Optical networks must promptly respond to failures and efficiently handle dynamic traffic in order to fulfill their role as a critical infrastructure. Leveraging network softwarization and virtualization, virtual software-defined networks offer sufficient flexibility towards this goal by sharing the physical infrastructure among multiple tenants whose traffic must traverse the network hypervisor. In a resilient optical control plane each switch must be assigned to a primary and backup hypervisor instance through short control paths, which challenge will be addressed in this paper. First, we propose an intelligent greedy hypervisor placement heuristic which maximizes acceptance ratio for current, and preparedness for future requests. Secondly, we introduce a graph neural network model that can be seamlessly integrated with either our integer linear program or heuristic method to yield high-quality placements in significantly less time compared to our prior solutions. This enhancement renders our approach applicable to larger networks, significantly expanding its practical utility. Finally, we propose a self-adjusting hypervisor migration strategy, which continuously adapts the placement to the dynamically changing virtual network requests, thus, ensuring service continuity by avoiding frequent control plane reconfigurations. Through simulations we show that our hypervisor placement and migration strategies provide a balanced control load while they can handle a wide variety of changes.

中文翻译：

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用于虚拟软件定义光网络的自调整弹性控制平面


光纤网络必须及时响应故障并有效处理动态流量，以履行其作为关键基础设施的角色。利用网络软件化和虚拟化，虚拟软件定义网络通过在流量必须遍历网络管理程序的多个租户之间共享物理基础设施，为实现这一目标提供了足够的灵活性。在弹性光学控制平面中，必须通过较短的控制路径将每台交换机分配给主虚拟机管理程序实例和备份虚拟机管理程序实例，本文将解决这一挑战。首先，我们提出了一种智能贪婪虚拟机管理程序放置启发式方法，它可以最大限度地提高当前请求的接受率，并为未来的请求做好准备。其次，我们引入了一个图形神经网络模型，该模型可以与我们的整数线性程序或启发式方法无缝集成，与我们以前的解决方案相比，它可以在更短的时间内产生高质量的放置。这种增强使我们的方法适用于更大的网络，从而显着扩展了其实际用途。最后，我们提出了一种自我调整的虚拟机管理程序迁移策略，该策略不断调整放置以适应动态变化的虚拟网络请求，从而通过避免频繁的控制平面重新配置来确保服务的连续性。通过模拟，我们表明，我们的虚拟机管理程序放置和迁移策略提供了平衡的控制负载，同时它们可以处理各种变化。