The conservation of information, towards an axiomatized modular modeling approach to congestion control
For network researchers and engineers, this work provides a more accurate and generic modeling framework for congestion control, though it is an incremental improvement over existing approaches.
The paper introduces a modular fluid-flow network congestion control model based on the conservation of information, capable of representing any network topology and capturing transient behavior more accurately than existing models, as validated by packet-level simulations.
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.