LRD network traffic predicting based on SRD model

Gao, Bo; Zhang, Qinyu; Liang, Yonghui; Zhang, Naitong

doi:10.1109/wcsp.2012.6542937

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…The final predicted value ) ( t x is obtained when the predictive values of the PF component and the remainder ) (t u are summed. The prediction method in this paper employs the same data in forecasting using the EMD-ARMA [7] and WNN [8] model. To evaluate the results of the objective quantitative evaluation, this study also uses the root mean squared error (RMSE) and the relative root mean square error (RRMSE) as the evaluation criteria.…”

Section: Flashp2p Network Traffic Predictionmentioning

confidence: 99%

“…In recent work, some hybrid prediction methods have been proposed. In [6], the empirical mode decomposition (EMD) method [7] is utilized to convert a long sequence of related flows into short sequence-related traffic through wavelet analysis. Thus, the computational complexity of the prediction algorithm is reduced while accuracy is increased .…”

Section: Introductionmentioning

confidence: 99%

“…Compared to the ARMA time series model, the GARCH model can improve prediction accuracy significantly. In addition, through converting a long-related model into a short-related model, the computational complexity of the prediction algorithm can also be reduced, and the proposed algorithm achieves higher accuracy than that achieved by the EMD-ARMA model [7] and WNN (Wavelet Neural Network) model [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research of a Novel Flash P2P Network Traffic Prediction Algorithm

Yuan

Zhao

et al. 2015

Procedia Computer Science

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To improve the quality of service and network performance of the FlashP2P video-on-demand, the prediction FlashP2P network traffic flow is very useful to t control the network video traffic. In this paper, a novel prediction algorithm to forecast the traffic rate of the Flash P2P video is proposed. This method is based on the combination of the local mean decomposition (LMD) and the generalized autoregressive conditional heteroscedasticity (GARCH). LMD is used to decompose the original long-related flow into the summation of the short-related flow. Then, GRACH is utilized to predict the short-related flow. The developed algorithm is tested on a university's campus network. The predicted results show that our proposed method can achieve higher accuracy than those obtained by existing algorithms, such as EMD-ARMA(Empirical Mode Decomposition and Auto-Regressive and Moving Average Model) and WNN(Wavelet Neural Network), while keeping lower computational complexity.

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Section: Flashp2p Network Traffic Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research of a Novel Flash P2P Network Traffic Prediction Algorithm

Yuan

Zhao

et al. 2015

Procedia Computer Science

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show abstract

“…In this section, by explaining the self-similarity property of video traffic data of flash P2P theoretically, it is proved that network video traffic of flash P2P conforms to the long-range dependent model based on EMD and ARIMA using the experimental data [22][23][24][25][26][27][28][29][30][31] . Besides, the reason for the self-similarity property of video traffic of flash P2P is explained.…”

Section: Self-similar Mathematical Model Of the Flash P2p Trafficmentioning

confidence: 99%

“…Therefore, the ARIMA model is used to fit IMF. In fact, ARIMA model is the combination of differential operation and ARMA model, which means that the series after differentiation can be fitted with ARMA model as long as the traffic series achieves stationary after differentiation of an appropriate order [30] . Definition 5 ARIMA model is defined as follows.…”

Section: Proof Of Long Range Dependence Property Based On Emd and Arimamentioning

confidence: 99%