Fault diagnosis in microservice systems has increasingly embraced multimodal observation data for a holistic and multifaceted view of the system, with Graph Neural Networks (GNNs) commonly employed to model complex service dependencies. However, despite the intuitive appeal, there remains a lack of compelling justification for the adoption of GNNs, as no direct evidence supports their necessity or effectiveness. To critically evaluate the current use of GNNs, we propose DiagMLP, a simple topology-agnostic baseline as a substitute for GNNs in fault diagnosis frameworks. Through experiments on five public datasets, we surprisingly find that DiagMLP performs competitively with and even outperforms GNN-based methods in fault diagnosis tasks, indicating that the current paradigm of using GNNs to model service dependencies has not yet demonstrated a tangible contribution. We further discuss potential reasons for this observation and advocate shifting the focus from solely pursuing novel model designs to developing challenging datasets, standardizing preprocessing protocols, and critically evaluating the utility of advanced deep learning modules.

本研究针对现有微服务系统故障诊断中图神经网络（GNNs）应用的有效性提出问题，开发了DiagMLP作为替代方案进行评估。实验结果表明，DiagMLP的性能不仅与基于GNN的方法相当，有时甚至超过它们，揭示了当前使用GNN建模服务依赖性所未能提供的实际贡献。研究建议将重点转向开发具有挑战性的数据集和标准化预处理协议。

图神经网络在微服务系统多模态故障诊断中的有效性研究