Load-aware nonlinearity estimation for efficient resource allocation in elastic optical networks

Abstract

Elastic optical networking has emerged as a promising technology to accommodate high-capacity and dynamic bandwidth demands of next-generation wireless networks. However, the nonlinear impairments affect the network performance in terms of system reach distance, spectral efficiency and network utilization. The nonlinear impairments are currently assigned a fixed reference margin based on a worst case estimation which results in sub-optimal spectrum utilization. Therefore, in this paper, we propose a novel load-aware nonlinearity estimation model which is more accurate compared to the fixed reference margin and is shown to reduce request blocking ratio. We further present a routing, modulation and spectrum assignment (RMSA) solution using the proposed nonlinearity model. The integrated approach of our RMSA solution is evaluated for dynamic service requests with single and mixed line-rate traffic demands over the National Science Foundation Network (NSFNET) topology. The results presented in this paper validate the benefits of our novel nonlinearity estimation model and the proposed algorithm in terms of service blocking ratio and spectrum utilization.