Abstract
Network traffic prediction is not only an academic problem, but also a concern of industry and network performance department. Efficient prediction of network traffic is helpful for protocol design, traffic scheduling, detection of network attacks, etc. In this paper, we propose a network traffic prediction method based on the Echo State Network. In the first place we prove that the network traffic data are self-similar by means of the calculation of Hurst exponent of each traffic time series, which indicates that we can predict network traffic utilizing nonlinear time series models. Then Echo State Network is applied for network traffic forecasting. Furthermore, to avoid the weak-conditioned problem, grid search algorithm is used to optimize the reservoir parameters and coefficients. The dataset we perform experiments on are large-scale network traffic data at different time scale. They come from three provinces and are provided by ZTE Corporation. The result shows that our approach can predict network traffic efficiently, which is also a verification of the self-similarity analysis.
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References
Boutaba, R., Salahuddin, M.A., Limam, N., et al.: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J. Internet Serv. Appl. 9(1), 16 (2018)
Zhou, B., He, D., Sun, Z.: Traffic Modeling and Prediction using ARIMA/GARCH Model. In: Nejat Ince, A., Topuz, E. (eds.) Modeling and Simulation Tools for Emerging Telecommunication Networks, pp. 101–121. Springer, Boston, MA (2006). https://doi.org/10.1007/0-387-34167-6_5
Shu, Y., Yu, M., Yang, O., et al.: Wireless traffic modeling and prediction using seasonal ARIMA models. IEICE Trans. Commun. 88(10), 3992–3999 (2005)
Babu, C.N., Reddy, B.E.: A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Appl. Soft Comput. 23, 27–38 (2014)
Cortez, P., Rio, M., Rocha, M., et al.: Multi-scale Internet traffic forecasting using neural networks and time series methods. Expert. Syst. 29(2), 143–155 (2012)
Eswaradass, A., Sun, X.H., Wu, M.: Network bandwidth predictor (NBP): a system for online network performance forecasting. In: Proceedings of 6th IEEE International Symposium on Cluster Computing and the Grid (CCGRID), p. 4–pp. IEEE (2006)
Chabaa, S., Zeroual, A., Antari, J.: Identification and prediction of internet traffic using artificial neural networks. J. Int. Learn. Syst. Appl. 2(03), 147 (2010)
Li, Y., Liu, H., Yang, W., Hu, D., Xu, W.: Inter-data-center network traffic prediction with elephant flows. In: NOMS 2016-2016 IEEE/IFIP Network Operations and Management Symposium, pp. 206–213. IEEE (2016)
Bermolen, P., Rossi, D.: Support vector regression for link load prediction. Comput. Netw. 53(2), 191–201 (2009)
Nie, L., Jiang, D., Yu, S., et al.: Network traffic prediction based on deep belief network in wireless mesh backbone networks. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–5. IEEE (2017)
Poupart, P., Chen, Z., Jaini, P., et al.: Online flow size prediction for improved network routing. In: 2016 IEEE 24th International Conference on Network Protocols (ICNP), pp. 1–6. IEEE (2016)
Song, C., Havlin, S., Makse, H.A.: Self-similarity of complex networks. Nature 433(7024), 392 (2005)
Angeles Serrano, M., Krioukov, D., Boguná, M.: Self-similarity of complex networks and hidden metric spaces. Phys. Rev. Lett. 100(7), 078701 (2008)
Crovella, M.E., Bestavros, A.: Self-similarity in World Wide Web traffic: evidence and possible causes. IEEE/ACM Trans. Networking 5(6), 835–846 (1997)
Park, K., Willinger, W.: Self-Similar Network Traffic and Performance Evaluation. Wiley, New York (2000)
Brockwell, A.E.: Likelihood-based analysis of a class of generalized long-memory time series models. J. Time Ser. Anal. 28, 386–407 (2006). https://doi.org/10.1111/j.1467-9892.2006.00515.x
Witt, A., Malamud, B.D.: Quantification of long-range persistence in geophysical time series: conventional and benchmark-based improvement techniques. Surv. Geophys. 34, 541–651 (2013). https://doi.org/10.1007/s10712-012-9217-8
Crovella, M., Krishnamurthy, B.: Internet measurement: infrastructure, traffic & applications. DBLP (2006)
Khayari, R.E.A., Sadre, R., Haverkort, B.R.: A validation of the pseudo self-similar traffic model. In: International Conference on Dependable Systems & Networks. IEEE (2002)
Barunik, J., Kristoufek, L.: On Hurst exponent estimation under heavy-tailed distributions. Phys. A Stat. Mech. Appl. 389, 3844–3855 (2010)
Alvarez-Ramirez, J., Echeverria, J.C., Rodriguez, E.: Performance of a high-dimensional R/S method for Hurst exponent estimation. Phys. A Stat. Mech. Appl. 387, 6452–6462 (2008)
Bianchi, F.M., et al.: Prediction of telephone calls load using Echo State Network with exogenous variables. Neural Netw. 71, 204–213 (2015)
Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004). https://doi.org/10.1126/science.1091277
Chatzis, S.P., Demiris, Y.: Echo State Gaussian process. IEEE Trans. Neural Netw. 22(9), 1435–1445 (2011)
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This paper is supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0439).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Xu, Y., Li, Q., Meng, S. (2019). Self-similarity Analysis and Application of Network Traffic. In: Yin, Y., Li, Y., Gao, H., Zhang, J. (eds) Mobile Computing, Applications, and Services. MobiCASE 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-030-28468-8_9
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