A Modeling Algorithm to Network Flows in OTN Based on E1 Business

  • Fei XiaEmail author
  • Fanbo Meng
  • Zongze Xia
  • Xiaobo Huang
  • Li Song
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)


Recently, Optical Transport Networks (OTN) have been extensively deployed and applied in communication networks. Compared with traditional transport networks, OTN can provide much larger traffic transport ability. However, the properties and characteristics of network flows in OTN are not deeply studied and this is still a larger gap between theory analysis and practical applications. This paper studies the modeling problem of network flows in OTN. We propose a Walsh transform-based modeling method to describe end-to-end traffic amount of network flows in OTN. Firstly, the end-to-end traffic is denoted as a independent identically distributed random time-varying series. Then the Walsh transform theory is used to characterize the end-to-end traffic of network flows. By calculating the corresponding parameters, the proposed model is build correctly. Simulation results show that our approach is feasible and effective.


End-to-end traffic Walsh transform Traffic modeling Optical transport networks Traffic engineering 


  1. 1.
    Jiang, D., Nie, L., Lv, Z., et al.: Spatio-temporal Kronecker compressive sensing for traffic matrix recovery. IEEE Access 4, pp. 3046–3053 (2016)CrossRefGoogle Scholar
  2. 2.
    Jiang, D., Xu, Z., Xu, H.: A novel hybrid prediction algorithm to network traffic. Ann. Telecommun. 70(9), 427–439 (2015)CrossRefGoogle Scholar
  3. 3.
    Soule, A., Lakhina, A., Taft, N., et al.: Traffic matrices: balancing measurements, inference and modeling, In: Proceedings of SIGMETRICS 2005, vol. 33(1), 362–373 (2005)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zhang, Y., Roughan, M, Duffield, N., et al.: Fast accurate computation of large-scale IP traffic matrices from link loads. In: Proceedings of SIGMETRICS 2003, vol. 31(3), 206–217 (2003)CrossRefGoogle Scholar
  5. 5.
    Takeda, T., Shionoto, K.: Traffic matrix estimation in large-scale IP networks. In: Proceedings of LANMAN 2010, pp. 1–6 (2010)Google Scholar
  6. 6.
    Yingxun, F.: The Research and Improvement of the Genetic Algorithm. Beijing University of Posts and Telecommunications, Beijing (2010)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Jiang, D., Yuan, Z., Zhang, P., et al.: A traffic anomaly detection approach in communication networks for applications of multimedia medical devices. Multimed. Tools Appl. (2016)Google Scholar
  9. 9.
    Vaton, S., Bedo, J.: Network traffic matrix: how can one learn the prior distributions from the link counts only. In: Proceedings of ICC 2004, pp. 2138–2142 (2004)Google Scholar
  10. 10.
    Lad, M., Oliveira, R., Massey, D., et al.: Inferring the origin of routing changes using link weights. In: Proceedings of ICNP 2007, pp. 93–102 (2007)Google Scholar
  11. 11.
    Tune, P., Veitch, D.: Sampling vs sketching: an information theoretic comparison. In: Proceedings of INFOCOM 2011, pp. 2105–2113 (2011)Google Scholar
  12. 12.
    Jiang, D., Li, W., Lv, H.: An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing 2017(220), 160–169 (2017)CrossRefGoogle Scholar
  13. 13.
    Jiang, D., Xu, Z., Li, W., et al.: An energy-efficient multicast algorithm with maximum network throughput in multi-hop wireless networks. J. Commun. Netw. 18(5), 713–724 (2016)CrossRefGoogle Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    Jiang, D., Zhang, P., Lv, Z., et al.: Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet of Things J. 3(6), 1437–1447 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Fei Xia
    • 1
    Email author
  • Fanbo Meng
    • 2
  • Zongze Xia
    • 1
  • Xiaobo Huang
    • 1
  • Li Song
    • 1
  1. 1.State Grid Liaoyang Electric Power Supply CompanyLiaoyangChina
  2. 2.State Grid Liaoning Electric Power Company LimitedShenyangChina

Personalised recommendations