As SDN migrates to wide area networks and 5G core networks, a scalable, highly reliable, low latency distributed control plane becomes a key factor that differentiates operator solutions for network control and management. In order to meet the high reliability and low latency requirements under time-varying volume of control traffic, the distributed control plane, consisting of multiple controllers and a combination of out-of-band and in-band control channels, needs to be managed dynamically. To this effect, we propose a novel programmable distributed control plane architecture with a dynamically managed in-band control network, where in-band mode switches communicate with their controllers over a virtual overlay to the data plane with dynamic topology. We dynamically manage the number of controllers, switches, and control flows assigned to each controller as well as traffic over control channels achieving both controller and control traffic load-balancing. We introduce “control flow table” (rules embedded in the flow table of a switch to manage in-band control flows) in order to implement the proposed distributed dynamic control plane. We propose methods for off-loading congested controllers and congested in-band control channels using control flow tables. A validation test-bed and experimental results over multiple topologies are presented to demonstrate the scalability and performance improvements achieved by the proposed dynamic control plane management procedures when the controller CPU and/or availability or throughput of in-band control channels becomes bottlenecks.

Virtual network operators may distrust each other and require that their virtual infrastructure is not cohosted on the same physical equipment. In this paper, we formulate a virtual network embedding problem that maximizes the total revenue where conflicting virtual network operators utilize distinct physical devices. Virtual links in our model have the option of selecting among a range of discrete bandwidth values, which have a corresponding price. This way, any revenue function can be realized. We evaluate the performance of our model by comparing it with a fixed bandwidth scheme and demonstrate its superior performance in terms of revenue.