Hierarchical Traffic Grooming in Large-Scale WDM Networks

Abstract

The advances in fiber optics and wavelength division multiplexing (WDM) technology are viewed as the key to satisfying the data-driven bandwidth demand of today's Internet. The mismatch of bandwidths between user needs and wavelength capacity makes it clear that some multiplexing should be done to use the wavelength capacity efficiently, which will result in reduction on the cost of line terminating equipment (LTE). The technique is referred to as traffic grooming.

Previous studies have concentrated on different objectives, or on some special network topologies such as rings. In our study, we aim at minimizing the LTE cost to directly target on minimizing the network cost. We look into the grooming problem in elemental topologies as a starting point. First, we conduct proofs to show that traffic grooming in path, ring and star topology networks with the cost function we consider is NP-Complete. We also show the same complexity results for a Min-Max objective that has not been considered before, on the two elementary topologies. We then design polynomial-time heuristic algorithms for the grooming problem in rings (thus implicitly paths) and stars for networks of larger size. Experiments on various network sizes and traffic patterns show the effectiveness of our algorithms.

For general topology networks, we design a hierarchical approach which first partitions a large network into several clusters, then applies the method we use in star networks to each cluster, by selecting a hub node to groom traffic within the cluster. At the second hierarchy, we apply the star grooming method again only on the hub nodes. The polynomial-time hierarchical approach scales well and can cope with large networks of general topology efficiently, both for minimizing LTE cost and for lowering wavelength requirements.

We also design a clustering algorithm that can generate good results for subsequent steps in the hierarchical grooming method. Numerical results from experiments show that both the hierarchical grooming approach and our clustering algorithm generate satisfying results for the grooming on various network topologies and traffic demand patterns.