  Scalability of the control plane in a software-defined network (SDN) is
enabled by means of decentralization of the decision-making logic, i.e., by
replication of controller functions to physically or virtually dislocated
controller replicas. Replication of a centralized controller state also enables
the protection against controller failures by means of primary and backup
replicas responsible for managing the underlying SDN data plane devices. In
this paper, we investigate the effect of the deployed consistency model on
scalability and correctness metrics of the SDN control plane. In particular, we
compare the strong and eventual consistency, and make a case for a novel
adaptive consistency approach. The existing controller platforms rely on either
strong or eventual consistency mechanisms in their state distribution. We show
how an adaptive consistency model offers the scalability benefits in terms of
the total request-handling throughput and response time, in contrast to the
strong consistency model. We also outline how the adaptive consistency approach
can provide for correctness semantics that are unachievable with the eventual
consistency paradigm in practice. The adaptability of our approach provides a
balanced and tunable tradeoff of scalability and correctness for the SDN
application implemented on top of the adaptive framework. To validate our
assumptions, we evaluate and compare the different approaches in an emulated
testbed with an example of a load balancer controller application. The
experimental setup comprises up to five extended OpenDaylight controller
instances and two network topologies from the area of service provider and data
center networks.
