• DocumentCode
    2450040
  • Title

    An Engineering Approach to Prediction of Network Traffic Based on Time-Series Model

  • Author

    Shen Fu-Ke ; Zhang Wei ; Chang Pan

  • Author_Institution
    Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China
  • fYear
    2009
  • fDate
    25-26 April 2009
  • Firstpage
    432
  • Lastpage
    435
  • Abstract
    Campus networkpsilas Internet accessing traffic is complicated, non-linear and periodical. Our goal is to give out a engineering approach to prediction of network traffic based time-series analysis model (EPTS) for campus exit-link. In our EPTS with rate-limiting, we configure rate limit based interface, then use time-series decomposed model, give out the linear trend component, periodical component, and random component decomposed analysis model. We analyze two yearspsila traffic data of ECNU campus network exit-link and try to forecast the same linkpsilas traffic tendency of the following half year. We get the satisfied prediction results compared with either the linkpsilas real monitor data or time-series analysis model without rate-limiting. We believe our approach is a feasible method for forecasting network traffic tendency.
  • Keywords
    Internet; random processes; telecommunication network planning; telecommunication traffic; time series; EPTS model; Internet; campus network exit-link traffic prediction; engineering approach; network resource planning; network traffic forecasting; random component decomposed analysis model; rate limit-based interface; time-series model; Artificial intelligence; Bandwidth; Communication system traffic control; Computer crime; Computer science; Monitoring; Predictive models; Telecommunication traffic; Time series analysis; Traffic control; Time Series; rate-limiting; traffic forecast;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence, 2009. JCAI '09. International Joint Conference on
  • Conference_Location
    Hainan Island
  • Print_ISBN
    978-0-7695-3615-6
  • Type

    conf

  • DOI
    10.1109/JCAI.2009.104
  • Filename
    5159034