**5. References**


**15** 

*Slovenia* 

**Modeling and Simulating the Self-Similar** 

Telecommunication networks are growing very fast. The user's needs, in regards to new services and applications that have a higher bandwidth requirement, are becoming bigger every day. A telecommunication network requires early design, planning, maintenance, continuous development and updating, as demand increases. In that respect we are forced to incessantly evaluate the telecommunication network's efficiency by utilizing methods

Measuring, analyses and the modeling of self-similar traffic has still been one of the main research challenges. Several studies have been carried-out over the last fifteen years on: analysis of network traffic on the Internet [30], [31], traffic measurements in the high speed networks [32], and also measurement in the next generation networks [33]. Also, a lot of research works exist, where attention had been given to analysis of the network traffic caused by different applications, such as P2P [34], [35], network games [36] and VoIP application Skype [37]. Analyses of the measured network traffic help us to understand the basic behavior of network traffic. Various have showed that traffic in contemporary communication networks is well described with a self-similar statistical traffic model, which is based on fractal theory [6]. The pioneers in this field are: Leland, Willinger, and many others [1], [5], [6]. They introduced the new network traffic description in 1994. New description appeared as an alternative to traditional models, as were Poisson and Markov, which were used as a good approximation for telephone networks (PSNT networks) when describing the process of call durations and time between calls [5], [20]. These models do not allow descriptions of bursts, which are distinctive in today's network traffic. Such bursts can be described by a self-similarity model [5], [6], because it shows bursts over a wide-range of time scales. This contrasts with the traditional traffic model (Poisson model), which became very smooth during the aggregation process. The measure of bursts and also self-similarity present the Hurst parameter [1]-[4], which is correlated with another very important property called long-range dependence [5]-[8]. This property is also manifested with heavytailed probability of density distributions [5], [6], such as Pareto [43] or Weibull [44]. So Pareto's and Weibull's heavy-tailed distributions became the most frequently used

such as measurement, analysis modeling and simulations of these networks.

distributions to describe self-similar network traffic in communication networks.

**1. Introduction** 

**Network Traffic in Simulation Tool** 

Matjaž Fras1, Jože Mohorko2 and Žarko Čučej<sup>2</sup>

*2University of Maribor, Faculty of Electrical Engineering* 

*1Margento R&D, Maribor,* 

*and Computer Science, Maribor,* 

*European Workshop on Mobile and Personal Satellite Communications (EMPS 2000),* pp. 72-81, London, UK; September 18, 2000.

