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Manod:分布式系统的多模式异常检测框架
Wen Liu1, Degang Sun2, Haitian Yang1
1Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100080, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100080, China.
概括
通过分析指标和日志来提高分布式系统的可靠性. 这种多模式的方法显著提高了故障检测的准确性,减少了系统故障.
科学领域:
- 计算机科学
- 软件工程
- 系统工程
背景情况:
- 分布式基础设施对于可扩展的应用程序至关重要,需要强大的异常检测以实现系统稳定性.
- 目前的异常检测方法通常使用单个数据源 (指标或日志),导致不准确和错误的结果.
- 整合多式数据 (计量和日志) 提供了更全面的系统视图,以改善故障检测.
研究的目的:
- 提出Manod,一种用于监测分布式系统健康状况的半监督故障检测方法.
- 利用多式数据 (指标和日志) 进行更准确和可靠的异常识别.
- 提高检测系统异常的准确性并减少错误的阳性/阴性.
主要方法:
- 采用基于图的层次编码方法来生成歧视性表示.
- 使用预先训练的语言模型处理和建模系统指标和日志.
- 引入一个关闭的注意力融合机制,以有效地整合异构的数据模式.
主要成果:
- 马诺德在模拟数据集 (D1) 上获得了0.870的高F1分,在现实数据集 (D2) 上获得了0.934分.
- 提出的方法在故障检测任务中显著优于所有基线模型.
- 在异常检测中减轻错误阳性和错误负性的有效性.
结论:
- 通过使用多式联网数据,Manod提供了一种有效的半监督方法来检测分布式系统中的故障.
- 通过先进的融合技术整合指标和日志,提高了系统健康监测能力.
- 该方法显示了确保大型软件系统稳定可靠运行的强大潜力.
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