.png){kind=icon}
Return
FNoDe: Faulty Node Detection in Microservices Architecture
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-02215-8_6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As cloud services shift from monolithic architectures to microservices, post-failure fault and anomaly detection becomes increasingly challenging due to cascading effects across interdependent services and the overwhelming volume of heterogeneous logs and metrics. We propose FNoDe (Faulty Node Detection), a framework that integrates application logs, performance metrics, and distributed traces into a unified graph structure to detect both the root cause and type of anomaly. By leveraging a graph convolutional network (GCN), FNoDe learns system representations under normal and anomalous states from historical microservice data and uses these embeddings to classify new system states. Evaluated on five public benchmarks and two in-house microservice systems, FNoDe outperforms traditional methods by 20-30% in accuracy and maintains competitive performance with state-of-the-art frameworks, while also offering interpretability through XAI techniques.
Keywords:
DIAGNOSIS
Journal
B
IF:
0
Papers:
26
Citations:
0
Organization
Cited Papers
ShareSave
ShareSaveShareSaveShareSaveAnomaly Detection Using Program Control Flow Graph Mining From Execution Logs.png)
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIF0
PREAI
ShareSave
View more