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Intelligent Traffic Engineering for Future Intent-Based Software-Defined Transport Network

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Future Intent-Based Networking

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 831))

Abstract

This chapter addresses Traffic Engineering (TE) issues in future software-defined infrastructures using machine learning (ML) and neural networks. The Software-Defined Networks (SDN) architecture can be used to implement Intent-Based Networking (IBN) that enables the automation of network management tasks through elements of artificial intelligence (AI) and ML. The intent-based optical transport network infrastructure is proposed, adapted to the use of intelligent TE algorithms based on SDN and Optical Label Switching (OLS) technology. An algorithm for determining Intent-Based Software-Defined Transport Network (IBSDTN) states based on ML algorithms k-means and c-means is proposed. This algorithm allows the provision of an appropriate set of network parameters for training the appropriate control algorithms. A method of intelligent TE using graph neural networks to provide the necessary quality of service (QoS) parameters based on users intention during peak hours has been developed. This algorithm using the vector of network parameters, which also takes into account the parameter of energy consumption, manages network resources to provide the necessary QoS parameters.

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References

  1. Alvizu, R., Maier, G., Kukreja, N., Pattavina, A., Morro, R., Cavazzoni ,C.: Comprehensive survey on T-SDN: software-defined networking for transport networks. IEEE Commun. Surv. Tutorials 19(4), 2232–2283 (2017). https://doi.org/10.1109/COMST.2017.2715220

  2. Jia, W., Dong, X., Chen, Y., Chen, F.: A survey on All-optical IP convergence optical transport networks. In: 2019 7th International Conference on Information, Communication and Networks (ICICN), pp. 114–119 (2019). https://doi.org/10.1109/ICICN.2019.8834956

  3. Musumeci, M., et al.: An overview on application of machine learning techniques in optical networks. IEEE Commun. Surv. Tut. 21(2), 1383–1408 (2019)

    Article  Google Scholar 

  4. Kaidan, M., Maksymyuk, T., Andrushchak, V., Klymash, M.: Intelligent data flow aggregation in edge nodes of optical label switching networks. In: 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT), pp. 145–148 (2019)

    Google Scholar 

  5. Makridakis, S.: The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures 90, 46–60 (2017)

    Article  Google Scholar 

  6. Cheatham, B., Javanmardian, K., Samandari, H.: Confronting the risks of artificial intelligence. McKinsey Quarterly (2019)

    Google Scholar 

  7. Yang, H., et al.: Intelligent optical network with AI and blockchain. In: 2019 18th International Conference on Optical Communications and Networks (ICOCN), pp. 1–3 (2019). https://doi.org/10.1109/ICOCN.2019.8934148

  8. Maksymyuk,T., Dumych, S., Krasko, O., Kaidan, M., Strykhalyuk, B.: Study and development of next-generation optical networks. Smart Comput. Rev. 4 (2014)

    Google Scholar 

  9. Kaidan, M., Andrushchak, V., Maksymyuk, T., Klymash, M.: Scalability parameter in all-optical switches for optical label switching network. In: 15th International Conference on the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM-2019), pp. 120–123 (2019)

    Google Scholar 

  10. Beshley, M., Vesely, P., Prislupskiy, A., Beshley, H., Kyryk, M., Romanchuk, V., Kahalo, I.: Customer-oriented quality of service management method for the future intent-based networking. Appl. Sci. 10(22), 8223-1–8223-38 (2020)

    Google Scholar 

  11. Medvetskyi, M., Beshley, M., Klymash, M.: A quality of experience management method for intent-based software-defined networks. In: 2021 IEEE 16th International Conference on the Experience of Designing and Application of CAD Systems (CADSM), pp. 59–62 (2021). https://doi.org/10.1109/CADSM52681.2021.9385250 (2021)

  12. Beshley, M., Pryslupskyi, A., Panchenko, O., Beshley, H.: SDN/Cloud solutions for intent-based networking. In: 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT), pp. 22–25 (2019). https://doi.org/10.1109/AIACT.2019.8847731

  13. Ujcich, B.E., Bates, A., Sanders, W.H.: Provenance for intent-based networking. In: 2020 6th IEEE Conference on Network Softwarization (NetSoft), Ghent, Belgium, pp. 195–199 (2020)

    Google Scholar 

  14. Faraz, M., Ismail, M.: INMTD: intent-based moving target defense framework using software defined networks. Eng. Technol. Appl. Sci. Res. 10, 5142–5147 (2020)

    Article  Google Scholar 

  15. Zeydan, E., Turk, Y.: Recent advances in intent-based networking: a survey. In: 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, pp. 1–5 (2020)

    Google Scholar 

  16. Beshley, M., Kryvinska, N., Yaremko, O., Beshley, H.: A self-optimizing technique based on vertical handover for load balancing in heterogeneous wireless networks using big data analytics. Appl. Sci. 11, 4737 (2021). https://doi.org/10.3390/app11114737(2021)

    Article  Google Scholar 

  17. Beshley, M., Kryvinska, N., Beshley, H., Yaremko, O., Pyrih, J.: Virtual router design and modeling for future networks with QoS guarantees. Electronics 10, 1139 (2021). https://doi.org/10.3390/electronics10101139(2021)

