Skip to main content
SpringerLink
Log in

Research of the QoE Fast ReRouting Processes with Differentiated R-Factor Maximization for VoIP-Flows Using the Tensor Model of the Corporate Telecommunication Network

  • Conference paper
  • First Online:
Current Trends in Communication and Information Technologies (IPF 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 212))

Included in the following conference series:

Abstract

In the work, the flow-based model of the QoE Fast ReRouting is proposed. The model is based on the implementation of the single path or multipath routing, the conditions of flow conservation, which are introduced for routing variables that regulate the construction of both primary and backup paths. In addition, restrictions have been introduced to prevent the overloading of communication links with packet flows, the implementation of which actually provided bandwidth protection. The model was supplemented by the conditions of structural network elements protection (node, link, and route). The peculiarity of these conditions is the consideration of possible packet losses due to congestion of router interfaces. In order to obtain analytical expressions for the calculation of the R-factor for each of the VoIP-flows, a tensor generalization of the mathematical model of routing has been performed. Based on the tensor description of the network, it was possible to obtain expressions for calculating the average multipath end-to-end delay and packet loss probability, which allowed to formulate in analytical form the R-factor calculation expressions for each of the VoIP-Flows. The novelty of the proposed model is the formulation of the problem of QoE Fast ReRouting in the optimization form when the optimality criterion was the maximum of the additive form, represented by the sum of weighted according to the IP-priority values of R-factor for each of the VoIP-Flows. The results of the study of the proposed model confirmed its efficiency and adequacy, which was especially evident in the conditions of complex network topologies, high network congestion, and flow differentiation relative to the IP-priority values of packets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dodd, A.Z.: The Essential Guide to Telecommunications (Essential Guide Series). Prentice Hall, Hoboken (2019)

    Google Scholar 

  2. Mas-Machuca, C., Musumeci, F., Vizarreta, P., Pezaros, D., Jouët, S., Tornatore, M., Hmaity, A., Liyanage, M., Gurtov, A., Braeken, A.: Reliable control and data planes for softwarized networks. In: Guide to Disaster-Resilient Communication Networks, pp. 243–270. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44685-7_10

  3. Strelkovskaya, I., Solovskaya, I.: Using spline-extrapolation in the research of self-similar traffic characteristics. J. Electr. Eng. 70(4), 310–316 (2019). https://doi.org/10.2478/jee-2019-0061

    Article  Google Scholar 

  4. Strelkovskaya, I., Solovskaya, I., Makoganiuk, A.: Spline-extrapolation method in traffic forecasting in 5G networks. J. Telecommun. Inf. Technol. 3, 8–16 (2019). https://doi.org/10.26636/jtit.2019.134719

    Article  Google Scholar 

  5. Strelkovskaya, I., Solovskaya, I., Makoganiuk, A.: Different extrapolation methods in problems of forecasting. In: Ilchenko, M., Uryvsky, L., Globa, L. (eds.) Advances in Information and Communication Technology and Systems. MCT 2019. Lecture Notes in Networks and Systems, vol. 152, pp. 217–228. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-58359-0_12

  6. Globa, L., Skulysh, M., Romanov, O., Nesterenko, M.: Quality control for mobile communication management services in hybrid environment. In: Ilchenko, M., Uryvsky, L., Globa, L. (eds.) Advances in Information and Communication Technologies. UKRMICO 2018. Lecture Notes in Electrical Engineering, vol. 560, pp. 76–100. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16770-7_4

  7. Semenov, A.A., Semenova, O.O., Voznyak, O.M., Vasilevskyi, O.M., Yakovlev, M.Y.: Routing in telecommunication networks using fuzzy logic. In: 2016 17th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM) Proceedings, pp. 173–177. IEEE (2016). https://doi.org/10.1109/EDM.2016.7538719

  8. Chiesa, M., Kamisiński, A., Rak, J., Rétvári, G., Schmid, S.: Fast recovery mechanisms in the data plane. TechRxiv. Preprint, pp. 1–46 (2020). https://doi.org/10.36227/techrxiv.12367508.v2

  9. Papán, J., Segeč, P., Palúch, P., Mikuš, Ľ., Moravčík, M.: The survey of current IPFRR mechanisms. In: Federated Conference on Software Development and Object Technologies, pp. 229–240. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-46535-7_18

