Routing for Global Congestion Avoidance

TL;DR

This paper explores graph-theoretic methods for modeling congested subnetworks and developing routing strategies to avoid dense regions, aiming to reduce network congestion and blocking probability.

Contribution

It introduces new approaches for modeling congestion and identifying routes that bypass dense subgraphs, addressing algorithmic challenges in global congestion avoidance.

Findings

Effective modeling of congested subnetworks

Identification of routes avoiding dense subgraphs

Reduction in network blocking probability

Abstract

Modeling networks as different graph types and researching on route finding strategies, to avoid congestion in dense subnetworks via graph-theoretic approaches, contributes to overall blocking probability reduction in networks. Our main focus is to study methods for modeling congested subnetworks and graph density measures, in order to identify routes that avoid dense subgraphs for global congestion avoidance, along with covering related algorithmic issues.