Enhancement of the adaptive routing tensor model in the infocommunication network with providing quality of experience by the R-factor

Authors

DOI:

https://doi.org/10.15587/2706-5448.2020.206031

Keywords:

infocommunication network, quality of experience, R-factor, adaptive routing, tensor model.

Abstract

The object of research is the routing processes and ensuring the quality of experience in the infocommunication network. To conduct research an improvement of the adaptive routing tensor model in an infocommunication network with quality of experience by the R-factor is proposed. The basis was a floe-based routing model that took into account possible packet losses caused by congestion of network elements and was represented by the conditions for implementing a multipath routing strategy, conditions of the flow conservation and conditions for preventing overload of communication links. To obtain in an analytical form the conditions for ensuring the quality of experience in terms of the R-factor, a tensor description of the infocommunication network is carried out. This allowed obtaining of analytical expressions for calculating the average end-to-end delay and the probability of packet loss, which were used to formulate the QoE conditions in terms of the R-factor.

In the framework of the proposed model, solving the technological problem of adaptive routing solutions are reduced to solving the optimization problem of nonlinear programming for calculating route variables. The introduced optimality criterion allowed it possible to ensure the adaptive nature of route decisions, when an increase in QoE requirements led to an increase in the amount of network resource used. To solve the problem, methods of mathematical programming were used, which are implemented in the MatLab package.

A study on a fragment of the infocommunication network made it possible to evaluate the adequacy and effectiveness of the proposed model. Using the obtained research results, it was possible to ensure the fulfillment of the specified QoE requirements in terms of the R-factor for the services provided to end users. Using the proposed model is characterized by high efficiency on load balancing across multiple routes in the infocommunication network. This was evidenced by the fact that at a given value of the R-factor, with an increase in the intensity of traffic entering the infocommunication network, there was a gradual increase in the number of routes involved. In other words, the network resource was distributed evenly and efficiently by 7–10 % relative to known analogues, which, when solving the same problem, immediately use all possible routes.

Author Biography

Maryna Yevdokymenko, Kharkiv National University of Radio Electronics, 14, Nauky ave., Kharkiv, Ukraine, 61166

