Performance-aware Optimal Controller Placements Via Genetic Algorithms for Software Defined Networking

Abstract

Software-defined Network is a new paradigm of providing the efficient network management using the concept of control and data place separation. Multi-controllers designing is a promising way to achieve reliability and scalability. However, it brings the new problem of controller placement in a distributed architecture. For this, two recent approaches of controller placement (CP) are based on controller placement simulated annealing (CPSA) and controller placement particle swarm optimization (CPPSO). However, these approaches are still not effective in placement of controllers. Thus, there is performance degrading of the systems. To solve these problems, the controller placement based on a Genetic Algorithm (CPGA) has been proposed in this research. The proposed CPGA has used the fitness value of each node to locate the controllers at their optimal place. Also, the GA operations continues until it gets the optimal placement of controllers and after locating the controller at their appropriate place, it was used for a long time in the case of near optimal rather than the existing approaches. The performance comparison has been done based on parameters such as throughput and delay. It is observed with comparison of CPSA and CPPSO that the proposed CPGA outperforms on given parameters. The proposed CPGA shows efficiency in placing controllers at their optimal locations.