**7. Conclusion**

In this chapter, we present our research in the area of measurements, modeling and simulations of the self-similar network traffic. Firstly, the state of the art method for modeling and simulating of self-similar network traffic is presented. We also describe a number of facts about self-similarity, long range dependences and probability, which are used to describe such stochastic processes. Described as well are the mechanism and models to simulate network traffic in the OPNET Modeler simulation tool. The main goal of our research is to simulate measured network traffic, where we tend to minimize discrepancies between the measured and the simulated network traffic in the sense of packet-rate, bit-rate, bursts intensity, and variances. One of the big challenges in our research work was to find appropriate method to estimate parameters of data source network traffic processes that are based on measured network packet's traffic. The estimated parameters are needed during the modeling of the measured network traffic in the simulation tool. For those reasons, we have developed different methods, which allow estimation of the parameters of data source network traffic processes, based on the measured network packet's traffic.

At the end of the chapter, all phases needed for simulating the measured network traffic in the OPNET simulation tool are presented. During the analysis phase we pay attention to the self-similar property, which has become the basic model for describing today's network traffic. In the network traffic theory, the properties of short and long-range dependence are directly prescribed by the values of estimated parameter *H*. In our network traffic analysis, we prove that network traffic (test traffic 2) can exist where Hurst parameter is bigger than 0.5, but this process does not have the property of a long-range dependence.

For the purpose of parameters estimation of data source network traffic processes, we have used a method that mimics packet defragmentation. Through the use of this method we offer estimated parameters, used in simulations, where six traffics are simulated by different distributions for each of the measured test traffic. It can be seen from simulations that in the case of modeling self-similar traffic, short-range dependence is more appropriate for choosing exponential distribution to describe a packet-size process. The exponential distribution does not impact the extreme peaks in the modeled traffic. Pareto distribution is unsuitable for this purpose.

Heavy-tailed distributions, especially Pareto, are suitable for modeling a packet-size process of the measured network traffic, which are self-similar and also have the property of a longrange dependence (test traffic 1).

There are discrepancies between the measured and the modeled traffics in the sense of packet-rate, bit-rate, bursts intensity, and variances. With a method which mimics defragmentation, a good approximation of the measured network traffic is obtained. We cannot claim that this is the optimal method for all situations, because there are some limitations, although it shows good results through simulation in OPNET Modeler. We have noticed that estimating the shape-parameter of Pareto is very delicate, because a small deviation in the parameter causes large discrepancies regarding the network traffic's average values, which is one of the important criteria for traffic modeling.
