Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?
Journal:
arXiv
Published Date:
Jan 6, 2025
Abstract
Graph Neural Networks (GNNs) are widely adopted for fault diagnosis in
microservice systems, premised on their ability to model service dependencies.
However, the necessity of explicit graph structures remains underexamined, as
existing evaluations conflate preprocessing with architectural contributions.
To isolate the true value of GNNs, we propose DiagMLP, a deliberately minimal,
topology-agnostic baseline that retains multimodal fusion capabilities while
excluding graph modeling. Through ablation experiments across five datasets,
DiagMLP achieves performance parity with state-of-the-art GNN-based methods in
fault detection, localization, and classification. These findings challenge the
prevailing assumption that graph structures are indispensable, revealing that:
(i) preprocessing pipelines already encode critical dependency information, and
(ii) GNN modules contribute marginally beyond multimodality fusion. Our work
advocates for systematic re-evaluation of architectural complexity and
highlights the need for standardized baseline protocols to validate model
innovations.
