An Efficient Proposed Methodology for Efficient Task Management in Fog Computing for Healthcare System Using Hybrid Meta-Heuristic Function

The paper introduces a new paradigm for fog computing system task management to minimize latency and enhance the overall system efficiency. The proposed methodology is for global and local task optimization and for resolving the issue of latency in communication networks employed in fog computing. The methodology utilizes the Whale Optimization Algorithm (WOA), a bio-inspired optimization algorithm based on the social behavior and hunting pattern of humpback whales. Two meta-heuristic task offloading algorithms, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), are also introduced inspired by this paradigm. The algorithms reduce the IoT application response time considerably and distribute tasks uniformly among fog nodes and outperform the conventional approaches such as Round Robin (RR) and throttled algorithms and other bio-inspired approaches such as Modified PSO (MPSO) and Bat Algorithm (BLA). Experimental results validate the significant reduction of latency for IoT-sensor applications when the proposed methodology is used compared to conventional algorithms. This confirms the effectiveness of the proposed method in task management optimization and enhancing the performance of fog computing systems.