Bdamp and Bamp Optimization of Communication in SDN-Based Fog and Cloud Computing

Software-Defined Networking (SDN) has emerged as a revolutionary paradigm. The integration of SDN within fog networks represents a synergistic convergence of two cutting-edge technologies. With the complexity of SDN serving fog networks, the optimization of communication cost becomes paramount. Addressing the intricate challenges of communication cost optimization necessitates the application of sophisticated methodologies. Multi-Objective Optimization (MOO) algorithms present a robust solution, allowing for the simultaneous optimization of multiple conflicting objectives. By employing MOO, this research proposes a bi-objective optimization model for the intra- and inter-domain communication cost of controller deployment in an SDN-based computing network. The evaluation performed has captured two aspects of the performance of using Binary Angle quantization Multi-objective Particle swarm optimization (BAMP) and Binary crowding Distance Angle quantization Multi-objective Particle swarm optimization (BDAMP) for SDN controllers’ deployment. The first aspect is multi-objective-based evaluation, and the second aspect is the SDN network performance. Our developed BAMP and BDAMP have shown superiority over the benchmarks in terms of both aspects. Most importantly, the best performance is achieved by BDAMP in terms of both intra- and inter- communication cost.