Summary of Kgroot: Enhancing Root Cause Analysis Through Knowledge Graphs and Graph Convolutional Neural Networks, by Tingting Wang et al.
KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networks
by Tingting Wang, Guilin Qi, Tianxing Wu
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a method for automatic fault localization in online micro-service systems, which is crucial for minimizing fault detection and repair time. The existing manual approach relies on experience, leading to inefficiencies and inaccuracies. KGroot, a proposed system, leverages event knowledge and correlation analysis using graph convolutional networks (GCNs) to identify the root cause of faults. By integrating knowledge graphs with GCNs, FEKG constructs an online graph in real-time and compares it with historical data for accurate fault localization. The paper demonstrates the effectiveness of KGroot in pinpointing fault types with 93.5% accuracy, surpassing state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fault localization is a challenging task in online micro-service systems due to the vast amount of monitoring data and complex interdependencies between services and components. Faults can trigger a cascade of alerts, making it difficult to quickly identify the root cause. Existing manual methods rely on experience, which is unreliable and lacks automation. KGroot uses event knowledge and correlation analysis to automatically identify fault events and propagation paths, achieving high accuracy in pinpointing fault types. |
