Dynamic Multi-Workflow Scheduling: A Comparative Analysis of Real-time Data

Fog computing stands as a recent and significant technological advancement in the realm of computing technologies, addressing several limitations inherent in cloud computing and yielding diverse optimizations. As the demand for the payper-use model rises, dynamic workflow scheduling has emerged as a prominent trend for handling incoming workflow tasks. This paper shows a comparative analysis of three optimization algorithms - Genetic Algorithm(GA), Particle Swarm Optimization(PSO), and Grey Wolf Optimization(GWO) - with a focus on two crucial parameters: total cost and resource utilization. The study employs dynamic datasets to ensure the practicality and applicability of the results across various problem domains, ultimately contributing to their optimization. The findings of the comparative analysis are thoroughly examined and discussed in the results section.