AGWO: Cost Aware Task Scheduling in Cloud Fog Environment Using Hybrid Metaheuristic Algorithm

Abstract

In IoT concepts, efficient approaches like cloud-fog computing are emerging, enhancing system benefits. The performance and output of such frameworks can be greatly improved by optimized scheduling of Internet of Things (IoT) task requests. This study presents a novel technique for scheduling Internet of Things requests in a cloud-fog environment, based on an adaption of ant grey wolf optimisation (AGWO). By combining the operators of ant colony optimisation (ACO) and grey wolf optimisation (GWO), AGWO aims to improve the speed and quality of ACO's solution discovery. The suggested AGWO approach is evaluated using numerous datasets of varying sizes, both synthetic and real-world. The effectiveness of AGWO is further investigated by comparing it to standard metaheuristic methods. The experimental results demonstrate that AGWO is superior to competing strategies in terms of makespan time and cost while resolving the task scheduling problem.