混合多目标海洋捕食者基于算法聚类,用于轻量级资源调度和在雾中放置应用程序
1Department of Computer Science and Engineering, K. S. Rangasamy College of Technology, Tiruchengode, Namakkal, 637 215, Tamil Nadu, India. rbaskar@ksrct.ac.in.
Scientific reports
|May 7, 2025
概括
本研究介绍了一种新方法,可以有效地将物联网 (IoT) 应用程序分配到雾计算节点. 混合多目标海洋捕食者基于算法的聚类和雾采集器 (HMMPACFP) 技术优化了资源使用,并最大限度地减少了网络延迟,以提高服务质量 (QoS).
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:
- 物联网 (IoT) 需要用于需要低延迟和用户接近的应用程序的雾计算,以补充传统的云基础设施.
- 在雾环境中,对物联网应用程序的有效分配和调度对于现实的部署和最佳性能至关重要.
- 现有的调度方法面临多目标挑战,包括资源浪费,网络延迟,以及在雾节点上最大限度地提高服务质量 (QoS).
研究的目的:
- 开发一种用于雾节点分配和物联网应用程序动态调度的新型组合技术.
- 通过优化资源利用,最小化网络延迟和增强 QoS 来解决雾调度的多目标性质.
- 引入一种轻量化技术,以在雾环境中高效地部署物联网应用.
主要方法:
- 开发混合多目标海洋捕食者基于算法的聚类和雾采集器 (HMMPACFP) 技术.
- 使用雾选取器组件,根据预定义的 QoS 参数将物联网组件分配给雾节点.
- 使用iMetal和iFogSim进行模拟试验,使用超量 (HV) 和代际距离 (IGD) 度量评估性能.
主要成果:
- 拟议的HMMPACFP方案与基准方法相比,表现优越.
- 雾选器与HMMPACFP的集成实现了32.18%更快的融合和26.92%更大的解决方案多样性.
- 综合方法在优化过程中显示了勘探和开采率之间的更好的平衡.
结论:
- 在雾计算环境中,HMMPACFP技术为动态调度和资源分配提供了有效的解决方案.
- 开发的方法成功地解决了用于物联网应用的雾节点分配所固有的多目标挑战.
- 这些发现突出了HMMPACFP在提高物联网部署在雾计算中的效率和性能方面的潜力.
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