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
Link To Document :
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