An Energy Aware Task Scheduling Model Using Ant-Mating Optimization in Fog Computing Environment

Fog computing has become a platform of choice for executing emerging applications with low latency requirements. Since the devices in fog computing tend to be resource constraint and highly distributed, how fog computing resources can be effectively utilized for executing delay-sensitive tasks is a fundamental challenge. To address this problem, we propose and evaluate a new task scheduling algorithm with the aim of reducing the total system makespan and energy consumption for fog computing platform. The proposed approach consists of two key components: 1) a new bio-inspired optimization approach called Ant Mating Optimization (AMO) and 2) optimized distribution of a set of tasks among the fog nodes within proximity. The objective is to find an optimal trade-off between the system makespan and the consumed energy required by the fog computing services, established by end-user devices. Our empirical performance evaluation results demonstrate that the proposed approach outperforms the bee life algorithm, traditional particle swarm optimization and genetic algorithm in terms of makespan and consumed energy.