Multi-scale high-speed network traffic prediction using k-factor gegenbauer ARMA model
In: ICC 2004 (2004 IEEE International Conference on Communications), 2004
Konferenz
- print, 16 ref
Zugriff:
Gegenbauer autoregressive moving average (GARMA) model has the ability to capture both short- and long-range dependent characteristics of the underlying data. GARMA has been used for modeling and forecasting financial time series that exhibit long-range dependency (LRD). Since the high-speed network traffic exhibits a high degree of LRD characteristic, GARMA could be used for its modeling and prediction. In this paper, we present a simplified parameter estimation procedure and an adaptive prediction scheme for the k-factor GARMA model. The adaptation gives the model the ability to capture the non-stationary characteristic of the data. The k-factor GARMA is applied to model four different types of real traffic data: MPEG and JPEG video, Ethernet and Internet. These models are then used to predict one-step-ahead traffic value at different timescales. The results show that the estimated parameters of the k-factor GARMA model provide a detailed and accurate presentation for the traffic characteristics in both time and frequency domain. We also demonstrate that the prediction performance of the k-factor GARMA model outperforms that of the traditional autoregressive (AR) model.
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Multi-scale high-speed network traffic prediction using k-factor gegenbauer ARMA model
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Autor/in / Beteiligte Person: | SADEK, Nayera ; KHOTANZAD, Alireza |
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Zeitschrift: | ICC 2004 (2004 IEEE International Conference on Communications), 2004 |
Veröffentlichung: | Piscataway, New Jersey: IEEE, 2004 |
Medientyp: | Konferenz |
Umfang: | print, 16 ref |
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