**1. Introduction**

350 Telecommunications Networks – Current Status and Future Trends

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

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

Modeling and Simulating the Self-Similar Network Traffic in Simulation Tool 353

• and for ensuring quality of service (QoS) for real-time streaming multimedia

The simplest tools that measure and capture the packets of network traffic are packet sniffers. Packet sniffers, also known as protocol or network analyzers, are tools that monitor and capture network traffic with all content of network traffic. We can use sniffers to obtain the main information about network traffic, such as packet size, inter-arrival time and the type and structure of IP protocol. Sniffers have become very important and indispensable tools for network administrators. Figure 1 shows traffic captured by a packet sniffer.

Using this information, network administrators can make the network more efficient.

Fig. 1. User interface of WireShark sniffer during the network capturing.

applications.

During past years another aspect of network traffic studying has also appeared. In this case, the network traffic is researched from application or data source point of view, especially focused on statistics of file sizes and inter-arrival times between files [19]. These research works are very important for describing a relation between packet network traffic on lower ISO/OSI layers and data source network traffic on higher layers of ISO/OSI model. Based on the research of WWW network traffic, it has been shown that file sizes of such traffic are best described by Pareto distribution with shape parameter *α* = 1 [38]. That was also shown for the FTP traffic, where the shape parameter of Pareto distribution is in the range 0.9 < *α* < 1.1 [20]. In [6], [39], and [40] it is shown that inter-arrival time of TCP connections are selfsimilar processes, which can be described by Weibull heavily tailed distribution.

With expansion of simulation tools, which are used for simulation of communication networks, the knowledge about simulating the network traffic also becomes very important. One of the important tasks in simulations is also knowledge about modeling and simulating of network traffic. Network traffic is usually modeled in simulation tools from an application point of view [42], [45]. It is usually supposed that the file size statistics and file inter-arrival times are known [39], [40]. Such kinds of traffic models are supported by most commercial telecommunication simulation tools such as the OPNET Modeler [10], [11], [24], used in our simulations and experiments. Consequently, for using the measured data of packet traffic, when modeling file statistics, it is necessary to transform packets' statistics into files' statistics [9, 10]. This transformation contains opposite operations in relation to the fragmentation and encapsulation process. Extensive research and investigation about traffic sources in contemporary networks show that this approach requires an in-depth analysis of packet's traffic (which needs specialized, very powerful and consequently, expensive instruments). This approach, in the case of encrypted packets and non-standard application protocols, is not completely possible. In such cases, capture of entire packets is also necessary, which can be problematic in contemporary high-speed networks. Another approach estimates distribution parameters of file data sources from measured packets' network traffic. For such approach, we have developed and tested different methods [42], [45]. Estimated distribution parameters are used for modeling of the measured network traffic for simulation purposes. Through the use of these methods we want to minimize discrepancies between the measured and simulated traffic in regards to an average bit rate and bursts, which are characteristic of self-similar traffic.
