Efficient Workflow Scheduling in Fog-Cloud Environments using Ant Colony Optimization

Fog computing has emerged as a solution to address challenges encountered in cloud computing at the network edge, and provides benefits such as low latency, increased availability, and reduced costs. Consequently, the growing adoption of fog and edge computing has led to workflow scheduling becoming a significant challenge in both cloud and hybrid fog-cloud computing environments. This paper aims to explore the necessity of optimizing resource utilization to prevent over- or under-utilization of computing resources. Through the implementation of an ant colony optimization (ACO) algorithm to achieve an efficient workflow scheduling approach. The main goal is to allocate compute tasks fog and cloud resources optimally, thereby minimizing the overall response time. Additionally, a task scheduling strategy employing the ACO algorithm is proposed to enhance task mobility among fog nodes and further minimize response time. We also compare the outcomes of traditional scheduling algorithms like FCFS, SJF, and RR in cloud and hybrid cloud-fog architectures. The simulation results of the proposed approach using well-known scientific workflows suggest that cloud-fog integration for workflow scheduling improves processing performance while reducing cost.