Decision-Making in Fog Computing: A Comparative Analysis of Pareto-Based Optimization and Weighted Objective Models for Application Placement

The expanding adoption of Internet of Things (IoT) applications has amplified the challenge of identifying optimal solutions for resource allocation while balancing multiple competing objectives. As devices and applications proliferate, the complexity of resource management in fog computing escalates, necessitating advanced optimization techniques capable of adapting to evolving conditions and requirements. This paper presents a comparative analysis of two optimization strategies: Pareto-based multi-objective methods, exemplified by FP-NSGA-II (Fog Placement NSGA-II), a customized version of the Non-dominated Sorting Genetic Algorithm II tailored for task placement in fog environments, and weighted sum objective approaches, represented by Time Cost aware Scheduling (TCaS) and Weighted Sum-Genetic Algorithm (WS-GA). Through simulations and performance evaluations, this study compares these approaches in terms of trade-off management, scalability, and solution quality across varying system workloads. The experimental results highlight the strengths of the Pareto-based method, emphasizing its superior scalability and solution quality compared to weighted objective methods, along with its enhanced ability to balance multiple objectives while maintaining solution quality. The findings indicate that the Pareto-based approach is particularly effective in scenarios with competing objectives, as it provides a diverse set of solutions that can be tailored to specific application requirements.