PhD

V. V. Popovskyy Department of Infocommunication Engineering

References

  1. Janevski, T., Jankovic, M., Markus, S. (2017). Quality of service regulation manual. Telecommunication development Bureau, 173.
  2. Mellouk, А., Tran, Н. А., Hoceini, S. (2013). Quality of Experience for Multimedia: Application to Content Delivery Network Architecture. J. Wiley and Sons, 235. doi: http://doi.org/10.1002/9781118649367
  3. Barreiros, M., Lundqvist, P. (2016). QOS-Enabled Networks: Tools and Foundations. Wiley Series on Communications Networking & Distributed Systems, Wiley, 254. doi: http://doi.org/10.1002/9781119109136
  4. Stallings, W. (2015). Foundations of modern networking: SDN, NFV, QoE, IoT, and Cloud. Addison-Wesley Professional, 560.
  5. Dubey, R. K., Kumar, A. (2015). Comparison of subjective and objective speech quality assessment for different degradation/noise conditions. International Conference on Signal Processing and Communication (ICSC), 261–266. doi: http://doi.org/10.1109/icspcom.2015.7150659
  6. Liotou, E., Tsolkas, D., Passas, N. (2016). A roadmap on QoE metrics and models. 23rd International Conference on Telecommunications (ICT). Thessaloniki, 1–5. doi: http://doi.org/10.1109/ict.2016.7500363
  7. Arndt, S., Antons, J.-N., Gupta, R., ur Rehman Laghari, K., Schleicher, R., Moller, S., Falk, T. H. (2013). Subjective quality ratings and physiological correlates of synthesized speech. Fifth International Workshop on Quality of Multimedia Experience (QoMEX). Klagenfurt am Worthersee, 152–157. doi: http://doi.org/10.1109/qomex.2013.6603229
  8. Mello, C. A. B., Albuquerque, R. Q. (2015). Reference-Free Speech Quality Assessment for Mobile Phones Based on Audio Perception. IEEE International Conference on Systems, Man, and Cybernetics. Kowloon, 2413–2417. doi: http://doi.org/10.1109/smc.2015.422
  9. Gaoxiong, Y., Wei, Z. (2012). The Perceptual Objective Listening Quality Assessment algorithm in telecommunication: Introduction of ITU-T new metrics POLQA. 1st IEEE International Conference on Communications in China (ICCC). Beijing, 351–355. doi: http://doi.org/10.1109/iccchina.2012.6356906
  10. Radwan, O. (2017). An architectural model for managing quality of experience of web services. Ninth International Conference on Ubiquitous and Future Networks (ICUFN). Milan, 513–518. doi: http://doi.org/10.1109/icufn.2017.7993837
  11. Hikmatullah, M. R., Haryadi, S. (2017). Perceptual evaluation of speech quality over the top call service. 3rd International Conference on Wireless and Telematics (ICWT). Palembang, 181–185. doi: http://doi.org/10.1109/icwt.2017.8284163
  12. Mancas, C., Mocanu, M. (2013). Enhancing QoS/QoE in multimedia networks. IEEE International Conference on Communications Workshops (ICC). Budapest, 637641. doi: http://doi.org/10.1109/iccw.2013.6649311
  13. ITU-T G.109. (2007). Amendment 1 New Appendix I – The E-model-based quality loops for predicting speech transmission quality and user satisfaction from time-varying transmission impairments, 18.
  14. ITU-T G.107. (2015). The E-model: a computational model for use in transmission planning.
  15. Seppänen, J., Varela, M., Sgora, A. (2014). An autonomous QoE-driven network management framework. Journal of Visual Communication and Image Representation, 25 (3), 565–577. doi: http://doi.org/10.1016/j.jvcir.2013.11.010
  16. Lemeshko, O. V., Yeremenko, O. S., Nevzorova, O. S. (2020). Potokovі modelі ta metodi marshrutizatsії v іnfokomunіkatsіynikh merezhakh: vіdmovostіykіst, bezpeka, masshtabovanіst. Kharkiv: NURE, 308. doi: http://doi.org/10.30837/978-966-659-282-1
  17. Kron, G. (1949). Tensor analysis of networks. J. Wiley and Sons, 635.
  18. Lemeshko, O., Yevdokymenko, M., Anad Alsaleem, N. Y. (2018). Development of the tensor model of multipath qоe-routing in an infocommunication network with providing the required quality rating. Eastern-European Journal of Enterprise Technologies, 5 (2 (95)), 40–46. doi: http://doi.org/10.15587/1729-4061.2018.141989
  19. Lemeshko, O., Evseeva, O., Yevdokymenko, M. (2018). Tensor Flow-Based Model of Quality of Experience Routing. 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). Lviv, 1005–1008. doi: http://doi.org/10.1109/tcset.2018.8336364
  20. Yevdokymenko, M. (2019). Routing Tensor Model with Providing Multimedia Quality. International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). Kyiv, 819–824, doi: http://doi.org/10.1109/picst47496.2019.9061280
  21. Lemeshko, O., Yevdokymenko, M., Yeremenko, O., Nevzorova, O., Snihurov, A., Kovalenko, T. (2019). Fast ReRoute Model with VoIP Quality of Experience Protection. 3rd IEEE International Conference Advanced Information and Communication Technologies (AICT). Lviv, 16–21, doi: http://doi.org/10.1109/aiact.2019.8847918
  22. Lemeshko, O. V., Yeremenko, O. S., Hailan, A. M. (2016). QoS solution of traffic management based on the dynamic tensor model in the coordinate system of interpolar paths and internal node pairs. Radio Electronics & Info Communications (UkrMiCo): Proceedings of the International Conference, 1–6. doi: http://doi.org/10.1109/ukrmico.2016.7739625
  23. Lemeshko, O., Yeremenko, O. (2016). Routing Tensor Model Presented in the Basis of Interpolar Paths and Internal Node Pairs. Problems of Infocommunications Science and Technology (PIC S&T): Proceedings of the Third International Scientific-Practical Conference. Kharkiv, 201–204. doi: http://doi.org/10.1109/infocommst.2016.7905381
  24. Lemeshko, O., Yevdokymenko, M., Hu, Z., Yeremenko, O. (2020). Inter-domain routing method under normalized Quality of Service based on hierarchical coordination. CEUR Workshop Proceedings of The Third International Workshop on Computer Modeling and Intelligent Systems (CMIS), 2608, 394–408.
  25. Lemeshko, O., Yeremenko, O., Yevdokymenko, M. (2018). Tensor Model of Fault-Tolerant QoS Routing with Support of Bandwidth and Delay Protection. IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). Lviv, 135–138. doi: http://doi.org/10.1109/stc-csit.2018.8526707

Published

2020-06-30

How to Cite

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

Issue

Section

Systems and Control Processes: Original Research