A Causal Approach to the Study of TCP Performance

Abstract

Communication networks are complex systems whose operation relies on a large number of components that work together to provide services to end users. As the quality of these services depends on different parameters, understanding how each of them impacts the final performance of a service is a challenging but important problem. However, intervening on individual factors to evaluate the impact of the different parameters is often impractical due to the high cost of intervention in a network. It is, therefore, desirable to adopt a formal approach to understand the role of the different parameters and to predict how a change in any of these parameters will impact performance. The approach of causality pioneered by J. Pearl provides a powerful framework to investigate these questions. Most of the existing theory is non-parametric and does not make any assumption on the nature of the system under study. However, most of the implementations of causal model inference algorithms and most of the examples of usage of a causal model to predict intervention rely on assumptions such linearity, normality, or discrete data. In this article, we present a methodology to overcome the challenges of working with real-world data and extend the application of causality to complex systems in the area of telecommunication networks, for which assumptions of normality, linearity and discrete data do no hold. Specifically, we study the performance of TCP, which is the prevalent protocol for reliable end-to-end transfer in the Internet. Analytical models of the performance of TCP exist, but they take into account the state of network only and disregard the impact of the application at the sender and the receiver, which often influences TCP performance. To address this point, we take as application the file transfer protocol (FTP), which uses TCP for reliable transfer. Studying a well-understood protocol such as TCP allows us to validate our approach and compare its results to previous studies. We first present and evaluate our methodology using TCP traffic obtained via network emulation, which allows us to experimentally validate the prediction of an intervention. We then apply the methodology to real-world TCP traffic sent over the Internet. Throughout the article, we compare the causal approach for studying TCP performance to other approaches such as analytical modeling or simulation and and show how they can complement each other.