<p>The study of Internet traffic behavior in a real IP network is the subject of this thesis. Traffic Matrix of a telecommunication network represents the exchanged traffic volume between the source and destination nodes in the network and is a critical input for network studies. However, in most cases, traffic matrices are not readily available. Existing network management protocols such as the ‘Simple Network Management Protocol’ (SNMP) have been used to gather other observable measures, such as link load observations. The first part of this thesis reviews famous methods and approaches that try to infer and estimate the source-destination traffic matrix from the observable link loads.</p> <p>Another important subject in networks is to predict bandwidth requirements in the future. The second part of this thesis reviews some existing methods and approaches of traffic prediction. Recently a traffic prediction method which uses multiple Time-Series analysis, each operating on a different time-scale, has been proposed. This method uses multiple ‘AutoRegressive Integrated Moving Average’ (ARIMA) filters to predict the future bandwidth requirements. Each ARIMA filter operates on a different time scale, i.e., quarter-hour, hour, day, and week. The proposed method associates a weight with each ARIMA filter, and adjusts the weights according to which filter is currently the most accurate predictor. A review of this newly proposed method is presented. Extensive experimental results have been gathered to test the robustness of the method. The filter coefficients of each ARIMA filter have been varied, and the accuracy of the predicted traffic has been measured. Extensive experimental measurements indicate that the model is very robust, and that large changes to each filter's coefficients have only a small effect on the accuracy. In all cases we evaluated, the method is very robust, predicting short-term future traffic demands with typically ≈95% success rates.</p> / Master of Applied Science (MASc)