ST-GraphRCA:用于物联网边缘计算中的空间时间图谱传播的根本原因分析模型
Tianyi Su1, Ruibing Mo2, Yanyu Gong2
1Department of Electrical and Information Engineering, Shandong University of Science and Technology, Jinan 250031, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
一个新的时空图形传播模型,ST-GraphRCA,有效地识别了物联网 (IoT) 边缘微服务中异常的根本原因. 它在复杂的分布式系统中显著提高了故障定位速度和准确性.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
- 人工智能的人工智能
背景情况:
- 大规模的物联网 (IoT) 数据需要在边缘分布式微服务.
- 识别这些边缘微服务中的异常根源是一个重大的运营挑战.
研究的目的:
- 提出一种新的时空图形传播模型,ST-GraphRCA,用于物联网边缘环境中的根本原因分析.
- 解决时间序列异步和高效故障定位的挑战.
主要方法:
- 开发了一种PCA-DTW混合特征提取,用于异步时间序列数据的动态对齐.
- 使用流量保护原则构建了一个基于流的向前传播图.
- 综合动态边缘权重和异常分数用于因果推理和故障路径推断.
- 采用拓受约束的高实用性挖掘算法来过根源候选者.
主要成果:
- ST-GraphRCA获得了0.89的F1-Score,超过了现有的方法.
- 在资源受限的边缘场景中显示了238.8ms的平均本地化时间,这是六倍的改进.
- 成功过不可到达的服务组合,以识别低频,高风险的根本原因.
结论:
- 在大型物联网系统中,ST-GraphRCA为异常故障追踪提供了有效的解决方案.
- 为分布式微服务系统的智能操作和维护提供技术支持.
- 为边缘计算异常检测验证时空图形传播的有效性.
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