Task scheduling in cloud-fog computing using discrete binary particle swarm meta-heuristic with modified sigmoid function

Cloud-Fog IoT networking is a resourceful technology to aid the processing of IoT end device requests. These devices generate tasks that need optimized computing and reduced latency for applications that operate in real-time environments. This article sets forth a cloud-fog task scheduler that schedules diverse tasks on vertically scaled Cloud and Fog virtual machines. For the proposed Binary Particle Swarm Optimizer (BPSO) based scheduler, an apt choice identified is to employ a modified sigmoid function with the logarithm decreasing inertia weight policy to deliver an optimal scheduling scheme. Moreover, the parameters of the BPSO are tuned inferring the best practices prescribed in literature. The results show that proposed method caters better than existing heuristic techniques to improve makespan and load imbalance.