Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (5): 289-293.doi: 10.11896/jsjkx.200400056

• Computer Network • Previous Articles     Next Articles

Chaotic Prediction Model of Network Traffic for Massive Data

XIANG Chang-sheng1, CHEN Zhi-gang2   

  1. 1 School of Computer and Communication,Hunan Institute of Engineering,Xiangtan,Hunan 411104,China
    2 School of Computer Science and Engineering,Central South University,Changsha 410000,China
  • Received:2020-04-14 Revised:2020-09-02 Online:2021-05-15 Published:2021-05-09
  • About author:XIANG Chang-sheng,born in 1971,Ph.D,associate professor.His main research interests include artificial intelligence,data mining and machine lear-ning.(13077331687@163.com)
    CHEN Zhi-gang,born in 1964,professor,Ph.D supervisor,is a senior member of China Computer Federation.His main research interests include cluster computing,computer security,wireless networks,parallel and distributed system,etc.
  • Supported by:
    National Natural Science Foundation of China(61672540),Natural Science Foundation of Hunan Province,China(2018JJ2082) and Outstanding Young Scholars Program of Hunan Provincial Education Department,2018(18B386).

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Abstract: Aiming at the chaotic and massive characteristics of network traffic,in order to make up for the shortcomings of network traffic prediction model to obtain better network traffic prediction results,a chaotic network traffic prediction model for massive data is proposed.First,wavelet analysis is used to deal with the original network traffic time series in multi-scale to obtain network traffic components with different characteristics.Then,the chaotic characteristics of network traffic components are analyzed and reconstructed respectively.The extreme learning machine in machine learning algorithm is used to model and predict.Finally,wavelet analysis is used to overlay the prediction results of network traffic components to get the original network traffic data prediction value,and the network traffic prediction simulation experiment is carried out.Experimental results show that,compared with other network traffic prediction models,the network traffic prediction accuracy of the proposed model is more than 90%,and the network traffic prediction results are more stable.It is an effective tool for network traffic modeling and prediction.

Key words: Extreme learning machine, Massive features, Modeling and prediction, Network traffic, Simulation test, Wavelet analysis

CLC Number: 

  • TP181
[1]VELAN P,CERMAK M,CELEDA P,et al.A survey of me-thods for encrypted traffic classification and analysis[J].International Journal of Network Management,2015,25(5):355-374.
[2]LI W X,QI H,XU R H,et al.Research progress and trend of data center network traffic scheduling [J].Chinese Journal of Computers,2020,43(4):600-617.
[3]LV N,ZHOU J X,FENG X,et al.A time enhanced airborne network traffic identification method [J].Journal of Northwest Polytechnic University,2020,38(2):341-350.
[4]LOTFOLLAHI M,SIAVOSHANI M J,ZADE R S H,et al.Deep packet:A novel approach for encrypted traffic classification using deep learning [J].Soft Computing,2017,28(9):1-14.
[5]ZHANG J,BAI G W,SHA X L,et al.Mobile network traffic prediction model based on spatiotemporal characteristics [J].Computer Science,2019,46(12):108-113.
[6]GUO J,YU Y B,YANG C Y.Multi step network traffic prediction based on total attention mechanism [J].Signal Processing,2019,35(5):758-767.
[7]LI S,ZHOU Y T,CHI Y,et al.Application of Gaussian process mixture model to network traffic prediction [J].Computer Engineering and Applications,2020,56(5):186-193.
[8]LI X L,WU T.Efficient network traffic prediction method based on PF-LSTM network [J].Computer Application Research,2019,36(12):3833-3836.
[9]ZHANG C,PAUL P.Long-term mobile traffic forecasting using deep spatio-temporal neural networks[C]//Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing.Angeles:ACM,2018:231-240.
[10]ZHAO J H,WANG M X,QU H,et al.A traffic prediction algorithm for satellite networks based on adaptive klms [J].Journal of Beijing University of Posts and Telecommunications,2018,41(3):51-55.
[11]NAREJO S,PASERO E.An Application of Internet Traffic Prediction with Deep Neural Network [J].Multidisciplinary Approaches to Neural Computing,2018,69(1):139-149.
[12]HAN Y,JING Y W,JIN J Y,et al.Short term prediction of network traffic based on improved black hole algorithm optimized ESN [J].Journal of Northeast University (Natural Science Edition),2018,39(3):311-315.
[13]TIAN Z D,LI S J,WANG Y H,et al.Network traffic prediction based on Gaussian process regression compensation Arima [J].Journal of Beijing University of Posts and Telecommunications,2017,40(6):65-73.
[14]CHEN Z,LIU Z,PENG L,et al.A novel semi-supervised lear-ning method for Internet application identification [J].Soft Computing,2017,21(8):1963-1975.
[15]CHEN X,TANG J Y.Internet of Things traffic prediction mo-del based on Bayesian and causal ridge regression [J].Journal of Sichuan University (Natural Science Edition),2018,55(5):965-970.
[16]LONG Z Y,AI J Q,ZOU H,et al.Network traffic predictionmodel based on improved gray wolf optimization algorithm [J].Computer Application Research,2018,35(6):1845-1848.
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