Modeling of statistical data sources based on measured network traffic

Abstract

In the process of network traffic modeling, for simulation purposes, there is often a need for statistical description of traffic data sources. Usually, the network traffic is measured by capturing packets at a physical level. Normally, the estimation of statistical description of traffic data sources cannot be derived directly from such captured packets traffic. For that reason, we have researched for simpler solutions, which are based on the estimation of statistical processes of traffic data sources from the measured packet network traffic. We have developed the estimation methods, which allow the estimation of suitable probability distribution functions and their parameters of stochastic processes of traffic data sources. Statistical distributions of network traffic processes, such as data lengths process and data inter-arrival time, are important since they can be used for modeling of network traffic in simulation tools. For that reason, the estimation method is firstly developed, which mimics the defragmentation process. This method allows an estimation of distributions of data source network traffic processes and their parameters for captured packet traffic. During further testing, this method shows some limitations, especially for the process of data lengths. For that reason, we have developed a new estimation method with the approach described in this paper in further detail. In the new estimation method, which is called estimation method based on histogram comparison (EMHC), we use the opposite concept where distribution of data lengths is transformed by a developed analytical model to a packet size’s histogram. The latter is further compared to a packet size histogram of captured packet traffic. The optimization method is used to find such distribution parameters of the data length process that cause minimal discrepancies between the histogram of captured packets and the estimated packet size histogram. To estimate the discrepancy between two histograms, a well-known χ2 test is used, which is modified by a weighting function that considers, beside packet frequencies, the packet lengths as well. The proposed algorithm and method are confirmed through validations and experiments in a simulation tool.