Development of the method to control telecommunication network congestion based on a neural model
DOI:
https://doi.org/10.15587/1729-4061.2019.164087Keywords:
telecommunication network, sensitivity function, neural network, dynamic system, queue controlAbstract
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 selectionReferences
- Tanenbaum, A. S., Wetherall, D. J. (2010). Computer Networks. PrenticeHall, 960.
- Stallings, W. (2016). Foundations of Modern Networking: SDN, NFV, QoE, IoT, and Cloud. Pearson Education, Inc., OldTappan, New Jersey, 544.
- Mao, G. (2017). Connectivity of Communication Networks. Springer, 435. doi: https://doi.org/10.1007/978-3-319-52989-9
- Vinogradov, N. A., Drovovozov, V. I., Lesnaya, N. N., Zembickaya, A. S. (2006). Analiz nagruzki na seti peredachi dannyh v sistemah kritichnogo primeneniya. Zviazok, 1 (61), 9–12.
- Snarskiy, A. A., Lande, D. V. (2015). Modelirovanie slozhnyh setey. Kyiv: Inzhiniring, 212.
- Bonaventure, O. (2018). Computer Networking: Principles, Protocols and Practices, 272. Available at: https://www.computer-networking.info/2nd/cnp3bis.pdf
- Keshav, S. (1991). Congestion Control in Computer Networks. University of California.
- Kurose, J. F., Ross, K. W. (2017). Computer Networking: A Top-Down Approach. Pearson Education, Inc., 864.
- Göransson, P., Black, C., Culver, T. (2017). Software Defined Networks: A Comprehensive Approach. Morgan Kaufmann, US, 436.
- Korolkova, A. V., Kulyabov, D. S., Tchernoivanov, A. I. (2009). On the classification of RED algorithms. Vestnik Rossiyskogo universiteta druzhby narodov. Seriya: Matematika, informatika, fizika, 3, 34–46.
- Maximov, V. V., Chmykhun, S. O. (2014). Classification of algorithms of controlling networks congestions. Naukovi zapysky Ukrainskoho naukovo-doslidnoho instytutu zviazku, 5 (33), 73–79.
- Maxymov, V. V., Chmykhun, S. O. (2015). Research of the algorithm of controlling congestion TCP VENO. Telekomunikatsiyni ta informatsiyni tekhnolohiyi, 4, 30–36.
- Tomovich, R., Vukobratovich, M. (1972). Obshchaya teoriya chuvstvitel'nosti. Moscow: Sovetskoeradio, 240.
- Shooman, M. L. (2002). Reliability of Computer Systems and Networks – Fault Tolerance, Analysisand Design. JohnWiley&Sons, 546.
- Toroshanko, Ya. I. (2016). Management reliability of telecommunication network on the analysis of sensitivity of the complex systems. Telekomunikatsiyni ta informatsiyni tekhnolohiyi, 3, 31–36.
- Toroshanko, Ya. I. (2016). Sensitivity analysis of systems of mass service on the base of model of adaptation and regulation of foreign traffic. Herald of Khmelnytskyi national university, 6 (243), 171–175.
- Lu, Z., Pan, Q., Wang, L., Wen, X. (2016). Overload Control for Signaling Congestion of Machine Type Communications in 3GPP Networks. PLOS ONE, 11 (12), e0167380. doi: https://doi.org/10.1371/journal.pone.0167380
- Mel'nikov, D. A. (1999). Informacionnye processy v komp'yuternyh setyah. Moscow, 256.
- Gol'dshteyn, B. S., Sokolov, N. A., Yanovskiy, G. G. (2014). Seti svyazi. Sankt-Peterburg: «BHV – Peterburg», 400.
- Tarhov, D. A. (2014). Neyrosetevye modeli i algoritmy. Moscow: Izdatel'stvo «Radiotekhnika», 352.
- Steklov, V. K., Berkman, L. N., Kilchytskyi, Ye. V. (2004). Optymizatsiya ta modeliuvannia prystroiv i system zviazku. Kyiv: Tekhnika, 576.
- Galushkin, A. I. (2010). Neyronnye seti: osnovy teorii. Moscow: Goryachaya liniya – Telekom, 496.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 Nikolay Vinogradov, Mikhailo Stepanov, Yaroslav Toroshanko, Vyacheslav Cherevyk, Alina Savchenko, Valerii Hladkykh, Oleksandr Toroshanko, Tetiana Uvarova
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.
