Causal study of Network Performance

TL;DR

This paper introduces a causal graph-based method for analyzing TCP network performance, aiming to better understand and predict the effects of interventions in complex, heterogeneous internet systems.

Contribution

It proposes a novel approach using causal graphs to model and interpret TCP performance, enabling prediction of intervention outcomes in diverse network environments.

Findings

Causal graphs effectively model TCP performance dependencies.

The method predicts effects of network interventions.

Enhanced interpretability of network performance dynamics.

Abstract

The use of Internet in the every day life has pushed its evolution in a very fast way. The heterogeneity of the equipments supporting its networks, as well as the different devices from which it can be accessed, have participated in increasing the complexity of understanding its global behavior and performance. In our study we propose a new method for studying the performance of TCP protocol based on causal graphs. Causal graphs offer models easy to interpret and use. They highlight the structural model of the system they represent and give us access to the causal dependences between the different parameters of the system. One of the major contribution of causal graphs is their ability to predict the effects of an intervention from observations made before this intervention.