Enhancing Task Placement in iFogSim Using Metaheuristic Algorithms: An Innovative Approach

Task placement plays a vital role in enhancing the efficiency and performance of fog and edge computing systems. This paper proposes an advanced approach to task placement in iFogSim using the Particle Swarm Optimization (PSO) algorithm, a robust metaheuristic inspired by the social behavior of swarms. The PSO-based framework dynamically optimizes the allocation of tasks to heterogeneous resources, aiming to minimize execution latency and maximize resource utilization. By addressing the challenges of workload variability and resource constraints, the proposed method achieves superior performance compared to traditional scheduling techniques. Experimental evaluations validate the effectiveness of the PSO algorithm, highlighting its potential for improving resource management in IoT-enabled smart ecosystems.

کلیدواژه ها:

Fog Computing ، Internet of Things (IoT) ، Task Placement ، iFogSim ، Metaheuristic Algorithms ، Particle Swarm Optimization