# Robust Multimodal Failure Detection for Microservice Systems

arXiv: https://arxiv.org/abs/2305.18985 · HTML: https://mlsanity.com/paper/2305.18985/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2023.

## Built on by (most cited first, 13 of 13)

- [ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2307.00754/) (2023)
- [AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds](https://mlsanity.com/paper/2501.06706/) (2025)
- [Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective](https://mlsanity.com/paper/2402.02820/) (2024)
- [Xpert: Empowering Incident Management with Query Recommendations via Large Language Models](https://mlsanity.com/paper/2312.11988/) (2023)
- [Building AI Agents for Autonomous Clouds: Challenges and Design Principles](https://mlsanity.com/paper/2407.12165/) (2024)
- [Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System](https://mlsanity.com/paper/2310.04701/) (2023)
- [Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features](https://mlsanity.com/paper/2307.10869/) (2023)
- [RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems](https://mlsanity.com/paper/2503.04252/) (2025)
- [Synthetic Time Series for Anomaly Detection in Cloud Microservices](https://mlsanity.com/paper/2408.00006/) (2024)
- [Contextual and Seasonal LSTMs for Time Series Anomaly Detection](https://mlsanity.com/paper/2602.09690/) (2026)
- [ARMOR: A Robust Self-Supervised Framework for Root Cause Analysis in Microservices under Missing Modality](https://mlsanity.com/paper/2603.25538/) (2026)
- [KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI](https://mlsanity.com/paper/2607.01788/) (2026)
- [OrEdge: Efficient Multi-Modal Anomaly Detection in Distributed Software Systems via Orthogonal-Domain Learning](https://mlsanity.com/paper/2608.00309/) (2026)

## Abstract (arXiv)

Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's performance. Over the years, many single-modal (i.e., metrics, logs, or traces) data-based nomaly detection methods have been proposed. However, they tend to miss a large number of failures and generate numerous false alarms because they ignore the correlation of multimodal data. In this work, we propose AnoFusion, an unsupervised failure detection approach, to proactively detect instance failures through multimodal data for microservice systems. It applies a Graph Transformer Network (GTN) to learn the correlation of the heterogeneous multimodal data and integrates a Graph Attention Network (GAT) with Gated Recurrent Unit (GRU) to address the challenges introduced by dynamically changing multimodal data. We evaluate the performance of AnoFusion through two datasets, demonstrating that it achieves the F1-score of 0.857 and 0.922, respectively, outperforming the state-of-the-art failure detection approaches.