在基于SDN的物联网医疗保健解决方案的质量优化基础上,基于边缘计算的缓存和分类的组成
Seyedeh Shabnam Jazaeri1, Parvaneh Asghari2, Sam Jabbehdari1
1Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.
The Journal of supercomputing
|June 26, 2023
概括
本研究介绍了电子健康物联网设备的新型光谱聚类方法,通过高效的软件定义网络 (SDN) 边缘缓存来提高服务质量 (QoS). MFO-Edge缓存算法显著减少了数据检索延迟,并提高了缓存命中率.
科学领域:
- 物联网 (IoT) 在医疗保健中的应用
- 网络优化 网络优化
- 数据缓存策略数据缓存策略
背景情况:
- 电子健康系统产生大量的数据,需要有效管理.
- 现有的网络架构与实时电子健康应用程序的延迟和带宽需求作斗争.
- 物联网设备在医疗保健中的集成需要先进的缓存和网络管理解决方案.
研究的目的:
- 提出一种新的光谱聚类方法来分组电子健康物联网设备.
- 在软件定义网络 (SDN) 边缘实施有效的缓存机制.
- 通过优化资源配置,提高电子健康应用的服务质量 (QoS).
主要方法:
- 使用光谱聚类来根据设备数据相似性和网络接近性对患者进行分组.
- 将已识别的集群连接到SDN边缘节点以进行本地化数据缓存.
- 使用MFO-Edge缓存算法对数据进行智能选择进行缓存.
- 优先考虑紧急和按需请求以获得更快的缓存响应.
主要成果:
- 与现有方法相比,拟议的方法显示出更高的性能.
- 实现了平均数据检索延迟的显著减少.
- 在优先级请求中报告了76%的缓存命中率.
- 观察到定期请求的缓存命中率为35%,表明有效的资源配置.
结论:
- 集成SDN边缘缓存和光谱聚类有效优化电子健康网络资源.
- MFO-Edge缓存算法通过减少延迟和改善数据可访问性来增强 QoS.
- 这种方法为电子健康物联网环境中的数据管理提供了可扩展和高效的解决方案.
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