Alsadie Deafallah, Alsulami Musleh
Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, 21961, Saudi Arabia.
Department of Software Engineering, College of Computing, Umm Al-Qura University, Makkah, 21961, Saudi Arabia.
Sci Rep. 2024 Oct 21;14(1):24675. doi: 10.1038/s41598-024-75859-3.
Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various scheduling algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) and comparing it with established methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). We investigate key performance metrics, including makespan, resource utilization, and energy consumption, across both cloud and edge configurations. Scientific workflows often involve complex tasks with dependencies, which can challenge traditional scheduling algorithms. While existing methods show promise, they may not fully address the unique demands of hybrid cloud-edge environments, potentially leading to suboptimal outcomes. Our proposed ModFOA integrates cloud and edge computing resources, offering an effective solution for scheduling workflows in these hybrid environments. Through comparative analysis, ModFOA demonstrates improved performance in reducing makespan and completion times, while maintaining competitive resource utilization and energy efficiency. This study highlights the importance of incorporating cloud-edge integration in scheduling algorithms and showcases ModFOA's potential to enhance workflow efficiency and resource management across hybrid environments. Future research should focus on refining ModFOA's parameters and validating its effectiveness in practical hybrid cloud-edge scenarios.
在混合云边缘环境中高效调度科学工作流对于优化资源利用和最小化完成时间至关重要。在本研究中，我们评估了各种调度算法，重点介绍了改进的萤火虫优化算法（ModFOA），并将其与蚁群优化（ACO）、遗传算法（GA）和粒子群优化（PSO）等既定方法进行比较。我们研究了跨云和边缘配置的关键性能指标，包括完工时间、资源利用和能源消耗。科学工作流通常涉及具有依赖性的复杂任务，这可能对传统调度算法构成挑战。虽然现有方法显示出前景，但它们可能无法完全满足混合云边缘环境的独特需求，可能导致次优结果。我们提出的ModFOA整合了云和边缘计算资源，为在这些混合环境中调度工作流提供了一种有效的解决方案。通过比较分析，ModFOA在减少完工时间和完成时间方面表现出更好的性能，同时保持具有竞争力的资源利用和能源效率。本研究强调了在调度算法中纳入云边缘集成的重要性，并展示了ModFOA在提高混合环境中工作流效率和资源管理方面的潜力。未来的研究应专注于优化ModFOA的参数，并在实际的混合云边缘场景中验证其有效性。
