Energy-Efficient Task Offloading in Fog Computing Systems Through Particle Swarm Optimization

Fog computing has come as a center stage solution for the shortcoming that were deemed characteristic of cloud computing since it seeks to provide computation nearer to the end devices. Nevertheless, task offloading in the fog computing system is a challenging problem, especially regarding energy consumption and system performance. The work presented herein is centred on designing a PSO based energyefficient task offloading approach. The proposed methodology takes advantage of PSO feature of mapping tasks with an adequate set of fog nodes in consideration to parameters such as energy consumption, processing time, and network bandwidth. The strategy which pursues to reduce energy consumption of mobile devices by minimizing a number of applications offloaded to the cloud is formulated as a multi-objective optimization task where energy minimization and maintaining high service quality are the objectives. The study also shows that PSO-based approach greatly leads to lesser energy consumption from conventional routing methods alongside the convenient workload and efficiency of the systems. The results show that PSO can be extremely beneficial in managing resources in the fog environment, so as to establish fog computing solutions that may be sustainable and scalable. This work also aims at enriching the development of fog computing systems as a solid approach to meeting the new massive computational requirements of IoT and other edge devices besides tackling energy efficiency issues.