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首页> 外文期刊>IIE Transactions >Integration of sparse singular vector decomposition and statistical process control for traffic monitoring and quality of service improvement in mission-critical communication networks
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Integration of sparse singular vector decomposition and statistical process control for traffic monitoring and quality of service improvement in mission-critical communication networks

机译：稀疏奇异矢量分解和统计过程控制的集成，用于关键任务通信网络中的流量监控和服务质量改善

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摘要

Mission-Critical Communication Networks (MCCNs) are wireless networks whose malfunction can cause significant problems. The nature of MCCNs puts an extremely high standard on the Quality of Service (QoS). QoS assurance starts from monitoring and change/anomaly detection of network packets data. This problem has been primarily studied by the research community of communication networks, in which the existing methods fall short for providing a privacy-preserving, minimum-disruption, global monitoring tool. Another relevant research area is Multivariate Statistical Process Control (MSPC), in which generic methods have been developed for monitoring high-dimensional data streams. These methods do not account for the special data distribution and correlation structure of packet streams. Nor are they efficient enough to suit real-time analytics in MCCNs. We propose a method called Sparse Singular Value Decomposition (SSVD)-MSPC. SSVD-MSPC addresses the aforementioned limitations and additionally provides key capabilities toward QoS improvement, including monitoring, fault identification, and fault characterization. Extensive case studies are conducted for MCCNs that experience faults of different magnitudes and various temporal shapes. SSVD-MSPC achieves good performance across the different settings in comparison with existing methods.
机译：关键任务通信网络（MCCN）是其故障可能导致严重问题的无线网络。 MCCN的性质为服务质量（QoS）提出了极高的标准。 QoS保证从监视和更改/异常检测网络数据包数据开始。这个问题已经由通信网络的研究团体进行了主要研究，其中现有的方法不足以提供保护隐私，最小干扰的全局监视工具。另一个相关的研究领域是多元统计过程控制（MSPC），其中已开发出通用方法来监视高维数据流。这些方法不考虑分组流的特殊数据分配和相关结构。它们的效率也不足以适应MCCN中的实时分析。我们提出了一种称为稀疏奇异值分解（SSVD）-MSPC的方法。 SSVD-MSPC解决了上述限制，并另外提供了用于QoS改善的关键功能，包括监视，故障识别和故障表征。针对经历不同大小和不同时间形状的断层的MCCN进行了广泛的案例研究。与现有方法相比，SSVD-MSPC在不同设置下均具有良好的性能。

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  • 来源
    《IIE Transactions》 |2018年第12期|1104-1116|共13页
  • 作者

    Kun Wang; Jing Li;

  • 作者单位

    Industrial Engineering, School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA;

    Industrial Engineering, School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Communication networks; quality of service; traffic monitoring; SPC; SVD; sparse learning;

    机译：通讯网络;服务质量;交通监控;SPC;SVD;稀疏学习;

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