Abstract
Most of network management tasks in traffic engineering such as traffic scheduling, path planning, both of them are required the accurate and fine-grained network traffic. However, it is difficult to capture and estimate the volume of network traffic due to its time-varying nature. In this paper, we study the network traffic estimation scheme to estimate the fine-grained network traffic. Firstly, the network traffic is constructed as a time series and the autoregressive moving average (ARMA) method is used to characterize and model network traffic. Secondly, in order to decrease the estimation errors of the ARMA model, we use the optimization theory to adjust the estimation results. We construct an objective function with constraints. We find that objective function is an NP-hard problem, then we introduce a heuristic algorithm to find the optimization results. Finally, to evaluate the performance of our proposed scheme, we construct a simulation platform and compare our scheme with that of the other methods in an SDN simulation platform. The simulation results indicate that our approach is effective and our method can reflect the network traffic characteristics.
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References
Jiang, D., Xu, Z., Liu, J., et al.: An optimization-based robust routing algorithm to energy-efficient networks for cloud computing. Telecommun. Syst. 63(1), 89–98 (2016)
Jiang, D., Nie, L., Lv, Z., et al.: Spatio-temporal Kronecker compressive sensing for traffic matrix recovery. IEEE Access 4, 3046–3053 (2016)
Guo, Y., Wang, Z., Yin, X., et al.: Traffic engineering in hybrid SDN networks with multiple traffic matrices. Comput. Netw. 126, 187–199 (2017)
Jiang, D., Zhao, Z., Xu, Z., et al.: How to reconstruct end-to-end traffic based on time-frequency analysis and artificial neural network. AEU-Int. J. Electron. Commun. 68(10), 915–925 (2014)
Liu, G., Guo, S., Zhao, Q., et al.: Tomogravity space based traffic matrix estimation in data center networks. Transp. Res. Part C: Emerg. Technol. 86, 39–50 (2018)
Hashemi, H., Abdelghany, K.F., et al.: Real-time traffic network state estimation and prediction with decision support capabilities: application to integrated corridor management. Transp. Res. Part C: Emerg. Technol. 73, 128–146 (2016)
Kawasaki, Y., Hara, Y., Kuwahara, M.: Traffic state estimation on a two-dimensional network by a state-space model. Transp. Res. Part C: Emerg. Technol. 5, 1–17 (2019)
Dias, K.L., Pongelupe, M.A., Caminhas, W.M., et al.: An innovative approach for real-time network traffic classification. Comput. Netw. 158, 143–157 (2019)
Jiang, D., Huo, L., Lv, Z., et al.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. pp(99), 1–15 (2018)
Ermagun, A., Levinson, D.: Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending. Transp. Rese. Part C: Emerg. Technol. 104(5), 38–52 (2019)
Jiang, D., Huo, L., Li, Y.: Fine-granularity inference and estimations to network traffic for SDN. PLoS One 13(5), 1–23 (2018)
Roughan, M., Zhang, Y., Willinger, W., et al.: Spatio-temporal compressive sensing and internet traffic matrices. IEEE/ACM Trans. Netw. (ToN) 20(3), 662–676 (2012)
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Huang, R., Jia, H., Huang, X. (2019). A SDN-Based Network Traffic Estimating Algorithm in Power Telecommunication Network. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-32216-8_9
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DOI: https://doi.org/10.1007/978-3-030-32216-8_9
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