ONE METHOD FOR COMPUTER NETWORKS TRAFFIC PREDICTION
Accurate prediction of the traffic volume of computer networks (CN) for both the long and short term plays a crucial role in monitoring, as well as in the effective management of the optimal use of available network resources. Typically, more experienced network administrators intuitively predict the traffic volume of the CN, however this is completely unacceptable for the administration of modern, large and complex CNs. Therefore, more accurate methods for predicting the traffic volume of the CN should be developed using machine learning methods that will help the CN administrators effectively plan and optimally manage the use of available network resources. This article proposes a method for short-term CN traffic volume prediction based on the CART (Classification and Regression Trees) model. The essence of the method is that, using decision trees, the previous sets of traffic volume states are classified according to the set of traffic volume state patterns and a linear regression model is constructed corresponding to each class. The method allows predicting the future CN traffic volume state by clustering vectors of current traffic volume states according to the most suitable previous patterns and using regression. Thus, the task of CN traffic volume short-term prediction is to determine the vectors of the current traffic volume states and a regression model for forecasting. To assess the accuracy of the method, the MASE (Mean Absolute Scaled Error) is used. The proposed method allows predicting the traffic volume of the CN for short periods of time, for example, for weeks, days, hours and seconds. The results of short-term forecasts can be used to improve QoS (Quality of Service), prevent overload of communication channels, optimal control of the available CN resources, etc (pp.124-133).
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