Optimizing resource management using hybrid metaheuristic algorithm for fog layer design in edge computing

The growing complexity of management in fog computing environments necessitates more efficient algorithms capable of optimizing resource allocation, minimizing latency, and maximizing throughput and energy efficiency. Existing techniques, consisting of the Multi-Objective Crow Search Algorithm (MOCSA) and Fuzzy Meta-Heuristics Optimization (FMHO), regularly suffer from suboptimal performance due to constrained exploration abilities and slower convergence fees. To overcome with these demanding situations, this paper proposes a singular Hybrid Metaheuristic Algorithm (HMA) that mixes the strengths of more than one metaheuristic techniques, along with genetic algorithms, simulated annealing, and gray wolf optimization (GA-SA-GWO). The HMA is specifically designed to enhance useful resource control in fog computing by optimizing useful resource allocation, lowering latency, and enhancing usual gadget performance. Experimental results exhibit that the proposed HMA significantly outperforms existing solutions, with 26.98% improved latency, 90.64% resource utilization, 96.05% throughput, 37.06% reduced energy utilization, and 93.85% energy utilization. These outcomes spotlight the HMA's potential to successfully manage sources in dynamic and unpredictable fog computing environments, providing a greater scalable and robust solution for actual-time applications.