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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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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.

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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.

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动态时间扭曲.有关因果推理的推理.边缘计算是一种边缘计算.高效益采矿的高效益采矿物联网的东西互联网.根源原因分析分析.

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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.