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

•We use the online sequence extreme learning machine (OS-ELM) to obtain an accurate forecast of future traffic based on historical data.•We develop an energy consumption model that includes the energy consumption of non-idle lightpaths, the loading energy consumption of idle lightpaths, and more.•The proposed MLES algorithm can address the dual challenges of energy consumption and crosstalk. 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.