Towards H-SDN Traffic Analytic Through Visual Analytics and Machine Learning

  • Tin Tze Chiang
  • Tan Saw ChinEmail author
  • Lee Ching Kwang
  • Y. Zulfadzli
  • K. Rizaludin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11611)


With new networking paradigm emerged through Software-Defined Networking (SDN) offering various networking advantages over the traditional paradigm, organizations are attracted to migration their legacy networks to SDN networks. However, it is both challenging and impractical for organizations to migrate from traditional network architecture to full SDN architecture overnight. Therefore, the migration plan is performed in stages, resulting in a new type of network termed hybrid SDN (H-SDN). Effective migration and traffic scheduling in H-SDN environment are the two areas of challenges organizations face. Various solutions have been proposed in the literatures to address these two challenges. Differing from the approaches taken in the literatures, this work utilizes visual analytic and machine learning to address the two challenges. In both full SDN and H-SDN environment, literatures showed that data analytics applications have been successfully developed for various purposes as network security, traffic monitoring and traffic engineering. The success of data analytic applications is highly dependent on prior data analysis from both automated processing and human analysis. However, with the increasing volume of traffic data and the complex networking environment in both SDN and H-SDN networks, the need for both visual analytic and machine learning in inevitable for effective data analysis of network problems. Hence, the objectives of this article are three-folds: Firstly, to identify the limitations of the existing migration plan and traffic scheduling in H-SDN, followed by highlighting the challenges of the existing research works on SDN analytics in various network applications, and lastly, to propose the future research directions of SDN migration and H-SDN traffic scheduling through visual analytics and machine learning. Finally, this article presents the proposed framework termed VA-hSDN, a framework that utilizes visual analytics with machine learning to meet the challenges in SDN migration and traffic scheduling.


H-SDN Migration Scheduling Visual analytic 



This research work is fully supported by the research grant of TM R&D and Multimedia University, Cyberjaya, Malaysia. We are very thankful to the team of TM R&D and Multimedia University for providing the support to our research studies.


