Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]

The key goal in optical network design is to introduce intelligence in the network and deliver capacity when and where it is needed. It is critical to understand the dependencies between network topology properties and the achievable network throughput. Real topology data of optical networks are scarce, and often large sets of synthetic graphs are used to evaluate their performance including proposed routing algorithms. These synthetic graphs are typically generated via the Erdos-Renyi (ER) and Barabasi-Albert (BA) models. Both models lead to distinct structural properties of the synthetic graphs, including degree and diameter distributions. In this paper, we show that these two commonly used approaches are not adequate for the modeling of real optical networks. The structural properties of optical core networks are strongly influenced by internodal distances. These, in turn, impact the signal-to-noise ratio, which is distance dependent. The analysis of optical network performance must, therefore, include spatial awareness to better reflect the graph properties of optical core network topologies. In this work, a new variant of the BA model, taking into account the internodal signal-to-noise ratio, is proposed. It is shown that this approach captures both the effects of graph structure and physical properties to generate better networks than traditional methods. The proposed model is compared to spatially agnostic approaches, in terms of the wavelength requirements and total information throughput, and highlights how intelligent choices can significantly increase network throughputs while saving fiber. (C) 2021 Optical Society of America

Automated summary

The key goal in optical network design is to introduce intelligence in the network and deliver capacity when and where it is needed. Traditional synthetic graph models like Erdos-Renyi and Barabasi-Albert may not accurately represent the real optical networks, and introducing signal-to-noise ratio in the modeling process can better reflect the performance of optical networks. This new approach can capture both the effects of graph structure and physical properties to generate better networks, leading to significantly increased network throughputs while saving fiber.