基于机器学习的云基础设施故障检测性能分析

Hojoon Won, Younghan Kim
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引用次数: 6

摘要

随着云基础设施的日益复杂,故障检测技术的重要性与日俱增。基于机器学习的故障检测技术通过日志分析和基于阈值的故障检测方法来克服现有故障检测方法的局限性。基于机器学习的故障检测方法受特征的影响很大。本文介绍了影响准确率的特征工程技术,并通过对各种特征分析模型的对比分析和验证,提出了一种提高云基础设施中故障检测模型性能的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Analysis of Machine Learning Based Fault Detection for Cloud Infrastructure
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.
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