Combating Resource Crunch in an Optical Network: Demand-Responsive Dynamic OSNR Margin Allocation

Abstract

Several network operators run their networks at high average utilization. At high utilization, it is more likely that Resource Crunch will occur due to there not being enough capacity to serve all offered traffic. One solution is to increase the capacity of the underlying optical network by using higher modulation formats (which provide higher throughput) through transponders capable of dynamically adjusting modulations. This is possible since operators traditionally use large Optical Signal-to-Noise Ratio (OSNR) margins (i.e., the difference between the minimum OSNR for a certain modulation and the observed OSNR). Using modulation formats with higher spectral efficiency (i.e., increasing modulation) decreases OSNR margins. When OSNR margins are small, OSNR fluctuations may trigger the transponder to use more robust, lower modulations. If these changes are frequent, Quality of Service may suffer. To reduce the number of modulation changes, we propose a Machine Learning model to forecast OSNR. When Resource Crunch starts, we choose what modulations to use in each lightpath (according to the forecast); and, when it is over, we revert to large margins, in a demand-responsive manner. Our results show that, during Resource Crunch, our method carries a larger load when compared to a scenario where conservative OSNR margins are used, while incurring significantly fewer modulation changes than a system that always uses the tightest OSNR margin possible.