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Data-Based Statistical Models of Data Networks
Machine (Statistical) learning methods are used for predicting the delivery times of the packages transmitted through the data network (DN). The statistical model of the DN is proposed, this model allows predicting the delivery times depending on a state of the DN (network load) and the statistical dependences between the delivery times of different transmitted packages. For constructing this model, various statistical methods (forecasting, dimensionality reduction) are applied to the data which are the results of computational experiments performed with detailed simulation model of the DN. The constructed model simulates the processes of package transmission over the DN. Motivation for a construction of such model is a need to create Monte Carlo network simulators to imitate the delivery times of transmitted packages, such simulators can be used in modeling of Information and Control Systems whose objects communicate with each other through the DN.