A Deep Learning Analysis of Internet Traffic Datasets for 5G MEC Dimensioning

This thesis work consists in studying Deep Learning models in order to predict, as well as possible, the Internet traffic in a mobile network environment and use them in the 5G context for MEC (Multi-access Edge Computing) dimensioning. In particular, the first chapter gives a brief introduction about AI and Machine Learning. Basic concepts needed to understand the mechanism behind Deep Learning are provided through the second chapter. Third chapter supplies a description of used dataset. Analysis and results of DL models are presented in chapter four. Finally, the last chapter shows an application of the whole study for MEC dimensioning.