DocumentCode :
263535
Title :
EMD-Based Multi-Model Prediction for Network Traffic in Software-Defined Networks
Author :
Longfei Dai ; Wenguo Yang ; Suixiang Gao ; Yinben Xia ; Mingming Zhu ; Zhigang Ji
Author_Institution :
Sch. of Math. Sci., Univ. of Chinese Acad. of Sci., Beijing, China
fYear :
2014
fDate :
28-30 Oct. 2014
Firstpage :
539
Lastpage :
544
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.
Keywords :
Internet; autoregressive moving average processes; mean square error methods; regression analysis; software defined networking; support vector machines; telecommunication traffic; ARMA method; EMD-based multimodel prediction; Internet2; MAPE statistic indicator; MSE; SDN; SVR method; cross correlation coefficient; empirical mode decomposition; mean absolute percentage error; mean square error; multifrequency characteristic; network traffic; software-defined network; Algorithm design and analysis; Correlation; Correlation coefficient; Forecasting; Prediction algorithms; Telecommunication traffic; Time series analysis; ARMA; EMD; Network Traffic Prediction; SDN; SVR;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mobile Ad Hoc and Sensor Systems (MASS), 2014 IEEE 11th International Conference on
Conference_Location :
Philadelphia, PA
Print_ISBN :
978-1-4799-6035-4
Type :
conf
DOI :
10.1109/MASS.2014.104
Filename :
7035741
Link To Document :
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