AI-based TCP Performance Modelling

This project aims to analyse the efficiency of artificial neural networks when modelling the performance of Transmission Control Protocol (TCP). First of all we need to understand what TCP performance depends on. We have therefore researched existing mathematical models. We have chosen to implement the Cardwell (2000) model. By using this model we can retrieve the data transfer time thanks to the timeout, loss rate, round trip time and initial congestion window. We have next built a network features dataset thanks to tcpdump and tcptrace. This set has enabled us to test our mathematical model and to build our artificial neural network. We have chosen to include the result of the mathematical model, the data packet number sent per connection and the average data packet size in the input of the artificial neural network in order to try to improve its efficiency. In order to analyse their performance we have chosen to use the correlation and the average relative error. By analysing our model we have shown the importance of the data. Indeed, we have to choose carefully their type and scope. We have shown also that our implementation of the mathematical model was inefficient. At the same time, we have reached a better accuracy with our artificial neural network model: 86.45 % of correlation and 37.4 % of average relative error