在使用深度学习的虚拟机架构中的故障分类.
Ajay Rawat1, Anupama Mishra2, Hind Alsharif3
1UK Research Innovation, Swindon, UK.
Scientific reports
|October 2, 2025
概括
本研究介绍了一种用于预测集群网络中虚拟机故障的新型模型. 该模型准确地分类故障,提高网络可靠性和服务可用性.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 机器学习 机器学习
背景情况:
- 网络性能严重依赖于故障概率和服务可用性.
- 频繁的集群网络故障可能导致服务检测和识别失败.
- 积极的故障检测和分类对于防止系统故障至关重要.
研究的目的:
- 开发和评估一个模型,用于集群网络中准确的故障分类.
- 预测虚拟机中的潜在故障,以确保服务的连续性.
- 提高网络服务的整体可靠性和性能.
主要方法:
- 开发了一种新型模型,包括特征选择,注意力变压器和特征变压器.
- 该模型旨在使用神经网络处理表格数据.
- 在Telstra集群网络的表格数据集上进行了实验分析.
主要成果:
- 拟议的模型实现了高性能指标:98.3%的准确性,98.3%的精度,97.4%的回忆和97.8%的F1分数.
- 该模型有效地识别和分类了故障事件,包括服务中断事件和连接中断.
- 追踪驱动的实验验证实了模型在预测虚拟机故障方面的有效性.
结论:
- 开发的模型在预测网络故障方面取得了显著的成功.
- 这些发现验证了虚拟机中故障发生预测的研究目标.
- 拟议的方法提供了一个强大的解决方案,用于增强网络故障管理和服务可用性.
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