基于深度学习的无监督KPI异常检测

Research on Unsupervised KPI Anomaly Detection Based on Deep Learning

【目的】关键性能指标(Key Performance Indicator,KPI)异常检测作为互联网智能运维的基础,对快速故障发现和修复具有重要意义。【文献范围】本文重点调研国内外基于深度生成模型的无监督KPI异常检测方法。【方法】系统地阐述了Donut、Bagel和Buzz三种无监督KPI异常检测方法的理论模型,并分析了它们在准确性和效率等方面的优势与不足。【结果】本文基于生产环境中的KPI数据验证了三个方法的性能。【局限】基于深度生成模型的KPI异常检测方法仍在不断地演进,未来将探索更多该领域的新方法。【结论】针对不同特征的KPI数据,需要采用不同的深度生成模型:对于时间信息敏感的KPI数据,需要采用Bagel进行异常检测;对于非周期性的复杂KPI数据,需要采用Buzz检测其异常行为。

[Objective]Automatic key performance indicator(KPI),the basis of Internet artificial intelligence operations(AIOps),is of vital importance to rapid failure detection and mitigation.[Scope of the literature]In this paper,we investigate unsupervised KPI anomaly detection methods,which are based on deep generative models.[Methods]We systematically describe the theoretic model of Donut,Bagel,and Buzz,which are all unsupervised KPI anomaly detection methods,and analyze their advantages and limitations in terms of accuracy and efficiency.[Results]We evaluate the performance of those three approaches based on real-world KPI data.[Limitations]The KPI anomaly detection methods based on deep generative model are continuously evolving,and we will explore more methods in this area.[Conclusions]Choosing a deep generative model should consider the characteristics of KPI data.Generally,if the KPI data is sensitive to timing information,we should apply Bagel to perform anomaly detection.Moreover,Buzz should be used if the data is non-seasonal and complex.