Open AccessTraffic Behavior Analysis During the Transition from One Level of the Network Hierarchy to Another
Author(s) -
Lyudmila Gomazkova,
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Oleg Bezbozhnov,
Osamah Al-Qadi,
Sergey Galich,
Denis Chadaev,
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Publication year - 2021
Publication title -
nbi tehnologii/nbi tehnologii
Language(s) - English
Resource type - Journals
eISSN - 2713-1564
pISSN - 2658-3593
DOI - 10.15688/nbit.jvolsu.2021.3.1
Subject(s) - degree (music) , hierarchy , computer science , traffic generation model , network traffic simulation , similarity (geometry) , hierarchical network model , network simulation , trace (psycholinguistics) , network traffic control , data mining , network topology , distributed computing , computer network , artificial intelligence , linguistics , philosophy , physics , network packet , acoustics , economics , market economy , image (mathematics)
The hierarchical network model is the most preferable in the design of computer networks, as it allows you to create a more stable structure of network, rationally allocate available resources, and also provide a higher degree of data protection. In this work, the study of the behavior of the traffic during the transition from one level of the network hierarchy to another, based on the study of the values of the traffic self-similarity degree during this transition. For the study, a simulation model of a computer network with a hierarchical topology was developed using the NS-3 simulator. Also, a window application was developed in the Visual C# programming language. With the help of this application the degree of self-similarity of the traffic was investigated using the files obtained as a result of processing the trace file. Thus, as a result of the study, it can be stated that any changes in the degree of self-similarity of the network traffic when this traffic moves from one level of the hierarchy to another level depends on such a condition as the direction of traffic movement. The initial degree of selfsimilarity of network traffic also effects on the network traffic self-similarity degree.