ABACUS: An Impairment Aware Joint Optimal Dynamic RMLSA in Elastic Optical Networks

Abstract

The challenge of optimal Routing and Spectrum Assignment (RSA) is significant in Elastic Optical Networks. Integrating adaptive modulation formats into the RSA problem - Routing, Modulation Level, and Spectrum Assignment - broadens allocation options and increases complexity. The conventional RSA approach entails predetermining fixed paths and then allocating spectrum within them separately. However, expanding the path set for optimality may not be advisable due to the substantial increase in paths with network size expansion. This paper delves into a novel approach called RMLSA, which proposes a comprehensive solution addressing both route determination and spectrum assignment simultaneously. An objective function named ABACUS, Adaptive Balance of Average Clustering and Utilization of Spectrum, is chosen for its capability to adjust and assign significance to average clustering and spectrum utilization. Our approach involves formulating an Integer Linear Programming model with a straightforward relationship between path and spectrum constraints. The model also integrates Physical Layer Impairments to ensure end-to-end Quality of Transmission for requested connections while maintaining existing ones. We demonstrate that ILP can offer an optimal solution for a dynamic traffic scenario within a reasonable time complexity. To achieve this goal, we adopt a structured formulation approach where essential information is determined beforehand, thus minimizing the need for online computations.