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

@article{5b9ba8df8fc24c24be68435b3942a11f,
title = "Integration of sparse singular vector decomposition and statistical process control for traffic monitoring and quality of service improvement in mission-critical communication networks",
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.",
keywords = "Communication networks, quality of service, sparse learning, SPC, SVD, traffic monitoring",
author = "Kun Wang and Jing Li",
year = "2018",
month = "12",
day = "2",
doi = "10.1080/24725854.2018.1474300",
language = "English (US)",
volume = "50",
pages = "1104--1116",
journal = "IISE Transactions",
issn = "2472-5854",
publisher = "Taylor and Francis Ltd.",
number = "12",

}

TY - JOUR

T1 - Integration of sparse singular vector decomposition and statistical process control for traffic monitoring and quality of service improvement in mission-critical communication networks

AU - Wang, Kun

AU - Li, Jing

PY - 2018/12/2

Y1 - 2018/12/2

N2 - 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.

AB - 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.

KW - Communication networks

KW - quality of service

KW - sparse learning

KW - SPC

KW - SVD

KW - traffic monitoring

UR - http://www.scopus.com/inward/record.url?scp=85058695099&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058695099&partnerID=8YFLogxK

U2 - 10.1080/24725854.2018.1474300

DO - 10.1080/24725854.2018.1474300

M3 - Article

AN - SCOPUS:85058695099

VL - 50

SP - 1104

EP - 1116

JO - IISE Transactions

JF - IISE Transactions

SN - 2472-5854

IS - 12

ER -