scholarly journals Tinklo apkrovos savastingumo tyrimas realiu laiku

2010 ◽  
Vol 53 ◽  
pp. 100-105
Author(s):  
Liudvikas Kaklauskas ◽  
Leonidas Sakalauskas
Keyword(s):  
Network Traffic ◽  
Stability Index ◽  
The Self ◽  
Hurst Index ◽  
Robust Method ◽  
Self Similarity ◽  
Software Module ◽  
On Line ◽  
Computer Based ◽  
Increment Ratio

Straipsnyje analizuojami indikatoriai, taikomi tinklo apkrovos savastingumui tirti: Hursto indeksas, stabilumo indeksas, IR (Increment Ratio) statistika. Kompiuteriniu modeliavimu ištirtas šių indikatorių tinkamumas tinklo apkrovos savastingumui vertinti realiu laiku. Sukurta programinių modulių biblioteka SSE (Self-Similarity Estimator), skirta fiksuoti ir agreguoti tinklo duomenų paketus, vertinanti tinklo apkrovos srautų savastingumą realiu laiku. Naudojant SSE programinių modulių biblioteką, suformuotų laiko eilučių Hursto indeksas ir IR statistika apskaičiuotos naudojant analitines formules, o stabilumo indeksas – robastiniu empirinių kvantilių regresijos metodu. Modulių bibliotekos SSE analizės efektyvumas ištirtas kompiuterinio modeliavimo būdu apskaičiuojant savastingumo indikatorius stabiliųjų procesų realizacijoms.Pagrindiniai žodžiai: savastingumas (self-similarity), Hursto indeksas, stabilumo indeksas, IR statistika.The Real-time Mode Research of Network Traffic FractalityLiudvikas Kaklauskas, Leonidas Sakalauskas Summaryhe article analyses the indicators implemented for investigating the network self-similarity: the Hurst index, stability index, IR (Increment Ratio) statistics. The suitability of these indicators for the on-line estimation of network traffic self-similarity was investigated by applying computer-based modelling. The software SSE (Self-Similarity Estimator) module library was developed; it was designed for the recording and aggregation of network traffic packages as well as for the on-line estimation of network traffic self-similarity. By using the SSE software module library, the Hurst index and the IR statistics of time series were estimated by applying analytic formulas, and the index of stability was estimated applying the robust method of regression of empirical quantiles. The efficiency of the analysis of the SSE module library was investigated by estimating the self-similarity indicators for realisation of the stabile processes while applying the method of computer-based modelling.

On the self-similarity of traffic generated by network traffic simulators

2015 ◽  
pp. 285-311 ◽  
Author(s):  
Diogo A.B. Fernandes ◽  
Miguel Neto ◽  
Liliana F.B. Soares ◽  
Mário M. Freire ◽  
Pedro R.M. Inácio
Keyword(s):  
Network Traffic ◽  
The Self ◽  
Self Similarity

Analysis of the Impact of Intensive Attacks on the Self-Similarity Degree of the Network Traffic

2008 ◽  
Author(s):  
Pedro R. M. Inácio ◽  
Mário M. Freire ◽  
Manuela Pereira ◽  
Paulo P. Monteiro
Keyword(s):  
Network Traffic ◽  
The Self ◽  
Self Similarity ◽  
Similarity Degree ◽  
The Impact

Effects of upper‐layer protocols on the self‐similarity of network traffic

2001 ◽  
Vol 84 (7) ◽  
pp. 19-30 ◽  
Author(s):  
Yoshiaki Sumida ◽  
Hiroyuki Ohsaki ◽  
Masayuki Murata ◽  
Hideo Miyahara
Keyword(s):  
Network Traffic ◽  
The Self ◽  
Self Similarity

scholarly journals Statistical research and modeling network traffic

2021 ◽  
Vol 244 ◽  
pp. 07002
Author(s):  
Tatiana Tatarnikova ◽  
Igor Sikarev ◽  
Vladimir Karetnikov ◽  
Artem Butsanets
Keyword(s):  
Network Traffic ◽  
Least Squares Method ◽  
Regression Line ◽  
The Self ◽  
Logarithmic Scale ◽  
Self Similarity ◽  
Statistical Research ◽  
Similarity Parameter ◽  
Real Network ◽  
Self Similar

