基于事实的决策树分析,以提高云计算的可靠性
Muhammad Asim Shahid1,2, Muhammad Mansoor Alam1,3,4,5, Mazliham Mohd Su'ud5
1Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia.
PloS one
|December 3, 2024
概括
这项研究比较了用于云计算故障预测的五种机器学习算法. 修改后的决策树 (J48) 算法实现了最高的准确性 (97.07%) 与最小的故障预测错误.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 云计算 云计算 云计算 云计算
背景情况:
- 由于成本效益和可扩展性,云计算 (CC) 的采用正在迅速增加.
- 企业正在迁移到云端,以提高可访问性和透明度.
- 准确的故障预测对于可靠的云基础设施至关重要.
研究的目的:
- 为了比较五种机器学习算法的准确性和故障预测能力.
- 确定用于云计算故障预测的最有效算法.
- 建议修改以提高所选择的算法的性能.
主要方法:
- 评估了AdaBoostM1,包装,决策树 (J48),深度学习 (Dl4jMLP) 和天真贝叶斯树 (NB树) 算法.
- 使用二次数据分析进行初始比较.
- 通过80/20,70/30和10倍的交叉验证分割进行了初级数据分析.
- 测量准确率和每个算法的故障预测错误.
主要成果:
- 纯粹的贝叶斯树 (NB树) 显示出高精度 (97.05%) 但时间复杂性差 (1.01s).
- 决策树 (J48) 提供了类似的准确性 (96.78%) 与显著更好的时间复杂性 (0.11s).
- 修改的决策树 (J48) 在验证分割中实现了最高的准确性 (高达97.07%),具有较低的故障预测.
结论:
- 决策树 (J48) 算法,经过修改,对于云计算故障预测非常有效.
- 增强的J48算法平衡了高精度与高效的计算性能.
- 这项研究为提高云系统可靠性提供了强大的解决方案.
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