766/) (2025)
- [Bridging Temporal and Textual Modalities: A Multimodal Framework for Automated Cloud Failure Root Cause Analysis](https://mlsanity.com/paper/2601.04709/) (2026)
- [CUJBench: Benchmarking LLM-Agent on Cross-Modal Failure Diagnosis from Browser to Backend](https://mlsanity.com/paper/2604.23455/) (2026)
- [STAR: A Stage-attributed Triage and Repair framework for RCA Agents in Microservices](https://mlsanity.com/paper/2605.15581/) (2026)
- [TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices](https://mlsanity.com/paper/2605.15611/) (2026)
- [SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles](https://mlsanity.com/paper/2606.15559/) (2026)
- [Safe Remediation as Risk-Constrained Intervention Decision in Microservice Systems](https://mlsanity.com/paper/2607.20005/) (2026)
- [OrEdge: Efficient Multi-Modal Anomaly Detection in Distributed Software Systems via Orthogonal-Domain Learning](https://mlsanity.com/paper/2608.00309/) (2026)
- [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)
- [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)
- [STAR: Adaptive Spatial-Temporal Normalization for Unified Microservice Incident Management](https://mlsanity.com/paper/2609.31645/) (2026)
- [SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systems](https://mlsanity.com/paper/2610.04893/) (2026)

## Abstract (arXiv)

The complexity and dynamism of microservices pose significant challenges to system reliability, and thereby, automated troubleshooting is crucial. Effective root cause localization after anomaly detection is crucial for ensuring the reliability of microservice systems. However, two significant issues rest in existing approaches: (1) Microservices generate traces, system logs, and key performance indicators (KPIs), but existing approaches usually consider traces only, failing to understand the system fully as traces cannot depict all anomalies; (2) Troubleshooting microservices generally contains two main phases, i.e., anomaly detection and root cause localization. Existing studies regard these two phases as independent, ignoring their close correlation. Even worse, inaccurate detection results can deeply affect localization effectiveness. To overcome these limitations, we propose Eadro, the first end-to-end framework to integrate anomaly detection and root cause localization based on multi-source data for troubleshooting large-scale microservices. The key insights of Eadro are the anomaly manifestations on different data sources and the close connection between detection and localization. Thus, Eadro models intra-service behaviors and inter-service dependencies from traces, logs, and KPIs, all the while leveraging the shared knowledge of the two phases via multi-task learning. Experiments on two widely-used benchmark microservices demonstrate that Eadro outperforms state-of-the-art approaches by a large margin. The results also show the usefulness of integrating multi-source data. We also release our code and data to facilitate future research.