The self-similarity properties of the considered traffic were checked on different time scales obtained on the available daily traffic data. An estimate of the tail severity of the distribution self-similar traffic was obtained by constructing a regression line for the additional distribution function on a logarithmic scale. The self-similarity parameter value, determined by the severity of the distribution “tail”, made it possible to confirm the assumption of traffic self-similarity. A review of models simulating real network traffic with a self-similar structure was made. Implemented tools for generating artificial traffic in accordance with the considered models. Made comparison of artificial network traffic generators according to the least squares method criterion for approximating the artificial traffic point values by the approximation function of traffic. Qualitative assessments traffic generators in the form of the software implementation complexity were taken into account, which, however, can be a subjective assessment. Comparative characteristics allow you to choose some generators that most faithfully simulate real network traffic. The proposed sequence of methods to study the network traffic properties is necessary to understand its nature and to develop appropriate models that simulate real network traffic.

scholarly journals On network traffic statistical analysis

2020 ◽  
Vol 48 ◽  
Author(s):  
Liudas Kaklauskas ◽  
Leonidas Sakalauslas
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Statistical Method ◽  
Network Traffic ◽  
Network Node ◽  
Hurst Index ◽  
Self Similarity ◽  
Present Measurement ◽  
Traffic Characteristics ◽  
Measurement Results

The present article deals with statistical university network traffic, by applying the methods of self-similarity and chaos analysis. The object of measurement is Šiauliai University LitNet network node maintaining institutions of education of the northern Lithuania region. Time series of network traffic characteristics are formed by registering amount of information packets in a node at different regimes of network traffic and different values of discretion of registered information are present. Measurement results are processed by calculating Hurst index and estimating reliability of analysis results by applying the statistical method. Investigation of the network traffic allowed us drawing conclusions that time series bear features of self-similarity when aggregated time series bear features of slowly decreasing dependence.

scholarly journals Basic characteristics of networks with self-similar traffic simulation

2019 ◽  
Vol 20 (1-2) ◽  
pp. 137-141
Author(s):  
Marek Aleksander ◽  
Roman Odarchenko ◽  
Sergiy Gnatyuk ◽  
Tadeusz Kantor
Keyword(s):  
Stochastic Process ◽  
Real Time ◽  
Experimental Research ◽  
Network Traffic ◽  
Network Performance ◽  
The Self ◽  
Self Similarity ◽  
Complex Models ◽  
Self Similar ◽  
Basic Characteristics

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 performance, 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..

A hitchhiker’s guide to microscopy and microanalysis using telecommunications, e-mail, and the Internet

1994 ◽  
Vol 52 ◽  
pp. 390-391 ◽  
Author(s):  
Nestor J. Zaluzec
Keyword(s):  
The Internet ◽  
Information Superhighway ◽  
The World ◽  
Exchange Information ◽  
On Line ◽  
Computer Based ◽  
Software And Hardware ◽  
Communication Software ◽  
Basic Ideas ◽  
E Mail

The Information SuperHighway, Email, The Internet, FTP, BBS, Modems, : all buzz words which are becoming more and more routine in our daily life. Confusing terminology? Hopefully it won't be in a few minutes, all you need is to have a handle on a few basic concepts and terms and you will be on-line with the rest of the "telecommunication experts". These terms all refer to some type or aspect of tools associated with a range of computer-based communication software and hardware. They are in fact far less complex than the instruments we use on a day to day basis as microscopist's and microanalyst's. The key is for each of us to know what each is and how to make use of the wealth of information which they can make available to us for the asking. Basically all of these items relate to mechanisms and protocols by which we as scientists can easily exchange information rapidly and efficiently to colleagues in the office down the hall, or half-way around the world using computers and various communications media. The purpose of this tutorial/paper is to outline and demonstrate the basic ideas of some of the major information systems available to all of us today. For the sake of simplicity we will break this presentation down into two distinct (but as we shall see later connected) areas: telecommunications over conventional phone lines, and telecommunications by computer networks. Live tutorial/demonstrations of both procedures will be presented in the Computer Workshop/Software Exchange during the course of the meeting.

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