智能决策用于节能雾节点的选择和IOT中的智能切换:一种机器学习方法
Rahat Ullah1, Muhammad Yahya2, Leonardo Mostarda3
1Institute of Optics and Electronics, Nanjing University of Information Science and Technology, Nanjing, China.
PeerJ. Computer science
|April 25, 2024
概括
本研究介绍了使用决策树预测服务请求的雾计算节点的动态重新配置方法. 这提高了雾节点的性能,大大降低了能源消耗,并改善了物联网 (IoT) 数据的服务命中率.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
- 机器学习 机器学习
背景情况:
- 物联网 (IoT) 产生了大量数据,由于安全性,带宽和延迟问题,这给云传输带来了挑战.
- 雾计算提供了本地数据处理,但面临着静态配置和雾节点处理/存储能力不足的局限性.
- 动态重新配置至关重要,以增强雾节点功能,以满足各种终端设备服务需求.
研究的目的:
- 调查雾服务的配置和动态重新配置,以响应终端设备的请求.
- 利用机器学习来预测服务请求,并实现主动的雾节点调整.
- 在吞吐量,能源消耗和服务可用性方面评估性能改进.
主要方法:
- 实施决策树 (DT) 机器学习模型来预测服务请求事件.
- 基于预测的服务需求,对雾节点进行动态重新配置.
- 通过模拟进行性能评估,重点关注击中率,错误率和能源消耗.
主要成果:
- 在大多数服务中,雾节点的成功率达到99%,显著降低了失败率.
- 与静态节点配置相比,显示能耗大幅降低,超过50%.
- 验证了动态切换和智能重新配置的有效性,以提高雾节点性能.
结论:
- 提出的基于决策树的动态重新配置模型有效地解决了物联网时代的雾计算局限性.
- 该方法优化了资源利用,从而节省了大量的能源,并改善了服务提供.
- 这种方法提高了雾计算的可扩展性和效率,用于处理动态物联网工作负载.
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