A machine learning method for green energy saving in space division multiplexing elastic optical networks

Abstract

Abstract Elastic optical networks (EONs) and space division multiplexing (SDM) are promising technologies for future core optical networks with high transmission capacity. However, energy consumption and crosstalk are two inevitable problems of practical application of space division multiplexing elastic optical networks (SDM-EONs). In this paper, we leverage machine learning techniques to decrease the energy consumption and crosstalk of SDM-EONs. Specifically, we use an online sequence extreme learning machine to forecast traffic load on the lightpath, and reconstruct an energy model containing energy-consumption components in the whole network so as to reduce energy consumption. We also present a suitable scheme to decrease crosstalk. Numerical results show that the proposed algorithm is effective in achieving substantial savings in energy consumption while maintaining blocking probability and crosstalk at levels comparable to those of earlier algorithms.