Analyzing Linear Communication Networks using the Ribosome Flow Model

TL;DR

This paper applies the Ribosome Flow Model to linear communication networks, providing analytical expressions for network performance metrics and optimizing parameters to minimize delay.

Contribution

It introduces the RFM as a novel analytical framework for modeling and analyzing linear communication networks, deriving closed-form solutions for key metrics.

Findings

Closed-form expressions for throughput and delay

Optimal hop length and transmission probability for minimal delay

Analytical insights into backpressure flow control

Abstract

The Ribosome Flow Model (RFM) describes the unidirectional movement of interacting particles along a one-dimensional chain of sites. As a site becomes fuller, the effective entry rate into this site decreases. The RFM has been used to model and analyze mRNA translation, a biological process in which ribosomes (the particles) move along the mRNA molecule (the chain), and decode the genetic information into proteins. Here we propose the RFM as an analytical framework for modeling and analyzing linear communication networks. In this context, the moving particles are data-packets, the chain of sites is a one dimensional set of ordered buffers, and the decreasing entry rate to a fuller buffer represents a kind of decentralized backpressure flow control. For an RFM with homogeneous link capacities, we provide closed-form expressions for important network metrics including the throughput and end-to-end delay. We use these results to analyze the hop length and the transmission probability (in a contention access mode) that minimize the end-to-end delay in a multihop linear network, and provide closed-form expressions for the optimal parameter values.