使用人工智能进行负载平衡,用于支持云计算的医疗保健领域万物互联网
Ibrahim Aqeel1, Ibrahim Mohsen Khormi1, Surbhi Bhatia Khan2,3
1College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种能源意识的人工智能 (AI) 负载平衡模型,使用混乱骑马优化算法 (CHROA) 为支持云的物联网 (IoT) 环境. 新的CHROA模型显著提高了吞吐量,并优化了能源资源,优于现有的方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 云计算 云计算 云计算
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 信息和通信技术 (ICT) 的快速增长导致了物联网 (IoT) 和万物互联网 (IoE).
- 实施物联网/IoE面临着能源和处理能力有限等挑战,特别是在数据密集型医疗保健应用中.
- 在支持云计算的物联网环境中,急需节能,智能负载平衡解决方案.
研究的目的:
- 为支持云计算的物联网环境提出一种基于人工智能 (AI) 的新能源意识负载平衡模型.
- 通过使用混乱骑马优化算法 (CHROA) 和大数据分析 (BDA) 增强负载平衡和能源优化.
- 根据现有的优化技术,评估拟议的CHROA模型的性能.
主要方法:
- 开发一个具有能源意识的AI负载平衡模型,结合混沌骑马优化算法 (CHROA).
- 利用大数据分析 (BDA) 在物联网环境中处理和分析大量数据.
- 增强骑马优化算法 (HROA) 使用CHROA技术中的混乱原则来改进优化.
主要成果:
- 与现有的算法相比,拟议的CHROA模型在负载平衡和能源优化方面表现出卓越的性能.
- 与人工蜂群 (ABC),引力搜索算法 (GSA) 和用火虫算法 (WD-FA) 进行的鱼防御算法 (58.24760.819 Kbps) 相比,CHROA的平均吞吐量显著更高 (70.122 Kbps).
- 实验评估证实了CHROA模型在优化能源资源和负载平衡方面的有效性.
结论:
- 基于CHROA的模型提供了一种创新的方法,用于智能负载平衡和能源优化,用于支持云端的物联网系统.
- 这些发现突出了CHROA模型在解决可持续物联网/IoE发展的关键挑战方面的潜力.
- 拟议的模型有助于创建更高效和节能的云支持物联网解决方案,特别是用于医疗保健.
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