  1. 1.
    Shu, Z., et al.: Traffic engineering in software-defined networking: Measurement and management. IEEE Access 4, 3246–3256 (2016)CrossRefGoogle Scholar
  2. 2.
    Shyr, J., Spisic, D.: Automated data analysis. Wiley Interdisc. Rev. Comput. Stat. 6(5), 359–366 (2014)CrossRefGoogle Scholar
  3. 3.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. MIT Press, Boston (1996)Google Scholar
  4. 4.
    Garg, S., Nam, J.E., Ramakrishnan, I.V., Mueller, K.: Model-driven visual analytics. In: 2008 IEEE Symposium on Visual Analytics Science and Technology, pp. 19–26. IEEE, Columbus (2008)Google Scholar
  5. 5.
    Keim, D.A., Mansmann, F., Thomas, J.: Visual analytics: how much visualization and how much analytics? SIGKDD Explor. Newsl. 11(2), 5–8 (2010)CrossRefGoogle Scholar
  6. 6.
    Sacha, D., et al.: What you see is what you can change: Human-centered machine learning by interactive visualization. Neurocomputing 268(13), 164–175 (2017)CrossRefGoogle Scholar
  7. 7.
    Bethel, E.W., Campbell, S., Dart, E., Stockinger, K., Wu, K.: Accelerating network traffic analytics using query-driven visualization. In: 2006 IEEE Symposium on Visual Analytics Science and Technology, pp. 115–122. IEEE, Baltimore (2006)Google Scholar
  8. 8.
    Chong, L., Yong-Hao, W.: Strategy of data manage center network traffic scheduling based on SDN. In: 2016 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), pp. 29–34. IEEE, Changsha (2016)Google Scholar
  9. 9.
    Guo, Y., Wang, Z., Yin, X., Shi, X., Wu, J., Zhang, H.: Incremental deployment for traffic engineering in hybrid SDN network, In: 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC), pp. 1–8. IEEE, Nanjing (2015)Google Scholar
  10. 10.
    Rathee, S., Sinha, Y., Haribabu, K.: A survey: hybrid SDN. J. Netw. Comput. Appl. 100, 35–55 (2017)CrossRefGoogle Scholar
  11. 11.
    Vissicchio, S., Vanbever, L., Bonaventure, O.: Opportunities and research challenges of hybrid software defined networks. ACM SIGCOMM Comput. Commun. Rev. 44(2), 70–75 (2014)CrossRefGoogle Scholar
  12. 12.
    Feamester, N., Rexford, J., Zegura, E.: The road to SDN. ACM Queue 11(12), 1–21 (2013)Google Scholar
  13. 13.
    He, J., Song, W.: Achieving near-optimal traffic engineering in hybrid software defined networks. In: 2015 IFIP Networking Conference (IFIP Networking), pp. 1–9. IEEE, Toulouse (2015)Google Scholar
  14. 14.
    Vissicchio, S., Vanbever, L., Cittadini, L., Xie, G.G., Bonaventure, O.: Safe update of hybrid SDN networks. IEEE/ACM Trans. Netw. 25(3), 1649–1662 (2017)CrossRefGoogle Scholar
  15. 15.
    Caria, M., Jukan, A., Hoffmann, M.: A performance study of network migration to SDN-enabled traffic engineering. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 1391–1396. IEEE, Atlanta (2013)Google Scholar
  16. 16.
    Das, T., Caria, M., Jukan, A., Hoffmann, M.: Insights on SDN migration trajectory. In: 2015 IEEE International Conference on Communications (ICC), pp. 5348–5353. IEEE, London (2015)Google Scholar
  17. 17.
    Poularakis, K., Iosifidis, G., Smaragdakis, G., Tassiulas, L.: One step at a time: optimizing SDN upgrades in ISP networks. In: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, pp. 1–9. IEEE, Atlanta (2017)Google Scholar
  18. 18.
    Ren, H., Li, X., Geng, J., Yan, J.: A SDN-based dynamic traffic scheduling algorithm. In: 2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 514–518. IEEE, Chengdu (2016)Google Scholar
  19. 19.
    Sun, D., Zhao, K., Fang, Y., Cui, J.: Dynamic traffic scheduling and congestion control across data centers based on SDN. Fut. Internet 10(7), 64–76 (2018)CrossRefGoogle Scholar
  20. 20.
    Wang, W., He, W., Su, J.: Enhancing the effectiveness of traffic engineering in hybrid SDN. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE, Paris (2017)Google Scholar
  21. 21.
    Veena, S., Manju, R.: Detection and mitigation of security attacks using real time SDN analytics. In: 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), pp. 87–93. IEEE, Coimbatore (2017)Google Scholar
  22. 22.
