Performance Analysis of Machine Learning Based Fault Detection for Cloud Infrastructure

被引:5
作者
Won, Hojoon [1 ]
Kim, Younghan [1 ]
机构
[1] Soongsil Univ, Sch Elect Engn, Seoul, South Korea
来源
35TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2021) | 2021年
关键词
cloud; machine learning; fault detection; feature importance; PLATFORM; SERVICE;
D O I
10.1109/ICOIN50884.2021.9333875
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As the cloud infrastructure becomes more complex, the importance of fault detection technology is increasing. A machine learning-based fault detection technology is being used to overcome the limitations of the existing fault detection method through log analysis and threshold-based fault detection method. Machine learning-based fault detection methods are greatly influenced by features. In this paper, we introduce feature engineering techniques that can affect accuracy, and propose a method to improve the performance of fault detection models in cloud infrastructure through comparative analysis and verification of various feature analysis models.
引用
收藏
页码:877 / 880
页数:4
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