Sdn controller allocation and assignment based on multicriterion chaotic salp swarm algorithm

Abstract

Increase in demand for multimedia and quality services requires 5G networks to resolve issues such as slicing, allocation, forwarding, and control using techniques such as software-defined networking (SDN) and network function virtualization. In this study, the optimum number of SDN multi-controllers are implemented based on a multi-criterion advanced genetic algorithm that takes into consideration three key parameters: Switch controller latency, hopcount, and link utilization. Preprocessing is the first step, in which delay, delay paths, hop-count, and hoppaths are computed as an information matrix (Infomat). Randomi-zation is the second step, and consists of initially placing controllers randomly, followed by an analytical hierarchical process evaluation that considers switch controller latency as the primary objective in the assignment process. In the third and last step, crossover and mutation genetic functions are implemented for a local search process until the best placement is found (when the primary objective threshold is reached). A novel chaos theory is applied to the salp swarm algorithm (SSA) for enhancing the optimizer’s performance in assigning the switch control-lers. The SSA is a dynamic optimal algorithm for switch controller connections. To enhance convergence rate and precision, chaotic maps extract random parameters based on Gaussian distribution for better control in reducing local optima. A logistic chaotic map is selected after analyzing its performance based on unim-odal and multimodal optimization. Evaluations are carried out on this proposed algorithm for 12 sets of topologies with two categories: Fewer and more switches. Simulation results show the highest efficiency in a computation time of 25 ms with varying thresholds and 12 ms with varying upper boundary utilization when compared with the particle swarm optimization (PSO) meta-heuristic algorithm. The effects of varying latency thresholds and upper boundary utilization on two set of topologies inferred more requests serviced with fewer controllers deployed.