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The fog computing paradigm is promising for deploying various delay-sensitive Internet of Things (IoT) applications. The resource-constrained fog devices restrict the number of application deployments due to a lack of efficient resource estimation and discovery mechanisms for various emergent heterogeneous IoT applications. An efficient resource allocation strategy is one of the best choices to meet these application’s Quality of Service (QoS) requirements and improve system performance. However, finding the best allocation strategy for IoT applications with more than one QoS parameter is a challenge, and it has been proved as a non-deterministic polynomial time (NP)-complete problem. This article formulates a classical weighted multi-objective IoT service placement to optimize three parameters, i.e., makespan, cost, and energy. The non-convexity nature of the solution space motivates us to focus on the population-based meta-heuristic algorithm, i.e. Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO), along with their combination GA-SA, and GA-PSO. It implements the algorithm and compares it with the greedy-based random placement approach, varying the number of IoT applications with different parameters. The final results reveal that the hybrid method GA-SA outperforms other state-of-the-art algorithms.
Apat et al. (Tue,) studied this question.