Integration of sparse singular vector decomposition and statistical process control for traffic monitoring and quality of service improvement in mission-critical communication networks

Kun Wang, Jing Li

    Research output: Contribution to journalArticle

    Abstract

    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.

    Original languageEnglish (US)
    Pages (from-to)1104-1116
    Number of pages13
    JournalIISE Transactions
    Volume50
    Issue number12
    DOIs
    StatePublished - Dec 2 2018

    Fingerprint

    Statistical process control
    Telecommunication traffic
    Telecommunication networks
    Quality of service
    Singular value decomposition
    Monitoring
    Packet networks
    Wireless networks

    Keywords

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

    ASJC Scopus subject areas

    • Industrial and Manufacturing Engineering

    Cite this

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    abstract = "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.",
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