Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

The current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead of cloud computing, thereby achieving most of the required computations at the network edge of the IoT devices. Fog computing can thus improve the quality of service of delay-sensitive applications by allowing such applications to take advantage of the low latency provided by fog computing rather than the high latency of the cloud. Therefore, tasks in various IoT applications must be effectively distributed over the fog nodes to improve the quality of service, specifically the task response time. In this paper, two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm optimization (PSO), are used to propose two different scheduling algorithms to effectively load balance IoT tasks over the fog nodes under communication cost and response time considerations. The experimental results of the proposed algorithms are compared with those of the round robin (RR) algorithm. The evaluations show that the proposed ACO-based scheduler achieves an improvement in the response times of IoT applications compared to the proposed PSO-based and RR algorithms and effectively load balances the fog nodes.