Comparative Study on Throughput Optimization in NFV: Traditional Dissemination Techniques vs. Swarm Intelligence Approaches

Network Functions Virtualization (NFV) modernizes networks by replacing hardware with software, creating a more flexible network architecture and offering flexibility in dynamic network environments. This foundational technology is essential for creating the networks of the future, including the Internet of Things (IoT) and cellular services. NFV does provide flexibility, but it struggles to maintain system throughput during high traffic loads while achieving high resource utilization efficiency and dynamic packet routing. The problem lies in the fact that traditional request distribution mechanisms, such as flooding and gossip, fail to operate efficiently in complex network topologies (scale-free networks), leading to: (a) random information propagation that consumes unnecessary bandwidth; (b) failure to identify paths and VNF placements in a way that minimizes congestion. Consequently, the research gap lies in the absence of an optimized swarm intelligence method that can find the optimal solution for routing requests and allocating resources simultaneously, with the aim of maximizing throughput and reducing performance degradation caused by inefficiency in determining the Time-To-Live (TTL) parameter. This paper proposes the application of enhanced flooding and gossip messages to investigate request propagation behavior and illustrate the issue of throughput and costs on resource consumption based on the effect of the TTL parameter on request distribution, system throughput, and resource consumption. The Grey Wolf Optimization (GWO) algorithm is proposed to handle this degradation. GWO can find optimal solutions to route and NFV placements with inspiration from the social hunting behavior of grey wolves. Results show that GWO significantly outperforms both flooding and gossip, achieving higher throughput by optimizing TTL settings.