Machine Learning in Networking: Network Adaptation and Performance Analysis

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For network operators both 5G and IoT offer huge potential as well as immense challenge. With expected numerous network-connected devices, and orders of magnitude increased available bandwidth to these devices, network operators must plan their network properly to handle the deluge of traffic generated by unforeseen and unpredicted applications of future. Facilitated by advanced processing power, sophisticated algorithms and abundance data, artificial intelligence and machine learning has come to forefront of research and real-life problem-solving applications. This research aims to explore the use machine learning techniques for traffic classification and prediction to assist in network planning and operation. The Sheridan team, in collaboration with Rogers Communication, will carry out the following key research activities. 1. Analyze raw 4G traffic data, develop necessary tools for formatting and mapping data into suitable format, and carry out required preprocessing to prepare data for feature extraction. 2. Identify the known and desired features, explore for new and potential features, create derived features, and develop a set of features, vetted by Rogers, for final model building. 3. Construct a pair of ML models- one for traffic classification and one for traffic prediction. Iteratively, train and tune the models with the test dataset and selected feature set. 4. Develop a set of test scenarios for each model and perform methodical testing of the models with the test dataset. Analyze performance of the models with algorithm-specific standard performance evaluation matrices and required operational performance thresholds of Rogers.