    Yang, F., Jiang, Y., Pan, T., Xinhua, E.: Traffic anomaly detection and prediction based on SDN-enabled ICN. In: 2018 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–5. IEEE, Kansas City (2018)Google Scholar
  23. 23.
    Peng, H., Sun, Z., Zhao, X., Tan, S., Sun, Z.: A detection method for anomaly flow in software defined network. IEEE Access 6, 27809–27817 (2018)CrossRefGoogle Scholar
  24. 24.
    Li, C.H., et al.: Detection and defense of DDoS attack-based on deep learning in OpenFlow-based SDN. Int. J. Commun. Syst. 31(5) (2018)CrossRefGoogle Scholar
  25. 25.
    Hyun, J., Tu, N.V., Hong, J.W.: Towards knowledge-defined networking using in-band network telemetry. In: NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, pp. 1–7. IEEE, Taipei (2018)Google Scholar
  26. 26.
    Yao, H., Mai, T., Xu, X., Zhang, P., Li, M., Liu, Y.: NetworkAI: an intelligent network architecture for self-learning control strategies in software defined networks. IEEE Internet Things J. 5(6), 4319–4327 (2018)CrossRefGoogle Scholar
  27. 27.
    Koley, B.: The zero touch network. In: International Conference on Network and Service Management. Montreal, Quebec (2016)Google Scholar
  28. 28.
    Yan, S., Aguado, A., Ou, Y., Wang, R., Nejabati, R., Simeonidou, D.: Multilayer network analytics with SDN-based monitoring framework. IEEE/OSA J. Opt. Commun. Network. 9(2), A271–A279 (2017)CrossRefGoogle Scholar
  29. 29.
    Yan, S., Nejabati, R., Simeonidou, D.: Data-driven network analytics and network optimisation in SDN-based programmable optical networks. In: 2018 International Conference on Optical Network Design and Modeling (ONDM), pp. 234–238. IEEE, Dublin (2018)Google Scholar
  30. 30.
    Clemm, A., Chandramouli, M., Krishnamurthy, S.: DNA: an SDN framework for distributed network analytics. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 9–17. IEEE, Ottawa (2015)Google Scholar
  31. 31.
    Xie, J., Huang, F.R.Y.T., Xie, R., Liu, J., Wang, C., Liu, Y.: A survey of machine learning techniques applied to software defined networking (SDN): research issues and challenges. IEEE Commun. Surv. Tutor. 21(1), 393–430 (2019)CrossRefGoogle Scholar
  32. 32.
    Roughan, M., Zhang, Y., Willinger, W., Qiu, L.: Spatio-temporal compressive sensing and internet traffic matrices (extended version). IEEE/ACM Trans. Netw. 20(3), 662–676 (2012)CrossRefGoogle Scholar
  33. 33.
    Taieb, S.B.: Machine learning strategies for multi-step-ahead time series forecasting.. Universit Libre de Bruxelles, Belgium (2014)Google Scholar
  34. 34.
    Jiang, D., Wang, X., Guo, L., Ni, H., Chen, Z.: Accurate estimation of large-scale IP traffic matrix. AEU–Int. J. Electron. Commun. 65(1), 75–86 (2011)CrossRefGoogle Scholar
  35. 35.
    Zhou, H.F., Tan, L.S., Zeng, Q., Wu, C.M.: Traffic matrix estimation: a neural network approach with extended input and expectation maximization iteration. J. Netw. Comput. Appl. 60, 220–232 (2015)CrossRefGoogle Scholar
  36. 36.
    Bae, J., Falkman, G., Helldin, T., Riveiro, M.: Data Science in Practice, 1st edn. Springer, Cham (2018)Google Scholar
  37. 37.
    Cheng, T.Y.Y., Jia, X.H.: Compressive traffic monitoring in hybrid SDN. IEEE J. Sel. Areas Commun. 36(12), 2731–2743 (2018)CrossRefGoogle Scholar
  38. 38.
    Ahmed, N.K., Atiya, A.F., Gayar, N.E., El-Shishiny, H.: An empirical comparison of machine learning models for time series forecasting. Expert Syst. Appl. 39(8), 7067–7083 (2012)CrossRefGoogle Scholar
  39. 39.
    Thomas, J., Cook, K.: Illuminating the Path: The Research and Development Agenda for Visual Analytics. 1st edn. National Visualization and Analytics Ctr (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tin Tze Chiang
    • 1
  • Tan Saw Chin
    • 1
    Email author
  • Lee Ching Kwang
    • 2
  • Y. Zulfadzli
    • 2
  • K. Rizaludin
    • 3
  1. 1.Faculty of Informatics and ComputingMultimedia UniversityCyberjayaMalaysia
  2. 2.Faculty of EngineeringMultimedia UniversityCyberjayaMalaysia
  3. 3.Telekom Malaysia R&DJalan Persiaran MulitmediaCyberjayaMalaysia

Personalised recommendations