一句话：提出 HyperRCA，以超图神经网络建模微服务异构关系，实现主机、服务、实例的多粒度根因定位。
微服务架构中组件关系日益复杂，给系统鲁棒性维护带来挑战；已有的基于图神经网络的多模态数据方法存在两点不足：一是简单图难以表达部署、从属、依赖等一对多关系，二是难以同时对主机、服务、实例三个粒度上的故障进行定位。为此，作者提出 HyperRCA：以实例为超图节点对故障期间的系统状态建模，并设计三类超边——部署超边刻画基础设施关系、从属超边刻画服务层次、依赖超边刻画组件间交互，从而显式捕获异构关系；随后利用超图神经网络与多层感知机，基于超边特征训练根因定位模型，实现多粒度根因定位。实验表明其性能显著优于现有最先进方法：在单粒度数据集上 HR@5 最高提升 112.62%，在多粒度场景下最高提升 466.43%。
Modern enterprises are increasingly adopting microservice architectures to enhance system flexibility and scalability. However, in the face of ever-changing business requirements, the relationships between system components have become increasingly complex, resulting in significant challenges in maintaining system robustness. In recent years, multimodal data-driven approaches based on graph neural networks have emerged as a predominant solution for root cause localization in microservice systems. Our detailed analysis of architectural characteristics and existing research reveals two critical limitations. First, simple graph is insufficient to represent the one-to-many relationships inherent in microservice component interactions, such as deployment, subordinate, and dependency. Second, the current multimodal data-based method has difficulty in performing localization on faults occurring on hosts, services, and instances at the same time.To address these challenges, we propose HyperRCA, a novel multi-granular root cause analysis approach based on hypergraph neural networks. Our approach models system states during faults via a hypergraph with instances as graph nodes, explicitly capturing heterogeneous relationships through three innovative hyperedge designs: deployment hyperedges for infrastructure relationships, subordinate hyperedges for service hierarchies, and dependency hyperedges for inter-component interactions. We used hypergraph neural networks and multi-layer perceptrons to train a root cause localization model based on hyperedge features to achieve multi-granularity root cause localization. Experimental evaluations demonstrate significant performance improvements over state-of-the-art approaches. HyperRCA achieves a maximum HR@5 improvement of 112.62% on single-granularity datasets and 466.43% in multi-granularity scenarios.
DOI 原文 ·
@inproceedings{paperbot9194,
title = {Hypergraph Neural Network-based Multi-Granular Root Cause Localization for Microservice Systems},
author = {Yaxiao Li and Lu Wang and Chenxi Zhang and Qingshan Li and Siming Rong and Baiyang Wen and Xuyang Li and Kun Ma and Quanwei Du and KeYang Li and Lingfeng Pan and Xinyue Li and Mingxuan Hui},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00099}
}