Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2506.20673 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908551119634432 |
|---|---|
| author | Sun, Yongqian Pan, Xijie Xiong, Xiao Tao, Lei Wang, Jiaju Zhang, Shenglin Yuan, Yuan Li, Yuqi Jian, Kunlin |
| author_facet | Sun, Yongqian Pan, Xijie Xiong, Xiao Tao, Lei Wang, Jiaju Zhang, Shenglin Yuan, Yuan Li, Yuqi Jian, Kunlin |
| contents | Network failure diagnosis is challenging yet critical for high-performance computing (HPC) systems. Existing methods cannot be directly applied to HPC scenarios due to data heterogeneity and lack of accuracy. This paper proposes a novel framework, called ClusterRCA, to localize culprit nodes and determine failure types by leveraging multimodal data. ClusterRCA extracts features from topologically connected network interface controller (NIC) pairs to analyze the diverse, multimodal data in HPC systems. To accurately localize culprit nodes and determine failure types, ClusterRCA combines classifier-based and graph-based approaches. A failure graph is constructed based on the output of the state classifier, and then it performs a customized random walk on the graph to localize the root cause. Experiments on datasets collected by a top-tier global HPC device vendor show ClusterRCA achieves high accuracy in diagnosing network failure for HPC systems. ClusterRCA also maintains robust performance across different application scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC System Sun, Yongqian Pan, Xijie Xiong, Xiao Tao, Lei Wang, Jiaju Zhang, Shenglin Yuan, Yuan Li, Yuqi Jian, Kunlin Distributed, Parallel, and Cluster Computing Artificial Intelligence Network failure diagnosis is challenging yet critical for high-performance computing (HPC) systems. Existing methods cannot be directly applied to HPC scenarios due to data heterogeneity and lack of accuracy. This paper proposes a novel framework, called ClusterRCA, to localize culprit nodes and determine failure types by leveraging multimodal data. ClusterRCA extracts features from topologically connected network interface controller (NIC) pairs to analyze the diverse, multimodal data in HPC systems. To accurately localize culprit nodes and determine failure types, ClusterRCA combines classifier-based and graph-based approaches. A failure graph is constructed based on the output of the state classifier, and then it performs a customized random walk on the graph to localize the root cause. Experiments on datasets collected by a top-tier global HPC device vendor show ClusterRCA achieves high accuracy in diagnosing network failure for HPC systems. ClusterRCA also maintains robust performance across different application scenarios. |
| title | ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC System |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2506.20673 |