在智能城市的多云节点之间复制文件段:机器学习方法
Nour Mostafa1, Yehia Kotb1, Zakwan Al-Arnaout1
1College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了一种新型模型,用于在多云和边缘计算环境中高效地复制数据,优化智能城市和物联网 (IoT) 的资源共享. 该模型最大限度地降低了成本,同时提高了数据可用性和访问速度.
科学领域:
- 云计算 云计算 云计算
- 边缘计算 边缘计算
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 智慧城市管理 智慧城市管理
- 大数据管理大数据管理
背景情况:
- 智慧城市和物联网管理提出了复杂的,多维的挑战,特别是在云计算和边缘计算中.
- 在这些分布式环境中,资源共享是提高整体系统性能的关键组成部分.
- 在分布式应用程序中管理多petabyte数据集和增加用户/资源数量需要先进的解决方案.
研究的目的:
- 解决异质多云系统中当前大数据管理方法的局限性.
- 提出一种新的数据复制模型,在基于物联网的多云环境中优化数据访问,可用性和成本.
- 在复杂的分布式系统中提高数据管理的可扩展性和可耗性.
主要方法:
- 开发了一个数据复制模型,将成本函数最小化,考虑存储,主机访问和通信成本.
- 采用基于历史数据的成本组件相对权重的学习机制,适应不同的云环境.
- 数学验证了拟议的模型的稳定性和有效性.
主要成果:
- 该模型确保数据复制提高了可用性,同时降低了总体数据存储和访问成本.
- 它有效地平衡服务器负载,并改善数据访问时间.
- 拟议的方法避免了与传统的完整数据复制技术相关的开销.
结论:
- 开发的模型为在多云和边缘计算中高效的数据管理提供了数学上健全和有效的解决方案.
- 它在物联网和智能城市环境中处理大型数据集的传统方法上提供了显著的改进.
- 这项研究有助于更具可扩展性,成本效益和高性能的分布式数据管理系统.
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