Stochastic RWA and Lightpath Rerouting in WDM Networks

TL;DR

This paper introduces a stochastic optimization approach for routing and wavelength assignment in WDM networks, improving capacity utilization and service quality through probabilistic models and rerouting strategies.

Contribution

It presents the first two-stage stochastic integer programming models for RWA and lightpath rerouting, incorporating request uncertainty to enhance network performance.

Findings

Up to 19% improvement in grade of service

Up to 20% spectrum savings

36% more requests served with minimal bandwidth increase

Abstract

In a telecommunication network, Routing and Wavelength Assignment (RWA) is the problem of finding lightpaths for incoming connection requests. When facing a dynamic traffic, greedy assignment of lightpaths to incoming requests based on predefined deterministic policies leads to a fragmented network that cannot make use of its full capacity due to stranded bandwidth. At this point service providers try to recover the capacity via a defragmentation process. We study this setting from two perspectives: (i) while granting the connection requests via the RWA problem and (ii) during the defragmentation process by lightpath rerouting. For both problems, we present the first two-stage stochastic integer programming model incorporating incoming request uncertainty to maximize the expected grade of service. We develop a decomposition-based solution approach, which uses various relaxations of the problem and a newly developed problem-specific cut family. Simulation of two-stage policies for a variety of instances in a rolling-horizon framework of 52 stages shows that our stochastic models provide high-quality solutions compared to traditionally used deterministic ones. Specifically, the proposed provisioning policies yield improvements of up to 19% in overall grade of service and 20% in spectrum saving, while the stochastic lightpath rerouting policies grant up to 36% more requests using up to just 4% more bandwidth spectrum.