An optimized design of optical networks using evolutionary algorithms

Abstract

Optical networks are key enablers of the modern communication services to handle the increasing bandwidth requests. Virtualization is a feasible technology to response to the users' demands. On the other hand, the cloud computing, as a distinguished use case of the virtualized optical networks, has impacted the IT world. In virtualization of the resources, optimum mapping of the virtual optical networks on the physical infrastructure plays an important role. In this paper, a novel optimized scheme for mapping the virtual optical networks on the physical infrastructure is proposed. A new formulation for Routing and Wavelength Assignment, RWA, problem is presented. A novel encoding method for optical networks is proposed based on categorizing the wavelengths into different groups according to data transmission rate. Then, the Genetic Algorithm, GA, and Binary Particle Swarm Optimization, BPSO, as two popular evolutionary algorithms are implemented to find the optimum map of the virtual optical networks on the physical infrastructure using the proposed encoding method. The optimization constraints and two heuristics, proposed to satisfy them, are detailed. Finally, the simulation results for a physical infrastructure and different virtual optical networks are presented. Results show that the GA outperforms the BPSO in terms of providing optimized solutions with less values of the defined cost function. But the run time required to find the optimum map of the virtual optical networks is more than the BPSO.