Topology Bench: Systematic Graph Based Benchmarking for Core Optical Networks

TL;DR

The framework enhances the generalisability of optical network research by providing a more objective and systematic approach to topology selection, and applies unsupervised machine learning to cluster real-world topologies into distinctive groups using nine optimal graph metrics using K-means.

Abstract

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterised by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource and represents a 61.5% increase in spatially-referenced real world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyse both real and synthetic networks using structural, spatial and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalisability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups using nine optimal graph metrics using K-means. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies.

Figures (11)

• Figure 1: Limitations in optical network topology datasets impede accurate modelling, benchmarking, and scalable solutions.
• Figure 2: Topology Bench includes tabular datasets for each topology, with 105 real-world topologies and 270,900 synthetic topologies. This is an example of the GEANT topology, presented in an easy-to-view format and ready-to-use for further analysis.
• Figure 3: Correlation heatmap of 21 graph metrics for 105 real world optical networks in Topology Bench.
• Figure 4: Distribution of (a) the diameter (hops), (b) network density, (c) maximum edge betweenness and number of edges (d) as a function of the number of nodes for small synthetic and real optical network datasets.
• Figure 5: Distance distribution of 16 North American networks compared to those of small synthetic optical networks.