Basic characteristics of networks with self-similar traffic simulation

• Autorzy: Aleksander Marek , Odarchenko Roman , Gnatyuk Sergiy , Kantor Tadeusz

Identyfikatory

DOI

10.24136/atest.2019.023

• Języki publikacji: EN

Abstrakty

EN

This paper is devoted to simulations the networks with self-similar traffic. The self-similarity in the stochastic process is identified by calculation of the herst parameter value. Based on the results, received from the experimental research of network perfomance, we may conclude that the observed traffic in real-time mode is self-similar by its nature. Given results may be used for the further investigation of network traffic and work on the existing models of network traffic (particularly for new networks concepts like IoT, WSN, BYOD etc) from viewpoint of its cybersecurity. Furthermore, the adequacy of the description of real is achieved by complexifying the models, combining several models and integration of new parameters. Accordingly, for more complex models, there are higher computing abilities needed or longer time for the generation of traffic realization.

Słowa kluczowe

EN

traffic; network traffic models; fractional Brownian motion; self-similarity; RMD algorithm

PL

ruch drogowy; modele ruchu sieciowego; ułamkowy ruch Browna; samopodobieństwo; algorytm RMD

• Wydawca: Instytut Naukowo-Wydawniczy "SPATIUM". sp. z o.o.
• Czasopismo: Autobusy : technika, eksploatacja, systemy transportowe
• Rocznik: 2019
• Tom: R. 20, nr 1-2
• Strony: 137--141
• Opis fizyczny: Bibliogr. 11 poz., il., wykr.

Twórcy

autor

Aleksander Marek

• Institute of Technology, State University of Applied Sciences in Nowy Sącz

autor

Odarchenko Roman

• Academic Dept of Telecommunication Systems, National Aviation University (Kyiv, Ukraine)

autor

Gnatyuk Sergiy

• Academic Dept of IT-Security, National Aviation University (Kyiv, Ukraine)

autor

Kantor Tadeusz

• Institute of Technology State University of Applied Sciences in Nowy Sącz

Bibliografia

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• 3. О. Sheluhin, А Tenyakshev, А. Osin. Modeling of information systems. Study guide, М.: Radiotechnica, 2005, 368 p.
• 4. А. Privalov, М. Bayeva. Modeling of self-similar traffic// News from the Samara science centre of Russian science academy, P. 1041-1046.
• 5. А. Kostromitskyi. Approaches to self-similar traffic modeling // Eastern-Europe journal of modern technologies, 2010, 4/7 (46), С. 46-49.
• 6. R. Shyhaliyev. Аnalysis and classification of network traffic in computer networks // Information technologies problems, 2010, № 2, С. 15-23.
• 7. Е. Dobrovolskyi, О. Nechyporuk. Мodeling of the network traffic with the use of context methods // Science works ОNАZ named after. О. S. Popov, 2005, № 1, P. 24-32.
• 8. І. Matychyn, V. Onyshchevko. Мodeling and analysis of telecommunication systems and networks traffic // Visnyk DUIKT, 2013, № 4, P. 20-27.
• 9. N. Trenogyn, D. Е. Sokolov. Fractal features of network traffic in a client-server information system // Vestnyk NII SYVPT, P. 163-172.
• 10. A. Kostromitsky Network traffic analysis and monitoring tool review, Access mode: http://pi.314159.ru/volotka/volotka1.htm
• 11. Y. Semenov Network modeling, Access mode: ttp://citforum.ck.ua/nets/semenov/4/45/modl4517.shtml

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).