ILP model and effective genetic algorithm for routing and spectrum allocation in elastic optical networks

Abstract

This paper proposes an integer linear programming (ILP) formulation and an effective heuristic based on genetic algorithm (GA) for static routing and spectrum allocation (RSA) problem in spectrum-sliced elastic optical path network (SLICE� The RSA problem belongs to the class of problems called NP-Hard, and then approaches to achieve optimal solutions are limited to solve only small instances. For real networks optimal methods cannot give a solution in viable time, hence heuristic approaches are required. Then, we propose an ILP model and Genetic Algorithm (GA) to optimize the RSA problem in SLICE network. In spite of scalability problem an integer linear programming (ILP) formulation is presented, with the goal of attaining optima solutions for small networks andascertaining our heuristic's efficiency. In addition, it is presented a detailed analysis for G A's parameters.

Spectrum Utilization with Increasing Traffic, Based on ILP Model in Elastic Optical Networks

“…. Digital signal processing (DSP) with coherent detection in optical communication has opened a different ways to utilize Nyquist wavelength division multiplexing (WDM) to achieve better spectral efficiency [4]. This kind of technology with conventional 50 GHz ITU-T frequency grid can support 100 Gigabits per second dense wavelength division multiplexing communication system [3].…”

Effect of transmission impairments in CO-OFDM based Elastic Optical Network design

“…ILP FORMULATION In this section, we present our proposed ILP formulation. We contrast this ILP with respect to the ILP for delaybandwidth product optimization (termed as DBP ILP, henceforward) as given in [22]. In the proposed multi-objective ILP, to minimize fragmentation along with delay-bandwidth product and congestion, we propose a new modified optimization framework.…”

“…Performance analysis of the proposed ILP and the heuristics for 6-node network with impairments is summarized in Table IV in terms of simulation time (T) and optimality gap (G). The optimality gap refers to the percentage of average deviation of a heuristic algorithm from the ILP in terms of the value of the overall objective function (22). We have considered demands (D) as 4 and 6 and N = 20 slots per link.…”

“…On the other hand, in [22], the objective function is mainly formulated to focus on the minimization of the network delay-bandwidth product. As a result, the demands are scattered, creating serious fragmentation issues.…”

Towards solving practical problems of large solution space using a novel pattern searching hybrid evolutionary algorithm – An elastic optical network optimization case study

“…These papers address static network planning problems (Capucho & Resendo, 2013;Cerutti, Martinelli, Sambo, Cugini, & Castoldi, 2014;Eira, Santos, Pedro, & Pires, 2014;Klinkowski, 2013;Lezama, Castanon, Sarmiento, & Martins, 2014;Long et al, 2013;Patel, Ji, Jue, & Ting, 2012;Velasco, Wright, Lord, & Junyent, 2013;, and dynamic scenarios (Almeida et al, 2013;Huang & Li, 2014;Long et al, 2012; as well. Generally, the optimization criteria in these problems are spectrum usage and request blocking probability for static and dynamic cases, respectively.…”