一种精细的Greylag Goose优化方法,用于在边缘计算系统中有效地分配物联网服务
Hossein Najafi Khosrowshahi1, Hadi S Aghdasi2, Pedram Salehpour1
1Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
Scientific reports
|May 5, 2025
概括
修改的灰色优化 (MGGO) 算法增强了边缘计算服务的配置. 在动态的物联网环境中,MGGO提高了能源消耗,延迟,吞吐量和负载平衡.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 分布式计算 (Distributed Computing) 是一种分布式计算.
背景情况:
- 物联网 (IoT) 的普及需要在边缘计算中高效的服务配置.
- 动态的工作负载和异质资源对现有的服务配置策略构成重大挑战.
- 目前的群集智能算法,如QPSO-SP和WOA-FSP,在平衡勘探和开发方面遇到了困难.
研究的目的:
- 引入一种新的优化算法,即修改的灰色滞优化 (Modified Greylag Goose Optimization, MGGO),用于边缘服务配置.
- 解决现有算法在处理动态和异质边缘环境中的局限性.
- 改善关键性能指标,包括能源消耗,延迟,吞吐量和负载平衡.
主要方法:
- 修改的灰优化 (MGGO) 算法的开发.
- 整合适应机制:动态人口分割,停滞检测和基于学习的控制.
- 使用合成服务安置工作负载进行实验性评估.
主要成果:
- MGGO在GGO,QPSO-SP,BOA和WOA-FSP相比显示出12-15%的改善.
- 该算法在所有评估指标上显示了增强的性能:能源消耗,延迟,吞吐量和负载平衡.
- 在动态边缘计算场景中,MGGO有效优化了服务配置.
结论:
- 拟议的MGGO算法为边缘服务配置提供了显著的进步.
- MGGO的适应机制有助于其在动态环境中的卓越性能.
- 这项研究强调了MGGO在提高边缘计算系统的效率和有效性方面的潜力.
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