    Article  Google Scholar 

  18. Beshley, M., Kryvinska, N., Seliuchenko, M., Beshley, H., Shakshuki, E., Yasar, A.: End-to-end QoS “smart queue” management algorithms and traffic prioritization mechanisms for narrow-band internet of things services in 4G/5G networks. Sensors 20(8), 2324-1–2324-30 (2020)

    Google Scholar 

  19. Fadlullah, Z., et al.: State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun. Surv. Tutorials 19(4), 2432–2455 (2017)

    Article  Google Scholar 

  20. Wu, Y.-J., Hwang, P.-C., Hwang, W.-S., Cheng, M.-H.: Artificial intelligence enabled routing in software defined networking. Appl. Sci. 10, 656 (2020)

    Article  Google Scholar 

  21. Kumar, S., Bansal, G., Shekhawat, V.S.: A Machine learning approach for traffic flow provisioning in software defined networks. In: 2020 International Conference on Information Networking (ICOIN), pp. 602–607 (2020). https://doi.org/10.1109/ICOIN48656.2020.9016529

  22. Gringeri, S., Basch, B., Shukla, V., Egorov, R., **a, J.: Flexible architectures for optical transport nodes and networks. IEEE Commun. Mag. 48(7), 40–50 (2010)

    Article  Google Scholar 

  23. Chankyun, L., June-Koo Rhee K.: Efficient Design and Scalable Control for Store-and-Forward Capable Optical Transport Networks. vol. 9, issue 8, pp. 699–710 (2017)

    Google Scholar 

  24. Legrand, T.: Labelled OBS test bed for contention resolution study. In: 2008 5th International Conference on Broadband Communications, Networks and Systems, pp. 82–87. IEEE (2008)

    Google Scholar 

  25. Lazaro, J., Arias, J., Martın, J.L., Cuadrado, C., Astarloa, A.: Implementation of a modified Fuzzy C-Means clustering algorithm for real-time applications. Microprocess, Microsyst. 29, 375–380 (2005)

    Article  Google Scholar 

  26. Icer, S.: Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods. Comput. Methods Programs Biomed 112, 38–46 (2013)

    Article  Google Scholar 

  27. Asyali, M.H., Colak, D., Demirkaya, O., Inan, M.S.: Gene expression profile classification: a review. Curr. Bioinform. 1, 55–73 (2006)

    Article  Google Scholar 

  28. Runkler, T.A., Katz, C.: Fuzzy clustering by particle swarm optimization. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 601–608 (2006)

    Google Scholar 

  29. Roh, Y., Geon H., Steven, E.: A survey on data collection for machine learning: a big data. AI integration perspective. IEEE Trans. Knowl. Data Eng. (2019)

    Google Scholar 

  30. Scarselli, F., Gori, M., Tsoi, A., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61–80 (2009)

    Article  Google Scholar 

  31. Kaidan, M., Andrushchak, V., Pitsyk, M.: Calculation model of energy efficiency in optical transport networks. In: 2015 Second International Scientific-Practical Conference Problems of Infocommunications Science and Technology (PIC S&T), pp. 167–170 (2015)

    Google Scholar 

  32. Song, W., Andrushchak, V., Kaidan, M., Beshley, M., Kochan, O., Su, J.: Methodology for calculating the energy consumption of information communication systems. Techn. Electrodynamics, 4, 80–88 (2020)

    Google Scholar 

  33. Przystupa, K., Beshley, M., Kaidan, M., Andrushchak, V., Demydov, I., Kochan, O., Pieniak, D.: Methodology and software tool for energy consumption evaluation and optimization in multilayer transport optical networks. Energies 13(23), 6370-1–6370-21 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Lviv Polytechnic National University, Lviv, 79013, Ukraine

    Volodymyr Andrushchak, Mykola Beshley, Taras Maksymyuk & Taras Andrukhiv

  2. Lemberg Solutions LLC, Lviv, 7900, Ukraine

    Lyubomyr Dutko

Authors
  1. Volodymyr Andrushchak
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  2. Mykola Beshley
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  3. Lyubomyr Dutko
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  4. Taras Maksymyuk
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  5. Taras Andrukhiv
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Correspondence to Volodymyr Andrushchak .

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Editors and Affiliations

  1. Lviv Polytechnic National University, Lviv, Ukraine

    Mikhailo Klymash

  2. Lviv Polytechnic National University, Lviv, Ukraine

    Mykola Beshley

  3. Saxon Study Academy, BA Dresden University of Cooperative Education, Dresden, Germany

    Andriy Luntovskyy

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Andrushchak, V., Beshley, M., Dutko, L., Maksymyuk, T., Andrukhiv, T. (2022). Intelligent Traffic Engineering for Future Intent-Based Software-Defined Transport Network. In: Klymash, M., Beshley, M., Luntovskyy, A. (eds) Future Intent-Based Networking. Lecture Notes in Electrical Engineering, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-030-92435-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-92435-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92433-1

  • Online ISBN: 978-3-030-92435-5

  • eBook Packages: EngineeringEngineering (R0)

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