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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.

The article contains the digitalization results for expert systems design in the framework of geoinformation management within Arctic while coronavirus and change of climate. Currently, the methods of designing expert systems within the framework of geoinformation management in the Arctic demonstrate the use of improved concepts of data collection and visualization. The article gives preference to the use of web constructors in distributed networks. An example of a working layout of an expert system for geoinformation support of the activities of a subarctic port in the conditions of ice waters is given. The results of the research can be useful in operational environmental activity and for university learning purposes, including Master’s programs.

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