  10. Monge, A.S., Szarkowicz, K.G.: MPLS in the SDN Era: Interoperable Scenarios to Make Networks Scale to New Services. O’Reilly Media Inc., Sebastopol (2015)

    Google Scholar 

  11. Cruz, P., Gomes, T., Medhi, D.: A heuristic for widest edge-disjoint path pair lexicographic optimization. In: 2014 6th International Workshop on Reliable Networks Design and Modeling (RNDM) Proceedings, pp. 9–15. IEEE (2014). https://doi.org/10.1109/RNDM.2014.7014925

  12. Lemeshko, O., Yeremenko, O.: Enhanced method of fast re-routing with load balancing in software-defined networks. J. Electr. Eng. 68(6), 444–454 (2017). https://doi.org/10.1515/jee-2017-0079

    Article  Google Scholar 

  13. Lemeshko, O., Yevdokymenko, M., Yeremenko, O., Hailan, A.M., Segeč, P., Papán, J.: Design of the fast ReRoute QoS protection scheme for bandwidth and probability of packet loss in software-defined WAN. In: 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM) Proceedings, pp. 1–5. IEEE (2019). https://doi.org/10.1109/CADSM.2019.8779321

  14. Lemeshko, O., Yeremenko, O., Tariki, N.: Solution for the default gateway protection within fault-tolerant routing in an IP network. Int. J. Electr. Comput. Eng. Syst. 8(1), 19–26 (2017). https://doi.org/10.32985/ijeces.8.1.3

    Article  Google Scholar 

  15. Yeremenko, O., Lemeshko, O., Persikov, A.: Secure routing in reliable networks: proactive and reactive approach. In: Shakhovska, N., Stepashko, V. (eds.) Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol. 689, pp. 631–655. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70581-1_44

  16. Lemeshko, O., Yevdokymenko, M., Yeremenko, O., Nevzorova, O., Snihurov, A., Kovalenko, T.: Fast ReRoute model with VoIP quality of experience protection. In: 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT) Proceedings, pp. 16–21 (2019). https://doi.org/10.1109/aiact.2019.8847918

  17. Lemeshko, O., Yevdokymenko, M., Yeremenko, O.: Fast ReRoute model with multimedia quality protection. In: 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET) Proceedings, pp. 287–292. IEEE (2020). https://doi.org/10.1109/TCSET49122.2020.235441

  18. Yevdokymenko, M.: Enhancement of the adaptive routing tensor model in the infocommunication network with providing quality of experience by the R-factor. Technol. Audit Prod. Reserves 3, (2(53)), 15–22 (2020). https://doi.org/10.15587/2706-5448.2020.206031

  19. Lemeshko, O., Yevdokymenko, M., Yeremenko, O., Al-Dulaimi, A.M.K., Segeč, P., Papán, J.: QoE-centric adaptive multipath routing under R-Factor with load balancing in SDN. In: 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T) Proceedings, pp. 1–6. IEEE (2020)

    Google Scholar 

  20. Barakabitze, A.A., Barman, N., Ahmad, A., Zadtootaghaj, S., Sun, L., Martini, M.G., Atzori, L.: QoE management of multimedia streaming services in future networks: A tutorial and survey. IEEE Commun. Surv. Tutor. 22(1), 526–565 (2019). https://doi.org/10.1109/COMST.2019.2958784

    Article  Google Scholar 

  21. Barakabitze, A.A., Sun, L., Mkwawa, I.H., Ifeachor, E.: A novel QoE-centric SDN-based multipath routing approach for multimedia services over 5G networks. In: 2018 IEEE International Conference on Communications (ICC) Proceedings, pp. 1–7. IEEE (2018). https://doi.org/10.1109/ICC.2018.8422617

  22. Oliveira, T., Sargento, S.: QoE-based load balancing of OTT video content in SDN networks. In: 2019 IEEE Symposium on Computers and Communications (ISCC) Proceedings, pp. 1–6. IEEE (2019). https://doi.org/10.1109/ISCC47284.2019.8969720

