A new GSO based method for SDN controller placement

Abstract

In the context of wide area networks (WANs) and software defined networking (SDN), reducing the communication delays experienced by the network devices is an important challenge whose solution requires a careful placement of controllers to decrease the end-to-end latencies. Although the majority of studies focusing on the controller placement problem (CPP) only consider the switch–controller propagation delays and inter-controller latencies, the controllers’ capacity needs to be addressed for lowering the end-to-end delays. While the controllers with a variety of processing rates, number of ports, and costs are available in the market, Internet Service Providers (ISPs) need to consider the affordability and capability of compensating their needs by maximizing the use of their networks’ resources. To propose a solution for this important problem, we consider the Knapsack 0–1problem and formulate the Garter Snake Optimization Capacitated Controller Placement Problem (GSOCCPP), a meta-heuristic algorithm, with new iterations and temperate mating conditions to solve the CPP. This algorithm uses a reasonable amount of computation time to obtain the minimum delays. A number of Topology-Zoo datasets are analyzed with a variety of small to large scale data plane nodes to evaluate the GSOCCPP algorithm based on different controllers’ capacities. The simulation results demonstrate that, in addition to outperforming similar meta-heuristic and clustering algorithms such as the Firefly Algorithm, Particle Swarm Optimization, and the k-means++, our newly proposed GSOCCPP algorithm is successful in achieving the lowest execution time among the analyzed algorithms. Furthermore, this proposed solution has a more efficient memory consumption compared to other algorithms for controller placement in different network topologies.