智能城市和智能建筑的AI支持的动态边缘云资源配置
Marian-Cosmin Dumitru1, Simona-Iuliana Caramihai1, Alexandru Dumitrascu1
1Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, RO-060042 Bucharest, Romania.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了一个季节性自动回归集成移动平均 (SARIMA) 模型,用于物联网中的无云端切换. 它确保了连续运行和提高了资源预测准确性,优于传统方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 智能城市物联网 (IoT) 设备的扩散产生了大量的数据,由于带宽限制,这给基于云的分析带来了挑战.
- 边缘计算提供了一个解决方案,通过处理更接近源的数据,优化资源配置和提高云计算性能.
- 对于边缘和云端节点来说,动态资源管理至关重要,需要根据实时负载条件对任务进行优先排序.
研究的目的:
- 提出云边缘环境中动态资源管理的预测模型.
- 在云端连接中断期间确保连续运行并保持命令循环完整性.
- 提高资源利用预测的准确性,以便在智能城市应用中有效地分配资源.
主要方法:
- 开发一个季节性自动回归集成移动平均线 (SARIMA) 模型来预测资源需求.
- 在连接丢失时,在云端和边缘节点之间实施无切换机制.
- 通过使用现实世界资源利用数据,对简单移动平均线 (SMA) 基线进行SARIMA模型的评估.
主要成果:
- 与SMA基线相比,SARIMA模型在预测准确度方面显著改善.
- 在CPU使用预测和35%的RAM使用预测中实现了高达64%的改进.
- 验证了将季节性和自回归元件纳入边缘计算中的预测建模的有效性.
结论:
- 提出的基于SARIMA的方法有效地管理动态云边缘环境中的资源.
- 确保不间断的服务和预测完整性,这对于智能城市应用至关重要.
- 突出了先进的时间序列模型在优化边缘计算性能和资源配置方面的潜力.
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