Optimization of Service Function Chain Placement in Cloud-Fog-Edge Networks

Abstract

There is an explosion in IoT devices, 5G technology, and MECs, that results in increasing demands on effective and scalable network services management. Service function chaining, defined as the sequence of functions in VNFs on a path, is one of the core principles behind the NFV architecture design. SFC allocation to the heterogeneous clouds–fogs–edges network is an NP-hard problem characterized by mutually conflicting goals, such as latency minimization, energy and cost reduction, and resource maximization. In this study, an in-depth comparison study is carried out on three population-based optimization algorithms for solving the placement problem of SFCs using Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Grey Wolf Optimization (GWO) on three cases: (1) VNF deployment cost/QoE optimization in a 5G hybrid cloud with 12 nodes and weighting factor γ=0.4; (2) SFC graph matching on MEC-NFV networks with a 100-node physical network, 20 VNFs, and equal utilization weights α=β=γ=1/3; and (3) multi-instance SFC mapping on Fog-to-Cloud (F2C) IoT environment with a 5-VNF chain across 5 nodes. These three algorithms have been evaluated under identical conditions: 10 independent runs, 200 iterations, 20–30 agents. Results demonstrate that GWO achieves the best VNF deployment objective (W =73.92, a 15.1% improvement over the BGWO baseline), PSO achieves the highest resource utilization (52.9%) in MEC-NFV placement, and both PSO and GWO reduce F2C end-to-end latency by 25% compared to the ILP reference (12 vs. 16 units at three instances), while all three algorithms reduce latency by approximately 80% relative to cloud-only deployment. PSO emerges as the most consistently high-performing algorithm across all three scenarios.