Machine learning techniques to improve router configurations in large optical networks

我们建议使用机器学习技术来自动调整大型光网络中的路由器配置；自动调优应该能够更好地响应拥塞问题，并减少容易出错的操作。通过我们与Ciena的拟议合作，我们将能够访问捕获内部网关协议（IGP）指标的日志数据，例如大型网络链路上的入站和出站流量，以及人工操作员执行的配置更改。使用监督学习，我们建议学习在哪个上下文中执行配置更改。接下来，我们将创建一个模拟，它能够大致生成与日志相同的数据，我们将在其中应用我们在前一阶段学习的模型。这将给我们一个基准性能评估。为了改进我们的初始模型，我们建议在上一步获得的模拟器中使用强化学习。最后，我们建议在一个小型实验室中使用实际路由器测试得到的强化学习策略；这将帮助我们评估哪些更改是必要的，以及在“上线”时需要承担哪些风险。

We propose to use machine learning techniques to automate the tuning of router configurations in large optical networks; automated tuning should lead to better responsiveness to congestion issues and less error-prone operation. Through our proposed collaboration with Ciena we will have access to log data that captures Interior Gateway Protocol (IGP) metrics such as inbound and outbound traffic on links in a large network, as well as configuration changes performed by human operators. Using supervised learning, we propose to learn in which context the configuration changes are performed. Next, we will create a simulation that is capable of roughly generating the same data as the logs we have, where we will apply the model that we learned in the previous stage. This will give us a baseline performance evaluation. To improve on our initial model, we propose to use reinforcement learning in the simulator obtained in the previous step. Finally, we propose to test the resulting reinforcement learning policy in a small lab with actual routers; this should helps us evaluate what changes may be necessary and which risks to expect when going "live".