Effective delivering capacity in traffic dynamics based on scale-free network

TL;DR

This paper analyzes how delivering capacities are utilized in traffic on scale-free networks, revealing idle nodes, a critical betweenness threshold, and ways to optimize capacity distribution for maximum efficiency.

Contribution

It introduces a theoretical and simulation-based analysis of capacity utilization and proposes capacity distribution strategies to maximize network efficiency.

Findings

Many nodes remain idle under various traffic states.

A critical betweenness value separates different queue growth behaviors.

Proper capacity distribution can nearly maximize capacity utilization.

Abstract

We investigate the percentage of delivering capacities that are actually consumed in a typical traffic dynamics where the capacities are uniformly assigned over a scale-free network. Theoretical analysis, as well as simulations, reveal that there are a large number of idle nodes under both free and weak congested state of the network. It is worth noting that there is a critical value of effective betweenness to classify nodes in the congested state, below which the node has a constant queue size but above which the queue size increases with time. We also show that the consumption ratio of delivering capacities can be boosted to nearly 100% by adopting a proper distribution of the capacities, which at the same time enhances the network efficiency to the maximum for the current routing strategy.