通过修改的Firefly算法提高工作流效率,用于混合云边缘环境
Deafallah Alsadie1, Musleh Alsulami2
1Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, 21961, Saudi Arabia. dbsadie@uqu.edu.sa.
Scientific reports
|October 21, 2024
概括
修改后的火优化算法 (ModFOA) 在混合云边缘环境中高效地安排科学工作流程,在减少完成时间和优化资源使用方面超过其他方法.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
- 人工智能的人工智能
背景情况:
- 科学工作流需要在混合云边缘环境中进行高效的调度,以实现最佳的资源利用和最小的完成时间.
- 传统的调度算法与科学工作流程的复杂性以及混合云边缘架构的独特需求作斗争.
- 现有的方法,如殖民地优化 (ACO),遗传算法 (GA) 和粒子优化 (PSO) 是有前途的,但可能无法完全解决这些挑战.
研究的目的:
- 在混合云边缘环境中评估和比较科学工作流程的各种调度算法的性能.
- 引入和评估修改后的火优化算法 (ModFOA),作为优化这些集成系统中的工作流程调度的解决方案.
- 分析关键绩效指标,包括制造板,资源利用和能源消耗.
主要方法:
- 调度算法的比较分析:修改的火虫优化算法 (ModFOA),殖民地优化 (ACO),遗传算法 (GA) 和粒子优化 (PSO).
- 绩效指标的评估:制造业,资源利用和能源消耗.
- 在混合环境中对云和边缘计算配置进行测试.
主要成果:
- 与ACO,GA和PSO相比,ModFOA在减少制作时间和整体完成时间方面表现出卓越的表现.
- 该算法保持了资源利用和能源效率的竞争水平.
- ModFOA有效地整合了云计算和边缘计算资源,以实现增强的工作流程调度.
结论:
- 修改后的火优化算法 (ModFOA) 为在混合云边缘环境中调度科学工作流提供了有效的解决方案.
- 通过有效地整合云和边缘资源,ModFOA提高了工作流效率和资源管理.
- 未来的研究应该专注于参数精细化和ModFOA在现实世界的混合云边缘场景中的实际验证.
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