Extended array model of star capacity-aware delay-based next controller placement problem for multiple controller failures in software-defined wide area networks

The advent of the Software-Defined Networks (SDNs) has caused the control plane on the switches to be moved to a separate part of the data plane. Failure of a single controller deployed in the network disrupts the proper function of the network; therefore, we need to look for the multiple controller placement and find a way to plan the star ahead assignment of switches to the controllers. The two challenges to customizing the multiple controller placement problem in the form of star assignment are the same as the significant increase in the worst case of delay after reassigning switches to active controllers in the network and the network search space. Therefore, this search space can be significantly reduced by using standard array decision variables. In this paper, we present an optimal array model for the star capacity-aware delay-based next controller placement problem (SCDNCPP). The purpose of the proposed model is to minimize the maximum, for all switches, of the sum of the worst-case delay from the switch to the nearest first controller with enough capacity and the worst-case delay from the same switch to the nearest second controller with enough capacity. In addition, we formulate the problem with MIP (Mixed Integer Programming) model for multiple controller failure and solve it with CPLEX optimizer, but the execution time of the model is significantly longer. We also use the population-based simulated annealing algorithm to converge the problem rapidly toward the optimal solution and reduce time complexity. The simulation results are estimated with real Internet Zoo topologies. The delay improvement rate of the proposed approach according to the simulation results, in case of two controller failure, performs better than CNCP (Capacitated Next Controller Placement) and RCCPP (Resilient Capacity-aware Controller Placement Problem) approaches as much as 1.73 ms and 2.34 ms on Pameltto topology and 6 ms and 2.81 ms on Deltacom topology, respectively. The improvement rate improves significantly when the topology size grows. Additionally, the results show that the execution time of the heuristic algorithm to solve the problem is much better than the execution time of the mixed Integer programming formulation, on average, as much as 1.85 s on Pameltto topology and 1.98 s on Deltacom topology, respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.