The rapid proliferation of Internet of Things (IoT) devices and the increasing computational demands of modern applications in domains such as autonomous vehicles, smart healthcare, industrial automation, and smart cities have revealed fundamental limitations of traditional cloud-centric infrastructures. Although centralized cloud platforms provide substantial processing capacity, the physical distance between cloud data centers and data sources results in high latency, excessive bandwidth consumption, network congestion, and elevated operational costs. Fog computing has emerged as an effective complementary paradigm that extends computational and storage capabilities toward the network edge, enabling localized and real-time processing that significantly improves latency, resource efficiency, and overall Quality of Service (QoS). However, the optimal placement of Virtual Network Functions (VNFs) within Service Function Chains (SFCs) in fog–cloud environments remains a challenging NP-hard optimization problem due to heterogeneous resources, latency constraints, and complex system dynamics. In this thesis, a SUM-OF-SINGLE objective optimization framework based on an enhanced Ant Colony Optimization (ACO) algorithm is proposed to solve the VNF placement problem by aggregating end-to-end delay, deployment cost, and resource utilization into a unified objective function. The proposed ACO method employs a pheromone-guided adaptive search mechanism that enables efficient exploration of the large solution space while ensuring scalability and robustness under dynamic fog–cloud workloads. To comprehensively evaluate the effectiveness of the proposed framework, its performance was compared against five well-established benchmark algorithms, including Integer Linear Programming (ILP) as an exact optimal method, Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Lightweight Greedy Placement Algorithm (LGPA), a Random placement baseline, and a Multi-Objective Genetic Algorithm (MOGA). Extensive simulation results across multiple network configurations demonstrate that the proposed SUM-OF-SINGLE ACO (Multi-objective) algorithm consistently outperforms all benchmark approaches by achieving lower end-to-end latency, reduced deployment costs, and superior resource utilization efficiency. Furthermore, the algorithm exhibits higher stability, faster convergence, and stronger scalability when facing heterogeneous infrastructure conditions and fluctuating service workloads. Overall, the findings confirm that the proposed ACO-based framework provides an effective, scalable, and cost-efficient solution for VNF placement in next-generation fog–cloud computing systems and contributes meaningful insights for both academic research and real-world deployment of intelligent edge-enabled services.
