Leveraging Field Data for the Joint Optimization of Capacity and Availability in Low-Margin Optical Networks

The analysis of a public performance monitoring dataset reveals that reducing margins necessarily amounts to trade availability off for capacity. After carefully establishing this fact, we propose data-driven dynamic rate adaptation as a pragmatic solution to optimally reclaim margins in deployed networks. Based on the dataset, we show that the overall capacity of the monitored network could have been increased by 106% while maintaining the availability over 99.99% for 95% of the connections. Beyond such promising results, our work provides the fundamental tools to predict - for any network - how capacity upgrades would impact the availability, and to identify the most suitable rate adaptation mechanism to optimize margins.