An efficient approach of controlling traffic congestion in scale-free networks

TL;DR

This paper introduces a model for traffic in scale-free and random networks, demonstrating that selectively enhancing a small fraction of high-degree nodes' processing capacity can effectively control congestion.

Contribution

It proposes a tunable network model with degree-dependent message rates and shows that targeted enhancement of a small node subset can prevent congestion efficiently.

Findings

Selective enhancement of ~3% of nodes prevents congestion.

High-degree nodes are critical in traffic flow and congestion.

Analytical and numerical analysis supports targeted capacity increase effectiveness.

Abstract

We propose and study a model of traffic in communication networks. The underlying network has a structure that is tunable between a scale-free growing network with preferential attachments and a random growing network. To model realistic situations where different nodes in a network may have different capabilities, the message or packet creation and delivering rates at a node are assumed to depend on the degree of the node. Noting that congestions are more likely to take place at the nodes with high degrees in networks with scale-free character, an efficient approach of selectively enhancing the message-processing capability of a small fraction (e.g. 3%) of the nodes is shown to perform just as good as enhancing the capability of all nodes. The interplay between the creation rate and the delivering rate in determining non-congested or congested traffic in a network is studied more numerically and analytically.