Towards a Robust and Scalable TCP Flavors Prediction Model from Passive Traffic

Abstract

Different end-to-end Transmission Control Protocol (TCP) algorithms widely in use behave differently under network congestion. The TCP congestion control itself has grown increasingly complex which in practice makes predicting TCP per-connection states from passive measurements a challenging task. In this paper, we present a robust, scalable and generic machine learning-based model which may be of interest for network operators that experimentally infers the underlying variant of loss-based TCP algorithms within a flow from passive traffic measurements collected at an intermediate node. We believe that our study has also a potential benefit and opportunity for researchers and scientists in the networking community from both academia and industry who want to assess the characteristics of TCP transmission states related to network congestion. We validate the robustness and scalability approach of our prediction model through several controlled experiments. It turns out, surprisingly enough, that the learned prediction model performs reasonably well by leveraging knowledge from the emulated network when it is applied on a real-life scenario setting bearing similarity to the concept of transfer learning in the machine learning community. The accuracy of our experimental results both in an emulated network, realistic and combined scenario settings and across multiple TCP variants demonstrate that our model is effective and has considerable potential.