Optical multi-domain transport networks are often controlled by a hierarchical distributed architecture of controllers. Optimal placement of these controllers is very important for efficient management and control. Traditional SDN controller placement methods focus mostly on controller placement in datacenter networks. But the problem of virtualized controller placement for multi-domain transport networks needs to be solved in the context of geographically distributed heterogeneous multi-domain networks. In this context, edge datacenters have enabled network operators to place virtualized controller instances closer to users, besides providing more candidate locations for controller placement. In this study, we propose a dynamic controller placement method for optical transport networks that considers the heterogeneity of optical controllers, resource limitations at edge hosting locations, and latency requirements. We also propose a machine-learning framework that helps the controller placement algorithm with proactive prediction (instead of traditional reactive threshold-based approach). Simulation studies, considering practical scenarios and temporal variation of load, show significant cost savings compared to traditional placement approaches.

光学多域传输网络通常由控制器的分层分布式体系结构控制。这些控制器的最佳放置对于有效的管理和控制非常重要。传统的SDN控制器放置方法主要集中在数据中心网络中的控制器放置。但是，在地理上分布的异构多域网络的背景下，需要解决用于多域传输网络的虚拟控制器放置的问题。在这种情况下，边缘数据中心使网络运营商能够将虚拟化的控制器实例放置在离用户更近的地方，除了为控制器放置提供更多的候选位置。在这项研究中，我们提出了一种用于光传输网络的动态控制器放置方法，该方法考虑了光控制器的异构性，边缘托管位置的资源限制和延迟要求。我们还提出了一种机器学习框架，该框架可通过主动预测帮助控制器放置算法（而不是传统的基于反应性阈值的方法）。考虑到实际情况和负载随时间变化的仿真研究表明，与传统的放置方法相比，可节省大量成本。