Congestion in networks and manifolds, and fair-division problems

TL;DR

This paper explores congestion phenomena in negatively curved convex Riemannian manifolds, introducing a fair-division algorithm to estimate the size and impact of congestion cores, inspired by large-scale network behaviors.

Contribution

It presents a novel theoretical framework linking network congestion to Riemannian geometry and introduces a new fair-division method for analyzing congestion cores.

Findings

Congestion cores can be characterized geometrically in negatively curved manifolds.

The fair-division algorithm effectively estimates congestion core size.

The approach bridges network behavior and geometric analysis.

Abstract

Several large scale networks, such as the backbone of the Internet, have been observed to behave like convex Riemannian manifolds of negative curvature. In particular, this paradigm explains the observed existence, for networks of this type, of a "congestion core" through which a surprising large fraction of the traffic transits, while this phenomenon cannot be detected by purely local criteria. In this practical situation, it is important to estimate and predict the size and location of this congestion core. In this article we reverse the point of view and, motivated by the physical problem, we study congestion phenomena in the purely theoretical framework of convex Riemannian manifolds of negative curvature. In particular, we introduce a novel method of fair-division algorithm to estimate the size and impact of the congestion core in this context.