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.

中文翻译：

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利用现场数据联合优化低利润光网络的容量和可用性

对公共性能监控数据集的分析表明，降低利润必然等于用可用性换取容量。在仔细确定这一事实后，我们提出了数据驱动的动态速率自适应作为务实的解决方案，以优化回收已部署网络中的余量。根据数据集，我们表明受监控网络的整体容量可以增加 106%，同时保持 95% 连接的可用性超过 99.99%。除了这些有希望的结果之外，我们的工作提供了基本工具来预测 - 对于任何网络 - 容量升级将如何影响可用性，并确定最合适的速率适应机制以优化利润。