MULTIVARIATE COMBINATORIAL ELITISM GOLDEN EAGLE OPTIMIZATION FOR CONTROLLER PLACEMENT IN SDN ENVIRONMENT

Abstract

SDN is the model which integrates control plane over data plane. It monitors as well as controls network with the help of a controller. Numerous controllers were requirement of the SDN-based wireless network. Multiple controllers are termed by CPP. CPP focused on latency however unnoticed server under dynamic switches. In this paper, novel Multivariate Combinatorial Elitism Golden Eagle Controller Placement Optimization (MCEGECPO) technique is developed with better resource capacity of controllers. By applying a Combinatorial Elitism Golden Eagle Optimization, a controller placement is performed based on Multivariate functions.  Firstly, the populations of golden eagles (i.e. controllers) are initialized in the graph structures.  For each eagle, the fitness is estimated along with the Multivariate functions. The Elitism selection is applied to Golden Eagle Optimization to randomly select the controllers with the best fitness. Followed by, the global optimum solution is determined based on the position updates. As a result, the overall network performance is improved and obtains the delay. Experimental evaluation of MCEGECPO as well as existing techniques are conducted with various parameters such as packet delivery ratio, packet drop rate, throughput, average latency, and execution time. Experimental assessment shows MCEGECPO enhances packet delivery as well as throughput and minimizes latency, packet drop, and execution time compared with conventional methods.