Robust KPI Anomaly Detection for Large-Scale Software Services with Partial Labels

Abstract

To ensure the reliability of software services, operators collect and monitor a large number of KPI (Key Performance Indicator) streams constantly. KPI anomaly detection is vitally important for software service management. However, none of supervised learning methods, semi-supervised learning methods, transfer learning methods, or unsupervised learning methods achieve accurate anomaly detection for the large-scale, diverse, dynamically changing KPI streams with little labeling effort. In this paper, we propose PUAD, a PU learning-based method, to achieve accurate KPI anomaly detection requiring a few partial labels. It integrates clustering, PU learning, and semi-supervised learning to minimize labeling effort and improve anomaly detection accuracy simultaneously. Additionally, we propose a novel active learning method that selects the samples most likely to be positive in each iteration to avoid false alarms. We apply 208 real-world KPI streams collected from a large-scale software service provider to evaluate the performance of PUAD, demonstrating that it achieves a close F1-score to supervised learning methods with much fewer manual labels, and greatly outperforms semi-supervised learning methods, transfer learning methods, and unsupervised learning methods.