Minimum Cost SDN Routing With Reconfiguration Frequency Constraints

Software-defined network (SDN) controllers include mechanisms to globally reconfigure the network in order to respond to a changing environment. As demands arrive or leave the system, the globally optimum flow configuration changes over time. Although the optimum configuration can be computed with standard iterative methods, convergence may be slower than system variations, and hence it may be preferable to interrupt the solver and restart. In this paper, we focus on the class of iterative solvers with an exponential decrease over time in the optimality gap. Assuming dynamic arrivals and departures of demands, the computed optimality gap at each iteration $Q(t)$ is described by an auto-regressive stochastic process. At each time slot, the controller may choose to: 1) stop the iterative solver and apply the best found configuration to the network or 2) allow the solver to continue the iterations keeping the network in its suboptimal form. Choice 1) reduces the optimality gap leading to smaller routing costs but requires flow reconfiguration which hurts QoS and system stability. To limit the negative impact of reconfigurations, we propose two control policies that minimize the time-average routing cost while respecting a network reconfiguration budget. We experiment with realistic network settings using standard linear programming tools from SDN industry. In the experiments conducted over the GEANT networks and fat tree networks, our policies provide a practical means of keeping the routing cost small within a given reconfiguration constraint.