Load-Aware Nonlinearity Estimation for Elastic Optical Network Resource Optimization and Management

Abstract

Elastic optical networks (EONs) have emerged as a promising technology to accommodate the high-capacity and dynamic bandwidth demands of next-generation wireless networks. However, similar to the traditional wavelength division multiplexing optical networks, there exist significant challenges to manage nonlinearity effects in EONs. In this paper, we first analyze state-of-the-art nonlinearity estimation solutions and propose a novel load-aware nonlinearity estimation method. We further present a resource allocation algorithm using the proposed nonlinearity estimation scheme. In case the new embedded lightpath brings additional nonlinearity blocking the existing requests, we propose a mixed integer linear programming model and two heuristic algorithms using the proposed nonlinearity model as the service reconfiguration scheme for efficient resource allocation in EONs. The objective of the solution is to minimize the spectrum resource usage while satisfying the bandwidth demands of the connection and ensuring the quality of transmission. The proposed solutions are evaluated using an extensive simulation with off-line traffic requests and incrementally loaded requests against the benchmark solutions for two types of traffic profiles in a small network with six nodes and nine links and the National Science Foundation network. The results presented in this paper validate the benefits of the proposed nonlinearity estimation model and the corresponding algorithms to minimize the number of allocated frequency slots and service request blocking ratio while improving the overall network capacity.