# CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms

arXiv: https://arxiv.org/abs/2111.03753 · HTML: https://mlsanity.com/paper/2111.03753/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series](https://mlsanity.com/paper/1812.01767/) (2018)
- [Automatic Anomaly Detection in the Cloud Via Statistical Learning](https://mlsanity.com/paper/1704.07706/) (2017)
- [A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition](https://mlsanity.com/paper/2109.08800/) (2021)

## Built on by (most cited first, 24 of 24)

- [Foundation Models for Time Series Analysis: A Tutorial and Survey](https://mlsanity.com/paper/2403.14735/) (2024)
- [RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models](https://mlsanity.com/paper/2310.16340/) (2023)
- [TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis](https://mlsanity.com/paper/2210.09693/) (2022)
- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [ADGym: Design Choices for Deep Anomaly Detection](https://mlsanity.com/paper/2309.15376/) (2023)
- [A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends](https://mlsanity.com/paper/2408.00803/) (2024)
- [LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection](https://mlsanity.com/paper/2310.05668/) (2023)
- [Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection](https://mlsanity.com/paper/2206.02956/) (2022)
- [BALANCE: Bayesian Linear Attribution for Root Cause Localization](https://mlsanity.com/paper/2301.13572/) (2023)
- [Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection](https://mlsanity.com/paper/2311.16191/) (2023)
- [Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels](https://mlsanity.com/paper/2501.11959/) (2025)
- [Post-Training in Time Series Foundation Models: A Unifying Framework](https://mlsanity.com/paper/2607.20002/) (2026)
- [NetRCA: An Effective Network Fault Cause Localization Algorithm](https://mlsanity.com/paper/2202.11269/) (2022)
- [SLIM: a Scalable Light-weight Root Cause Analysis for Imbalanced Data in Microservice](https://mlsanity.com/paper/2405.20848/) (2024)
- [CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems](https://mlsanity.com/paper/2406.19711/) (2024)
- [A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management](https://mlsanity.com/paper/2407.14532/) (2024)
- [Cluster-Wide Task Slowdown Detection in Cloud System](https://mlsanity.com/paper/2408.04236/) (2024)
- [PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis](https://mlsanity.com/paper/2512.22113/) (2025)
- [KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networks](https://mlsanity.com/paper/2402.13264/) (2024)
- [Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?](https://mlsanity.com/paper/2501.02766/) (2025)
- [Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models](https://mlsanity.com/paper/2503.06413/) (2025)
- [ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC System](https://mlsanity.com/paper/2506.20673/) (2025)
- [Bridging Temporal and Textual Modalities: A Multimodal Framework for Automated Cloud Failure Root Cause Analysis](https://mlsanity.com/paper/2601.04709/) (2026)
- [CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency](https://mlsanity.com/paper/2607.08555/) (2026)

## Abstract (arXiv)

As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud. However, root cause analysis in these platforms is non-trivial due to the complicated system architecture. In this paper, we propose a root cause analysis framework called CloudRCA which makes use of heterogeneous multi-source data including Key Performance Indicators (KPIs), logs, as well as topology, and extracts important features via state-of-the-art anomaly detection and log analysis techniques. The engineered features are then utilized in a Knowledge-informed Hierarchical Bayesian Network (KHBN) model to infer root causes with high accuracy and efficiency. Ablation study and comprehensive experimental comparisons demonstrate that, compared to existing frameworks, CloudRCA 1) consistently outperforms existing approaches in f1-score across different cloud systems; 2) can handle novel types of root causes thanks to the hierarchical structure of KHBN; 3) performs more robustly with respect to algorithmic configurations; and 4) scales more favorably in the data and feature sizes. Experiments also show that a cross-platform transfer learning mechanism can be adopted to further improve the accuracy by more than 10%. CloudRCA has been integrated into the diagnosis system of Alibaba Cloud and employed in three typical cloud computing platforms including MaxCompute, Realtime Compute and Hologres. It saves Site Reliability Engineers (SREs) more than 20% in the time spent on resolving failures in the past twelve months and improves service reliability significantly.