Efficient Resource Scheduling in Fog: A Multi-Objective Optimization Approach

Abstract

Fog computing is a novel idea that extends cloud computing by offering services like processing, storage, analysis, and networking on fog devices closer to IoT devices. Numerous fog devices are required to process the ever-growing amount of data generated by IoT applications. The heterogeneous tasks from various IoT applications compete for a limited number of resources of these devices. The process of assigning this set of tasks to different available fog nodes according to QoS requirements for processing is resource scheduling. Resource scheduling aims to optimize resource utilization and performance metrics however, the dynamic nature of the Fog environment, resource-constrained, and heterogeneity in fog devices make resource scheduling a complex issue. This research presents the design and implementation of a multi-objective optimization-based resource scheduling algorithm using Modified Particle Swarm Optimization (MPSO) that addresses the application module placement and task allocation issues. This two-step MPSO-based resource scheduling model finds the optimal fog node to place each application module and assigns appropriate tasks to the most optimal fog nodes for execution. The proposed model unlocks the full potential of fog resources along with maximization of overall system performance in terms of optimization of cost, latency, energy consumption, and network usage. The simulation results indicate that using MPSO energy consumption is reduced by 53.94% and 43.58% as compared to First Come First Serve (FCFS) and Particle Swarm Optimization (PSO), respectively. The loop delay, network usage and cost using MPSO are reduced by 40.3%, 67.69% and 90.01% respectively, as compared to PSO algorithm.