Graphical Probabilistic Routing Model for OBS Networks with Realistic Traffic Scenario

TL;DR

This paper introduces a Bayesian network-based routing model for OBS networks that proactively reduces burst contention and loss, outperforming static routing methods under realistic traffic conditions.

Contribution

The paper presents the Graphical Probabilistic Routing Model (GPRM), a novel proactive routing approach for OBS networks using Bayesian networks to select less utilized links.

Findings

GPRM reduces Burst Loss Ratio (BLR) compared to static routing.

Simulation on NSFnet topology shows improved performance.

Proactive routing outperforms reactive approaches in burst contention management.

Abstract

Burst contention is a well-known challenging problem in Optical Burst Switching (OBS) networks. Contention resolution approaches are always reactive and attempt to minimize the BLR based on local information available at the core node. On the other hand, a proactive approach that avoids burst losses before they occur is desirable. To reduce the probability of burst contention, a more robust routing algorithm than the shortest path is needed. This paper proposes a new routing mechanism for JET-based OBS networks, called Graphical Probabilistic Routing Model (GPRM) that selects less utilized links, on a hop-by-hop basis by using a bayesian network. We assume no wavelength conversion and no buffering to be available at the core nodes of the OBS network. We simulate the proposed approach under dynamic load to demonstrate that it reduces the Burst Loss Ratio (BLR) compared to static approaches by using Network Simulator 2 (ns-2) on NSFnet network topology and with realistic traffic matrix. Simulation results clearly show that the proposed approach outperforms static approaches in terms of BLR.