Local Empathy provides Global Minimization of Congestion in Communication Networks

TL;DR

This paper introduces a local, empathy-based mechanism for routers in communication networks that self-organizes to delay congestion and achieve near-global optimization, outperforming traditional methods.

Contribution

The study demonstrates how local traffic awareness and empathy among nodes can significantly improve congestion management and transition smoothness in complex networks.

Findings

Local awareness delays congestion onset

Empathy leads to higher critical load capacity

Local rules can approximate global optimization

Abstract

We present a novel mechanism to avoid congestion in complex networks based on local knowledge of traffic conditions and the ability of routers to self-coordinate their dynamical behavior. In particular, routers make use of local information about traffic conditions to either reject or accept information packets from their neighbors. We show that when nodes are only aware of their own congestion state they self-organize into a hierarchical configuration that delays remarkably the onset of congestion although, leading to a sharp first-order like congestion transition. We also consider the case when nodes are aware of the congestion state of their neighbors. In this case, we show that empathy between nodes is strongly beneficial to the overall performance of the system and it is possible to achieve larger values for the critical load together with a smooth, second-order like, transition. Finally, we show how local empathy minimize the impact of congestion as much as global minimization. Therefore, here we present an outstanding example of how local dynamical rules can optimize the system's functioning up to the levels reached using global knowledge.