SOFTWARE SIMULATION MODEL OF COMPUTER NETWORK WITH MULTIFRACTAL TRAFFIC SIMULATION BASED ON MARKOV CHAIN

Authors

DOI:

https://doi.org/10.24025/2306-4412.4.2022.269137

Keywords:

computer networks, computer simulation model, network traffic, fractal dimension, Hurst expo-nent, multifractality

Abstract

In the work, a software simulation model of a computer network with multifractal traffic simulation based on the Markov chain for testing routing algorithms is developed. A method based on the theory of complex networks has been developed to generate the structure of a computer network. To simulate network traffic, a method of generating a multifractal binary sequence using the Markov chain has been developed. A computer network in a developed model is represented by a fully connected undirected weighted graph, in which nodes are routers, and edges are network connections between them. The weight of the edges is the inverse of the bandwidth of the communication channel. Nodes contain queues in which received packets are placed before determining the route of their dispatch and sending them to the next node. Time in the model is represented by discrete iterations. Routing is carried out on the basis of those algorithms, which must be tested on the model. To simulate network traffic, in the developed software simulation model, a method of generating a binary multifractal sequence based on Markov chains with a stochastic automaton, which makes possible to control the fractal dimension of the binary series on different scales, is proposed. As a result of a numerical experiment, the fact of the possibility of adjusting the Hurst index on a given time scale has been established. It is shown that the obtained time series using the cascading generator of the binary sequence have multifractal properties. That is, the cascading generator has more possibilities for adaptation to real examples of binary traffic. The scientific novelty of the conducted research is as follows: 1. The method of network traffic generation based on the Markov chain has been improved, which differs from the known ones in that it uses a cascade model of the generator of a binary numerical sequence at the "packet present" - "packet absent" level and allows to generate a traffic with multifractal properties with the possibility of their adjustment. 2. A method of software simulation modeling of a computer network based on the theory of complex networks and an improved method of generating network multifractal traffic, which allows to test routing algorithms and protocols, has been developed.

Author Biographies

Hanna Drieieva, Central Ukrainian National Technical University

Postgraduate Student

Oleksandr Drieiev, Central Ukrainian National Technical University

Candidate of Technical Sciences, Associate Professor

Yelyzaveta Meleshko, Central Ukrainian National Technical University

Doctor of Technical Sciences, Professor

Iryna Myronets, Cherkasy State Technological University

Candidate of Technical Sciences, Associate Professor

References

A.-L. Barabási, Network Science. Cambridge University Press, 2018. [Online]. Available: http://networksciencebook.com/

В. В. Пасічник, та Н. М. Іванущак, "Дослідження та моделювання складних мереж", Східно-Європейський журнал передових технологій, вип. 2, № 3 (44), с. 43-48, 2010.

V. А. Traag, Algorithms and Dynamical Models for Communities and Reputation in Social Networks. Springer International Publishing, 2014, p. 229. [Online]. Available: https://doi.org/10.1007/978-3-319-06391-1

D. J. Watts, and S. H. Strogatz, "Collective dynamics of "small-world" networks", Nature, vol. 393 (6684), pp. 440-442, 1998. [Online]. Available: https://www.nature.com/articles/30918

S. Robert, and J. Y. Le Boudec, "New models for pseudo self-similar traffic", Performance Evaluation, vol. 30 (1-2). pp. 57-68, 1997.

G. Horn, A. Kvalbein, J. Blomskøld, and E. Nilsen, "An empirical comparison of generators for self-similar simulated traffic", Performance Evaluation, vol. 64 (2), pp. 162-190, 2007.

T. Sobh, K. Elleithy, and A. Mahmood, Eds., Novel Algorithms and Techniques in Telecommunications and Networking. Springer, 2010, pp. 41-46.

H. M. Drieieva, O. A. Smirnov, O. M. Drieiev, and T. V. Smirnova, "A fractal analysis of a Markov chain based self-similar traffic generator", Central Ukrainian Scientific Bulletin, Engineering sciences, vol. 2 (33), pp. 161-172, 2019.

H. Drieieva, O. Drieiev, Ye. Meleshko, M. Yakymenko, and V. Mikhav, "A method of determining the fractal dimension of network traffic by its probabilistic properties and experimental research of the quality of this method", CEUR-WS, vol. 3171, pp. 1694-1707, Gliwice, Poland, 2022. [Online]. Available: http://ceur-ws.org/Vol-3171/paper120.pdf

Є. Мелешко, Г. Дрєєва, та В. Міхав, "Програмна імітаційна модель комп’ютерної мережі для тестування алгоритмів марштуртизації трафіку", на міжнар. наук.-техн. конф. "Автоматика, комп’ютерно-інтегровані технології та проблеми енергоефективності в промисловості і сільському господарстві (АКІТ-2022"). Кропивницький: Ексклюзив-Систем, 2022, с. 26-27.

A.-L. Barabási, and R. Albert, "Emergence of scaling in random networks", Science, vol. 286, no. 5439, pp. 509-512, 1999. [Online]. Available: https://doi.org/10.1126/science.286.5439.509

G. Millán, and G. Lefranc, "A fast multifractal model for self-similar traffic flows in high-speed computer networks", Information Technology and Quantitative Management (ITQM2013). Procedia Computer Science, 17, pp. 420-425, 2013.

E. Areström, and N. Carlsson, "Early online classification of encrypted traffic streams using multi-fractal features", in IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2019, pp. 84-89. doi: 10.1109/INFCOMW.2019.8845127.

V. J. Ribeiro, Z.-L Zhang, S. Moon, and C. Diot, "Small-time scaling behavior of Internet backbone traffic", Computer Networks, vol. 48, pp. 315-334, 2005. doi: 10.1016/j.comnet.2004.11.012

"Hurst exponent evaluation and R/S-analysis in Python", GitHub - Mottl/hurst. [Online]. Available: https://github.com/Mottl/hurst

Downloads

Published

2022-12-27

How to Cite

Drieieva, H., Drieiev, O., Meleshko, Y., & Myronets, I. (2022). SOFTWARE SIMULATION MODEL OF COMPUTER NETWORK WITH MULTIFRACTAL TRAFFIC SIMULATION BASED ON MARKOV CHAIN. Bulletin of Cherkasy State Technological University, (4), 58–66. https://doi.org/10.24025/2306-4412.4.2022.269137

Issue

No. 4 (2022): VISNYK Cherkaskogo derzhavnogo tehnologichnogo universitetu

Section

Information Technologies

URN

http://vtn.chdtu.edu.uaurn:2306:44554.2022.269137

License

Copyright (c) 2022 Ганна Миколаївна Дрєєва, Олександр Миколайович Дрєєв, Єлизавета Владиславівна Мелешко, Ірина Валеріївна Миронець

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The authors who publish in this journal agree to the following terms:

The authors reserve the right to authorship of their work and give the journal the right to first publish this work under the terms of the Creative Commons Attribution License CC BY-NC, which allows other persons to freely distribute published work with a mandatory reference to authors of the original work and the first publication of the work in this journal.

Authors have the right to conclude separate additional agreements for the non-exclusive distribution of the paper in the form in which it was published by this journal (for example, posting work in electronic repository or publishing as part of a monograph), provided that the link to the first publication in this journal is maintained.

The journal policy allows and encourages authors to post on the Internet (for example, in repositories of institutions or on personal websites) the manuscript of work, both before the submission of this manuscript to the editorial staff, and during its editorial work, as it contributes to the emergence of productive scientific discussion and positively affects the efficiency and dynamics of published work citation (see The Effect of Open Access).