The design of optical networks for maximum throughput under diverse traffic demands is a long-standing NP-hard problem. In this paper, by parameterizing the relationship between the network topology and traffic demand, a polynomial-time objective function, the demand weighted cost (DWC), is introduced and evaluated for different scale networks and diverse traffic scenarios. It is shown that the proposed DWC is highly correlated to network throughput, while speeding up the topology evaluation process by some five to six orders of magnitude. The DWC was then applied as the optimization target with three different topology optimization algorithms (DWC-selection, genetic algorithm, and our hierarchical topology design), achieving 90% and 460% throughput increases, on average, for small-scale (14-node) and large-scale (100-node) topology designs, respectively. The proposed methods have the potential of maximizing throughput in the design of future optical network topologies.

Exploring the relationship among traffic, topology, and throughput: towards a traffic-optimal optical network topology design

One of the key performance metrics for optical networks is the maximum achievable throughput for a given network. Determining it, however, is a nondeterministic polynomial time (NP) hard optimization problem, often solved via computationally expensive integer linear programming (ILP) formulations. These are infeasible to implement as objectives, even on very small node scales of a few tens of nodes. Alternatively, heuristics are used although these, too, require considerable computation time for a large number of networks. There is, thus, a need for an ultra-fast and accurate performance evaluation of optical networks. For the first time, we propose the use of a geometric deep learning model, message passing neural networks (MPNNs), to learn the relationship between node and edge features, the network structure, and the maximum achievable network throughput. We demonstrate that MPNNs can accurately predict the maximum achievable throughput while reducing the computational time by up to five-orders of magnitude compared to the ILP for small networks (10–15 nodes) and compared to a heuristic for large networks (25–100 nodes)—proving their suitability for the design and optimization of optical networks on different time- and distance-scales.

/pdf/intelligent-performance-inference-a-graph-neural-network-24ev6phn.pdf

Intelligent performance inference: A graph neural network approach to modeling maximum achievable throughput in optical networks

We introduce a polynomial-time distributed message passing algorithm for routing and wavelength assignment. Exact global solutions are obtained for small-scale networks and improvements are demonstrated on network scales beyond the reach of established global algorithms. © 2022 The Author(s)

/pdf/message-passing-towards-low-complexity-global-optimal-rhkcwjsm.pdf

Message Passing: Towards Low-Complexity, Global Optimal Routing and Wavelength Assignment Solutions for Optical Networks

Traffic-Aware Optical Network Growth Techniques

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Message Passing: Towards Low-Complexity, Global Optimal Routing and Wavelength Assignment Solutions for Optical Networks

Traffic-Aware Optical Network Growth Techniques