  23. Robinson, M., Milosavljevic, M., Kourtessis, P., Fisher, S., Stafford, G.P., Treiber, J., Burrell, M.J., Senior, J.M.: QoE based holistic traffic engineering in SDN enabled heterogeneous transport networks. In: 2017 19th International Conference on Transparent Optical Networks (ICTON) Proceedings, pp. 1–4. IEEE (2017). https://doi.org/10.1109/ICTON.2017.8024878

  24. Liotou, E., Samdanis, K., Pateromichelakis, E., Passas, N., Merakos, L.: QoE-SDN APP: a rate-guided QoE-aware SDN-APP for HTTP adaptive video streaming. IEEE J. Sel. Areas Commun. 36(3), 598–615 (2018). https://doi.org/10.1109/JSAC.2018.2815421

    Article  Google Scholar 

  25. Abar, T., Letaifa, A.B., Elasmi, S.: Enhancing QoE based on machine learning and DASH in SDN networks. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA) Proceedings, pp. 258–263. IEEE (2018). https://doi.org/10.1109/WAINA.2018.00095

  26. Ramakrishnan, S., Zhu, X., Chan, F., Kambhatla, K.: SDN based QoE optimization for HTTP-based adaptive video streaming. In: 2015 IEEE International Symposium on Multimedia (ISM) Proceedings, pp. 120–123. IEEE (2015). https://doi.org/10.1109/ISM.2015.53

  27. Strelkovskaya, I.V., Solovskaya, I.N.: Tensor model of multiservice network with different classes of traffic service. Radioelectron. Commun. Syst. 56(6), 296–303 (2013). https://doi.org/10.3103/S0735272713060058

    Article  Google Scholar 

  28. Lemeshko, O.V., Yeremenko, O.S., Hailan, A.M.: QoS solution of traffic management based on the dynamic tensor model in the coordinate system of interpolar paths and internal node pairs. In: 2016 International Conference Radio Electronics & Info Communications (UkrMiCo) Proceedings, pp. 1–6. IEEE (2016). https://doi.org/10.1109/UkrMiCo.2016.7739625

  29. Lemeshko, O., Yeremenko, O.: Routing tensor model presented in the basis of interpolar paths and internal node pairs. In: 2016 Third International Scientific-Practical Conference Problems of Infocommunications Science and Technology (PIC S&T) Proceedings, pp. 201–204. IEEE (2016). https://doi.org/10.1109/INFOCOMMST.2016.7905381

  30. Lemeshko, O.V., Yevseyeva, O.Y.O., Garkusha, S.V.: A tensor model of multipath routing based on multiple QoS metrics. In: 2013 International Siberian Conference on Control and Communications (SIBCON) Proceedings, pp. 1–4. IEEE (2013). https://doi.org/10.1109/SIBCON.2013.6693645

  31. ITU-T G.107: The E-model: a computational model for use in transmission planning (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kharkiv National University of Radio Electronics, 14 Nauky Avenue, Kharkiv, Ukraine

    Oleksandr Lemeshko, Oleksandra Yeremenko, Maryna Yevdokymenko & Tamara Radivilova

Authors
  1. Oleksandr Lemeshko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oleksandra Yeremenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maryna Yevdokymenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tamara Radivilova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Oleksandr Lemeshko , Oleksandra Yeremenko , Maryna Yevdokymenko or Tamara Radivilova .

Editor information

Editors and Affiliations

  1. O.S. Popov Odesa National Academy of Telecommunications, Odesa, Ukraine

    Petro Vorobiyenko

  2. National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine

    Mykhailo Ilchenko

  3. O.S. Popov Odesa National Academy of Telecommunications, Odesa, Ukraine

    Iryna Strelkovska

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lemeshko, O., Yeremenko, O., Yevdokymenko, M., Radivilova, T. (2021). Research of the QoE Fast ReRouting Processes with Differentiated R-Factor Maximization for VoIP-Flows Using the Tensor Model of the Corporate Telecommunication Network. In: Vorobiyenko, P., Ilchenko, M., Strelkovska, I. (eds) Current Trends in Communication and Information Technologies. IPF 2020. Lecture Notes in Networks and Systems, vol 212. Springer, Cham. https://doi.org/10.1007/978-3-030-76343-5_6

Download citation

Publish with us

Policies and ethics

Search

Navigation