在雾云协作中使用混合IPSO-GWO算法进行高效的工作流程调度.
Samar Awad1, Marwa Gamal2, Khaled Abd El Salam2,3
1Electrical Engineering Department, Computer and Control Branch, Faculty of Engineering, Suez Canal University, Ismailia, 41522, Egypt. Samar_Awad@eng.suez.edu.eg.
Scientific reports
|January 30, 2026
概括
一个新的混合优化策略,改进的粒子群优化 (IPSO) 与灰狼优化 (GWO),增强了雾云计算中的任务卸载. 这种方法显著减少了工作流执行时间,能源消耗和总成本.
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:
- 雾云计算对于系统性能和成本效益至关重要.
- 在异质的雾云环境中,任务卸载和工作流程安排是复杂的.
研究的目的:
- 为雾云任务卸载引入一种新的混合优化策略.
- 在雾云环境中提高系统性能和成本效益.
主要方法:
- 开发了一种混合增强的粒子优化 (IPSO) 和灰狼优化 (GWO) 算法.
- IPSO-GWO算法具有动态适应的惯性权重,用于平衡的勘探和开发.
- 模拟使用FogWorkflowSim框架与现实世界的科学工作流程进行.
主要成果:
- 在模拟中,IPSO-GWO方法的表现优于标准PSO,GWO,IPSO和GSA.
- 观察到显著的平均降低:26.14%的制造量,37.73%的能源消耗,和12.52%的总成本.
- 差异分析 (ANOVA) 证实了结果的统计学意义.
结论:
- 拟议的IPSO-GWO算法为雾云系统中的任务卸载和工作流程安排提供了卓越的性能.
- 这项研究有助于理解分布式计算中的工作流优化动态.
- 这些发现为未来计算范式中更智能,更适应性的任务调度铺平了道路.
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