Enhancing Workflow Efficiency: A Modified Firefly Algorithm for Hybrid Cloud-Edge Environments

Abstract

Efficient scheduling of scientific workflows in cloud computing environments is essential for optimizing resource utilization and minimizing completion time. In this study, we comprehensively evaluate different scheduling algorithms, focusing on the Modified Firefly Optimization Algorithm (ModFOA) in comparison with existing methods like Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Our investigation considers key performance metrics such as makespan, resource utilization, and energy consumption across diverse configurations and scenarios. Scientific workflows often involve intricate tasks with dependencies, posing challenges for efficient scheduling. While existing algorithms have shown promise, they may not fully address the unique requirements of cloud environments, leading to suboptimal outcomes. Therefore, we propose evaluating ModFOA’s effectiveness in scheduling scientific workflows in the cloud. Through comparative analysis, ModFOA demonstrates superior performance in terms of makespan, achieving lower completion times across various configurations. Additionally, ModFOA exhibits competitive resource utilization and moderate energy consumption, positioning it as a promising solution for optimizing workflow scheduling in cloud environments. This study underscores the significance of selecting efficient scheduling algorithms and highlights ModFOA’s potential in improving workflow scheduling and resource management in cloud environments. Further research could focus on refining ModFOA parameters and validating its practicality in real-world cloud computing scenarios.