# CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems

arXiv: https://arxiv.org/abs/2406.19711 · HTML: https://mlsanity.com/paper/2406.19711/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [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)
- [CausalRCA: Causal Inference based Precise Fine-grained Root Cause Localization for Microservice Applications](https://mlsanity.com/paper/2209.02500/) (2022)
- [Robust Failure Diagnosis of Microservice System through Multimodal Data](https://mlsanity.com/paper/2302.10512/) (2023)
- [Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data](https://mlsanity.com/paper/2302.05092/) (2023)
- [Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization](https://mlsanity.com/paper/2302.01987/) (2023)
- [Survey on Models and Techniques for Root-Cause Analysis](https://mlsanity.com/paper/1701.08546/) (2017)
- [An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning](https://mlsanity.com/paper/2206.03420/) (2022)
- [Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning](https://mlsanity.com/paper/2309.02124/) (2023)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and robustness. Due to the complex service invocation paths and dependencies in enterprise-level microservice systems, it is challenging to locate the anomalies promptly during service invocations, thus causing intractable issues for normal system operations and maintenance. In this paper, we propose a Causal Heterogeneous grAph baSed framEwork for root cause analysis, namely CHASE, for microservice systems with multimodal data, including traces, logs, and system monitoring metrics. Specifically, related information is encoded into representative embeddings and further modeled by a multimodal invocation graph. Following that, anomaly detection is performed on each instance node with attentive heterogeneous message passing from its adjacent metric and log nodes. Finally, CHASE learns from the constructed hypergraph with hyperedges representing the flow of causality and performs root cause localization. We evaluate the proposed framework on two public microservice datasets with distinct attributes and compare with the state-of-the-art methods. The results show that CHASE achieves the average performance gain up to 36.2%(A@1) and 29.4%(Percentage@1), respectively to its best counterpart.