Name
Abstract | ||
|---|---|---|
Accurately predicting for network traffic is significant for network operation and maintenance in software-defined networks (SDN). In this paper, Multi-frequency characteristic of complex network traffic is considered, and a new algorithm named EMD-based multi-model Prediction (EMD-MMP) for network prediction is proposed. The main idea in this algorithm is to decompose the network traffic series into different modes with different frequency by Empirical Mode Decomposition (EMD). According to the characteristics and the cross correlation coefficient of the modes, we reconstruct new components for de-noising by summing up parts of the high frequency modes. Then the new components and the remaining old modes are predicted by ARMA and SVR methods. Finally, the historical traffic data of Internet2 is employed for our experiments to demonstrate the precision of our new prediction algorithm compared with the Auto-Regressive and Moving Average (ARMA) and Support Vector Regression (SVR) models. On average, the EMD-MMP method improves ARMA and SVR by 0.62% and 10.6% at the Mean Absolute Percentage Error (MAPE) statistic indicator, and the Mean Square Error (MSE) of EMD-MMP is 12060.92 while the ARMA and SVR are 13968.8 and 47588.3. Besides, the EMD-MMP algorithm gives a better understanding of the nature of the network traffic. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MASS.2014.104 | MASS |
Keywords | Field | DocType |
internet2,svr,network traffic,mape statistic indicator,sdn,arma method,mean absolute percentage error,network traffic prediction,cross correlation coefficient,regression analysis,multifrequency characteristic,mean square error,svr method,software defined networking,autoregressive moving average processes,emd,internet,software-defined network,mse,arma,empirical mode decomposition,telecommunication traffic,emd-based multimodel prediction,support vector machines,sdn, network traffic prediction, emd, arma, svr,mean square error methods | Computer science,Mean squared error,Artificial intelligence,Complex network,Distributed computing,Mean absolute percentage error,Statistic,Support vector machine,Algorithm,Software-defined networking,Moving average,Machine learning,Hilbert–Huang transform | Conference |
ISSN | Citations | PageRank |
2155-6806 | 0 | 0.34 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Longfei Dai | 1 | 0 | 0.34 |
| Wenguo Yang | 2 | 37 | 4.43 |
| Suixiang Gao | 3 | 44 | 12.48 |
| Yinben Xia | 4 | 25 | 1.89 |
| Mingming Zhu | 5 | 0 | 0.34 |
| Zhigang Ji | 6 | 0 | 1.01 |