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<p>Server performance anomaly detection is a critical task in data science and system management, aiming to identify unusual patterns in server performance metrics. However, server data often exhibits high dimensionality and complexity, which makes it challenging to detect subtle and rare anomalies. Accurately modeling the relationships between various performance indicators, such as CPU load, memory usage, and network bandwidth, is especially difficult. Moreover, the scarcity of labeled anomaly data for training supervised models can limit the effectiveness of traditional anomaly detection methods. To address these challenges, we propose a novel method called POINGRAPH-AE(Poincare Embedding&#xa0;+&#xa0;Graph Attention Networks&#xa0;+&#xa0;Auto Encoder). It enhances anomaly detection performance by embedding server performance data into a graph structure, allowing us to capture both spatial and dependencies more effectively. Additionally, we incorporate a gain factor to improve edge weight modeling, ensuring that spatial relationships are preserved. Experimental results demonstrate that optimizing edge weights significantly improves detection performance, outperforming traditional methods. We propose an edge weight-based analysis method using graph neural networks, which provides interpretable detection results and offers insights into the anomaly’s context. The proposed method is shown to be robust and scalable, making it well-suited for detecting anomalies in complex, multimodal server performance data.</p>

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Optimized edge weighting in graph neural networks for server performance anomaly detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月12日

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  • Hao Zhang,
  • Huahu Xu,
  • Jiangang Shi,
  • Xinhua Lin,
  • Yiqin Gao,
  • Yuzhe Huang

摘要

Server performance anomaly detection is a critical task in data science and system management, aiming to identify unusual patterns in server performance metrics. However, server data often exhibits high dimensionality and complexity, which makes it challenging to detect subtle and rare anomalies. Accurately modeling the relationships between various performance indicators, such as CPU load, memory usage, and network bandwidth, is especially difficult. Moreover, the scarcity of labeled anomaly data for training supervised models can limit the effectiveness of traditional anomaly detection methods. To address these challenges, we propose a novel method called POINGRAPH-AE(Poincare Embedding + Graph Attention Networks + Auto Encoder). It enhances anomaly detection performance by embedding server performance data into a graph structure, allowing us to capture both spatial and dependencies more effectively. Additionally, we incorporate a gain factor to improve edge weight modeling, ensuring that spatial relationships are preserved. Experimental results demonstrate that optimizing edge weights significantly improves detection performance, outperforming traditional methods. We propose an edge weight-based analysis method using graph neural networks, which provides interpretable detection results and offers insights into the anomaly’s context. The proposed method is shown to be robust and scalable, making it well-suited for detecting anomalies in complex, multimodal server performance data.