Study and application of methods of fractal processes monitoring in computer networks

The field of the dissertation research is features of computer network packet traffic, the impact of network node features on traffic service, methods of real-time analysis of network traffic features and their application for dynamic prognostication of computer network packet traffic variance. The object of the research is the features of computer network packet traffic, the impact of network node features on computer network traffic service, methods of real-time network traffic features analysis and their application for dynamic prognostication of network traffic variances. The aim of work is to investigate fractal processes in computer networks, grounding on the results obtained to select methods suitable for real-time analysis of network traffic and to work out methods for real-time measurement of self-similarity as well as to apply it for perfection of computer networks service quality. Possibilities for mathematical modelling of network components, computer network packet traffic models and models using service theory instruments have been analysed. The package of network traffic features analysis has been worked out; it was used for analysis, assessment and comparison of methods for computer networks fractality and self-similarity research. For assessment of self-similarity of the network traffic time lines analysis, frequency/wave feature estimates, self-similarity analysis methods based on time line stability parameters estimators and assessed by the chaos theory means have been studied. The package of real-time network traffic self-similarity analysis has been worked out; by using it for real-time measurement of self-similarity the robust regression method and IR statistics were selected. Software for simulation of fractal processes was analysed. After applying a stochastically limited network communication model and algebra plus means, the GI/G/10//N computer network node is described. The simulation system of computer network self-similar and Poisson traffics was created and, by using it, the impact of network node features on quality of network traffic service was investigated. The worked methods were applied for perfection of computer network service quality. Methods for computer network traffic self-similarity real-time measurement have been worked out; they recurrently form a queue, calculate its statistical parameters and stored results in a temporal memory. These methods differ from others by real-time aggregation of network traffic by applying the method of smoothing moving means or the sum of transmitted data traffic during a time interval, the formed queue is stored in a temporal memory, recurrently calculates self-similarity parameters of a dynamically formed queue calculating α-stability index, IR estimator and Hurst coefficient, stores the calculations results in a temporal or/and permanent memory for further analysis. The method has been patented at the State Patent Bureau of the Republic of Lithuania, patent No. LT20011099. By using the method real-time analysis of computer network packet traffic self-similarity, an open network node control model including feedback has been worked out. Research results have been presented and discussed in two international conferences, one international seminar, two national conferences and three national seminars. Two articles have been published in foreign scholarly publications included into the Institute’s of Scientific Information list of main journals with the citation index, one published in international conference proceedings included into the Institute’s of Scientific Information list and four appeared in reviewed Lithuanian and foreign publications