Self-adjusting resilient control plane for virtual software-defined optical networks

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. • Adaptive hypervisor placement strategy proposed for resilient optical networks. • Integration of graph neural network model enhances placement efficiency. • Continuous adaptation with hypervisor migration ensures service continuity. • Simulations demonstrate balanced control load for dynamic traffic handling.