DOI: https://doi.org/10.15587/1729-4061.2019.164087

Development of the method to control telecommunication network congestion based on a neural model

Nikolay Vinogradov, Mikhailo Stepanov, Yaroslav Toroshanko, Vyacheslav Cherevyk, Alina Savchenko, Valerii Hladkykh, Oleksandr Toroshanko, Tetiana Uvarova

Abstract


The circuit of congestion control using feedback by the sign of function of sensitivity to telecommunications network performance was considered. To determine a given function, the use of a simple neural network model of a dynamic system was proposed. Control over the existence or a threat of congestion is executed based on the analysis of the length of a queue at the side of information receiver. To analyze the system, the cost function was determined as the objective function of congestion existence. The proposed algorithm of optimal control ensures the formation of a control signal in such a way that the system output should maximally match the pre-established features – the key indicators for network efficiency. The congestion control circuit with the feedback based on the sign of sensitivity of the function of system performance was developed. The sign of performance sensitivity provides an optimal direction to configure the data source rate.

The neural model for a multi-step prediction of the state of the queue at the side of the telecommunication network receiver was proposed. If the neural network is configured to monitor the dynamics of the system and shows that the quadratic error is negligible, it is believed that the executed step corresponds to the system output, predicted in advance.

The algorithm of additive increase/multiple decrease, which determines the change of the data source rate, depending on the sign of function of sensitivity of performance indicator was proposed. This algorithm is an alternative system of congestion prediction and flow control based on the threshold queue filling.

A comparative analysis of the effectiveness of controlling circuits for congestion detection based on queues and on the function of sensitivity of telecommunication network performance was performed. It was shown that the magnitude of the queue and fluctuation in the source rate is smaller than that for the queue-based circuit.

Results from modeling the performance of the proposed circuit show that the circuit based on a sensitivity function has better key performance indicators in comparison with the conventional circuit of queue threshold selection

Keywords


telecommunication network; sensitivity function; neural network; dynamic system; queue control

References


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GOST Style Citations


Tanenbaum A. S., Wetherall D. J. Computer Networks. 5th ed. PrenticeHall, 2010. 960 p.

Stallings W. Foundations of Modern Networking: SDN, NFV, QoE, IoT, and Cloud. Pearson Education, Inc., OldTappan, New Jersey, 2016. 544 p.

Mao G. Connectivity of Communication Networks. Springer, 2017. 435 p. doi: https://doi.org/10.1007/978-3-319-52989-9 

Analiz nagruzki na seti peredachi dannyh v sistemah kritichnogo primeneniya / Vinogradov N. A., Drovovozov V. I., Lesnaya N. N., Zembickaya A. S. // Zviazok. 2006. Issue 1 (61). P. 9–12.

Snarskiy A. A., Lande D. V. Modelirovanie slozhnyh setey: ucheb. pos. Kyiv: Inzhiniring, 2015. 212 p.

Bonaventure O. Computer Networking: Principles, Protocols and Practices. 2018. 272 p. URL: https://www.computer-networking.info/2nd/cnp3bis.pdf

Keshav S. Congestion Control in Computer Networks. University of California, 1991.

Kurose J. F., Ross K. W. Computer Networking: A Top-Down Approach. 7th ed. Pearson Education, Inc., 2017. 864 p.

Göransson P., Black C., Culver T. Software Defined Networks: A Comprehensive Approach. 2nd ed. Morgan Kaufmann, US, 2017. 436 p.

Korolkova A. V., Kulyabov D. S., Tchernoivanov A. I. On the classification of RED algorithms // Vestnik Rossiyskogo universiteta druzhby narodov. Seriya: Matematika, informatika, fizika. 2009. Issue 3. P. 34–46.

Maximov V. V., Chmykhun S. O. Classification of algorithms of controlling networks congestions // Naukovi zapysky Ukrainskoho naukovo-doslidnoho instytutu zviazku. 2014. Issue 5 (33). P. 73–79.

Maxymov V. V., Chmykhun S. O. Research of the algorithm of controlling congestion TCP VENO // Telekomunikatsiyni ta informatsiyni tekhnolohiyi. 2015. Issue 4. P. 30–36.

Tomovich R., Vukobratovich M. Obshchaya teoriya chuvstvitel'nosti. Moscow: Sovetskoeradio, 1972. 240 p.

Shooman M. L. Reliability of Computer Systems and Networks – Fault Tolerance, Analysisand Design. JohnWiley&Sons, 2002. 546 p.

Toroshanko Ya. I. Management reliability of telecommunication network on the analysis of sensitivity of the complex systems // Telekomunikatsiyni ta informatsiyni tekhnolohiyi. 2016. Issue 3. P. 31–36.

Toroshanko Ya. I. Sensitivity analysis of systems of mass service on the base of model of adaptation and regulation of foreign traffic // Herald of Khmelnytskyi national university. 2016. Issue 6 (243). P. 171–175.

Overload Control for Signaling Congestion of Machine Type Communications in 3GPP Networks / Lu Z., Pan Q., Wang L., Wen X. // PLOS ONE. 2016. Vol. 11, Issue 12. P. e0167380. doi: https://doi.org/10.1371/journal.pone.0167380 

Mel'nikov D. A. Informacionnye processy v komp'yuternyh setyah. Moscow, 1999. 256 p.

Gol'dshteyn B. S., Sokolov N. A., Yanovskiy G. G. Seti svyazi. Sankt-Peterburg: «BHV – Peterburg», 2014. 400 p.

Tarhov D. A. Neyrosetevye modeli i algoritmy. Moscow: Izdatel'stvo «Radiotekhnika», 2014. 352 p.

Steklov V. K., Berkman L. N., Kilchytskyi Ye. V. Optymizatsiya ta modeliuvannia prystroiv i system zviazku. Kyiv: Tekhnika, 2004. 576 p.

Galushkin A. I. Neyronnye seti: osnovy teorii. Moscow: Goryachaya liniya – Telekom, 2010. 496 p.







Copyright (c) 2019 Nikolay Vinogradov, Mikhailo Stepanov, Yaroslav Toroshanko, Vyacheslav Cherevyk, Alina Savchenko, Valerii Hladkykh, Oleksandr Toroshanko, Tetiana Uvarova

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061