对异质雾计算系统的负载感知资源分配的设计
Syed Rizwan Hassan1, Ateeq Ur Rehman2, Naif Alsharabi3,4
1Department of Electrical Engineering, Institute of Engineering and Fertilizer Research, Faisalabad, Pakistan.
本研究引入了雾计算的启发式方法,以减少网络负载和延迟. 该方法有效地利用基于边缘节点数据的雾资源,优化对延迟感知应用程序的性能.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
- 网络工程 网络工程
背景情况:
- 云计算提供集中式服务,而雾计算则在终端设备附近提供分布式资源.
- 雾计算适用于大规模应用,与云架构相比,可以减少延迟和网络负载.
- 有效的资源分配和负载均衡对于有效的系统部署至关重要.
研究的目的:
- 为优化雾计算资源利用提出基于启发式的方法.
- 为了减少在雾计算环境中的网络消耗和应用程序延迟.
- 通过有效的资源配置,提高延迟意识应用程序的性能.
主要方法:
- 开发了一个用于雾资源分配的启发式算法.
- 该算法考虑了边缘节点集群生成的数据量.
- 资源分配是动态调整基于边缘数据负载.
主要成果:
- 建议的启发式方法有效地减少了网络消耗.
- 应用程序延迟的显著减少被观察到.
- 评估证实了该方法在各种尺度上的有效性,实现了最佳性能.
结论:
- 基于启发式的雾计算方法优化了资源利用.
- 这种方法通过最小化网络负载和延迟来提高延迟意识应用程序的效率.
- 这些发现支持分布式计算环境的拟议策略的可扩展性和有效性.
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