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Mathematical Simulation of Infocommunication Networks Applying Chaos Theory

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The advent and wide usage of computer networks as well as an increasing number of various network services have resulted in the fact that the network traffic has become more complex and unpredictable. These properties have become especially apparent with the appearance of high-speed data transmission technology. It is connected with the fact that one of the main quality indeces (QoS) of operation of packet transmission networks is the number of lost packets. The loss of packets results to additional network traffic and, finally, “congestions”. At high speeds of data transmission packet losses, expressed in portions of a per-cent, lead to considerable information losses. It has been shown in numerous papers devoted to the research of network traffic that the abovementioned phenomena are related to the properties of the traffic self–similarity which is mainly caused by the TCP protocol behaviour. However to date models have not been offered which adequately describe the behaviour of the communication networks of information systems and which allow to apply the whole arsenal of classic methods of analyzing nonlinear dynamic systems.
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Twórcy
autor
  • Kharkov National University of Radio Electronics, Lenin Ave, 14, Kharkov, 61166, Ukraine
autor
  • V.N. Karazin Kharkiv National University, Svobody Sq. 4, 61022, Kharkov, Ukraine
autor
  • PAO "Ukrtelekom" Kharkov branch
Bibliografia
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  • 4. C. Zhu, O.W.W. Yang, J. Aweya, M. Oullete, D.Y. Montuno. 2002. A comparison of active queue management algorithms using the OPNET Modeler. IEEE Communication Magazine, 40(6):158-167.
  • 5. H. Kantz and T. Schreiber. 2003. Nonlinear Time Series Analysis, 2nd edition, Cambridge University Press, Cambridge, 388.
  • 6. H. Tong. 1990. Non-Linear Time Series: A dynamical System Approach, Oxford University Press, 564.
  • 7. J Hamilton, 1994. Time series analysis, Princeton University Press, 820.
  • 8. J. Padhye, V. Firoiu, D. Towsley and J Kurose. 1998. Modelling TCP Throughput: A Simple Model and its Empirical Validation. Proceedings of SIGCOMM’98, Vancouver, CA, September, 303-314.
  • 9. J. Nagle. 1984. Congestion Control in IP/TCP Internetworks, RFC 896, 9.
  • 10. K. Wróbel, S. Styła, A. Sumorek. 2012. Use of GIS systems in the construction of hydraulic model networks. ECONTECHMOD An International Quarterly Journal On Economics In Technology, New Technologies And Modelling Processes. – Vol. I, No 2, 63-67.
  • 11. Karpukhin A. V. 2009. Osobennosti realizacii protokola TCP v sovremennih kompyuternix setyax. Sistemi obrobki informacii - Kh.: KHUPS, – Vip.6(80), – 49-53., Ukraine.
  • 12. L. Brakmo, L. Peterson. 1995. TCPVegas: End to End Congestion Avoidance on a Global Internet.IEEE Journal of Selected Areas in Communications, 13(8):1465-1480.
  • 13. Leland W.E., Taqqu M.S., Willinger W., Wilson D.V. 1994. On the self-similar Nature of Ethernet Traffic. IEEE/ACM Transactions of Networking, Vol. 2(1), 1-15.
  • 14. Michael T. Rosenstein, James J Collins, and Carlo J. De Luca. 1993. A practical method for calculating largest Lyapunov exponent from small data sets. Journal Physica D, Volume 65 Issue 1-2, 117-134.
  • 15. O. Yaremko, B. Stryhalyuk, T Maksymyuk, O. Lavriv, D Kozhurov. 2013. The optimal power control method in multiuser cellular networks. ECONTECHMOD An International Quarterly Journal On Economics In Technology, New Technologies And Modelling Processes. – Vol. 2, No 1. 63-67.
  • 16. Packard N. H., Crutchfield J. P., Farmer J. D. Shaw R. S. 1980. Geometry from a Time Series. Phys. Rev. Lett., 45, 712-716.
  • 17. Patrick E McSharry. 2005. The Danger of wishing for chaos. Nonlinear dynamics, psychology, and life sciences 9(4):375-397.
  • 18. Peitgen H. O. Jurgen H. Saupe D., 1992. Chaos and Fractals – New Frontiers of Science, Springer-Verlag, NY, 864.
  • 19. R. Hegger, H. Kantz, and T. Schreiber. 1999. Practical implementation of nonlinear time series methods: The TISEAN package, CHAOS 9, 413.
  • 20. S.Floys.1995.Simulator tests.Available in ftp://ftp.ee.lbl.gov/papers/simtests. ps.Z ns is available at http://www-nrg.ee.lbl.gov/.
  • 21. Sebastiano Manzan. 2003. Essays in nonlinear economic dynamics, Thela Thesis, 116.
  • 22. Simulator NS-3 and concomitant documentation [Online resource]. – Access mode: http://nsnam.org/.
  • 23. The package of TISEAN programs and concomitant documentation [Online resource]. – Access mode: http://www.mpipks-dresden.mpg.de/~tisean/
  • 24. V. Jacobson. 1988. Congestion Avoidance and Control. In Proceedings of the SIGCOMM'88 Symposium, 314-332.
  • 25. Veres A., Boda M. 2000. The chaotic nature of TCP congestion control. Computer and Communication Societies Proceedings. IEEE, Vol. 3, 1715-1723.
  • 26. W. R. Stevens. 2003. TCP/IP protocols. Practical Guide for Programmers. BHV, 672.
  • 27. W. Willinger, M. S. Taqqu, A. Erramilli. 1996. A bibliographical guide to self-similar traffic and performance modeling for modern high-speed networks, Stochastic Networks: Theory and Applications (Oxford) (F. P.Kelly, S. Zachary, and I. Ziedins, eds.), Royal Statistical Society Lecture Notes Series, Oxford University Press Vol. 4, 339-366.
  • 28. W.Feng, P.Tinnakornsrisuphap. 2000. The Adverse Impact of the TCP Congestion-Control Mechanism in Distributed Systems. In Proceedings of International Conference on Parallel Processing (ICPP'00), 299-306.
  • 29. W. Feng, P. Tinnakornsrisuphap. 2000. The failure of TCP in High-Performance Computational Grids. In Proceedings of International Conference on Parallel Processing (ICPP'00), 21-31.
  • 30. Yu. Ryshkovets, P. Zhezhnych. 2013. Information model of Web-gallery taking into account user’s interests. ECONTECHMOD An International Quarterly Journal On Economics In Technology, New Technologies And Modelling Processes. – Vol. 2, No 3, 59-63.
Typ dokumentu
Bibliografia
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