• DocumentCode
    524601
  • Title

    Failure prediction method for Network Management System by using Bayesian network and shared database

  • Author

    Harahap, Erwin ; Sakamoto, Wataru ; Nishi, Hiroaki

  • Author_Institution
    Dept. of Syst. Design Eng., Keio Univ., Yokohama, Japan
  • fYear
    2010
  • fDate
    15-18 June 2010
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Network Management System (NMS) is a service that employs a variety of tools, applications, and devices to assist network administrators on monitoring and maintaining network. Keeping the network in high quality of service is the main purpose of NMS. This paper proposed a method to solve the network problem by making a prediction of failure based on network-data behavior. The prediction represented by conditional probability generated by Bayesian network. Bayesian network is a probability graphical model for representing the probabilistic relationship among a large number of variables and doing probabilistic inference with those variables. In order to describe how the prediction works, we discuss the prediction result by simulation on network congestion.
  • Keywords
    belief networks; database management systems; fault diagnosis; inference mechanisms; probability; telecommunication computing; telecommunication network management; Bayesian network; conditional probability graphical model; failure prediction method; network management system; network-data behavior; probabilistic inference; shared database; Application software; Bayesian methods; Data engineering; Databases; Design engineering; Engineering management; Monitoring; Prediction methods; Quality of service; Systems engineering and theory; Bayes theorem; Network Management System; congestion; fault management; prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Telecommunication Technologies (APSITT), 2010 8th Asia-Pacific Symposium on
  • Conference_Location
    Kuching
  • Print_ISBN
    978-1-4244-6413-5
  • Electronic_ISBN
    978-4-88552-244-4
  • Type

    conf

  • Filename
    5532294