The conservation of information, towards an axiomatized modular modeling approach to congestion control

TL;DR

This paper introduces a modular, conservation-of-information-based fluid-flow model for network congestion control, capable of representing any topology and capturing transient behaviors more accurately than existing models.

Contribution

It presents a novel, generic modular metamodel for network congestion, and classifies existing models, highlighting their limitations in transient behavior modeling.

Findings

Model accurately captures transient network behavior

Numerical simulations validate the model's precision

Provides a unified framework for diverse network topologies

Abstract

We derive a modular fluid-flow network congestion control model based on a law of fundamental nature in networks: the conservation of information. Network elements such as queues, users, and transmission channels and network performance indicators like sending/acknowledgement rates and delays are mathematically modelled by applying this law locally. Our contributions are twofold. First, we introduce a modular metamodel that is sufficiently generic to represent any network topology. The proposed model is composed of building blocks that implement mechanisms ignored by the existing ones, which can be recovered from exact reduction or approximation of this new model. Second, we provide a novel classification of previously proposed models in the literature and show that they are often not capable of capturing the transient behavior of the network precisely. Numerical results obtained from packet-level simulations demonstrate the accuracy of the proposed model.