Clustering Methods for Hierarchical Traffic Grooming in Large-Scale Mesh WDM Networks

Abstract

We consider a hierarchical approach for traffic grooming in large multiwavelength networks of a general topology. Inspired by similar concepts in the airline industry, we decompose the network into clusters, and select a hub node in each cluster to groom traffic originating and terminating locally. At the second level of the hierarchy, the hub nodes form a virtual cluster for the purpose of grooming intra-cluster traffic. Clustering and hierarchical grooming enables us to cope with large network sizes and facilitates the control and management of traffic and network resources. Yet, determining the size and composition of clusters so as to yield good grooming solutions is a challenging task. We identify the grooming-specific factors affecting the selection of clusters, and we develop a parameterized clustering algorithm that can achieve a desired trade-off among various goals. We also obtain lower bounds on two important objectives in traffic grooming: the number of lightpaths and wavelengths needed to carry the subwavelength traffic. We demonstrate the effectiveness of clustering and hierarchical grooming by presenting the results of experiments on two network topologies that are substantially larger than those considered in previous traffic grooming studies.