Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (3): 87-100.doi: 10.11871/jfdc.issn.2096-742X.2020.03.008

• Special Issue: Next Generation Internet Technology & Application • Previous Articles     Next Articles

Research on Unsupervised KPI Anomaly Detection Based on Deep Learning

Zhang Shenglin1(),Lin Xiaofei1(),Sun Yongqian1,*(),Zhang Yuzhi1(),Pei Dan2()   

  1. 1. College of Software, Nankai University, Tianjin 300350,China
    2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  • Received:2020-04-10 Online:2020-06-20 Published:2020-08-19
  • Contact: Sun Yongqian E-mail:zhangsl@nankai.edu.cn;filler.helloworld@gmail.com;sunyongqian@nankai.edu.cn;zyz@nankai.edu.cn;peidan@tsinghua.edu.cn

Abstract:

[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.

Key words: deep learning, unsupervised learning, key performance indicator, anomaly detection, generative model