Multi Controller Placement Optimization in Software Defined Network Controllers by using Genetic Algorithm

Abstract

SDN stands for software-defined network, and it gives scalability to manage the network by giving the concept of centralization and open control. The key concept of software-defined networks is that it separates the control mechanism and data mechanism, so the controller of SDN is only responsible for controlling the network functions. The controller can be programmed or decoded as per the need of the network administrator, whether the network administrator wants to expand the network or downscale the existing network. Open flow is the protocol which is used to build communication between controllers and a switching node. Open flow protocol is responsible for synchronizing and receiving the flow table when the request from the switches increases to control the level and multiple routing information comes to the controller. The controller could not be able to manage such an overflow of information. As a result, the performance of the controller was affected. In this paper genetic algorithm technique is used to resolve the load balancing issue between the multiple controllers. The proposed solution of algorithm will perform the method of selection, crossover, and mutation techniques to optimize the load balancing of controllers. The proposed technique will be compared with some benchmark algorithms, and it will be proved that the proposed GA outperforms as compared to other existing algorithms. By optimizing network performance and resource utilization, the research contributes to building resilient infrastructure and promoting sustainable industrialization.