Robust and accurate performance anomaly detection and prediction for cloud applications: a novel ensemble learning-based framework

Abstract

Effectively detecting run-time performance anomalies is crucial for clouds to identify abnormal performance behavior and forestall future incidents. To be used for real-world applications, an effective anomaly detection framework should meet three main challenging requirements: high accuracy for identifying anomalies, good robustness when application patterns change, and prediction ability for upcoming anomalies. Unfortunately, existing research about performance anomaly detection usually focuses on improving detection accuracy, while little research tackles the three challenges simultaneously. We conduct experiments for existing detection methods on multiple application monitoring data, and results show that existing detection methods usually focus on different features in data, which will lead to their diverse performance on different data patterns. Therefore, existing anomaly detection methods have difficulty improving detection accuracy and robustness and predicting anomalies. To address the three requirements, we propose an Ensemble Learning-Based Detection (ELBD) framework which integrates existing well-selected detection methods. The framework includes three classic linear ensemble methods (maximum, average, and weighted average) and a novel deep ensemble method. Our experiments show that the ELBD framework realizes better detection accuracy and robustness, where the deep ensemble method can achieve the most accurate and robust detection for cloud applications. In addition, it can predict anomalies in the next four minutes with an F1 score higher than 0.8. The paper also proposes a new indicator ARP_score to measure detection accuracy, robustness, and multi-step prediction ability. The ARP_score of the deep ensemble method is 5.1821, which is much higher than other detection methods.