ASAD: Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles

• Published in: Applied sciences Vol. 12; no. 12; p. 5855
• Main Authors: Wang, Hao, Zhang, Yuanyuan, Liu, Yijia, Liu, Fenglin, Zhang, Hanyang, Xing, Bin, Xing, Minghai, Wu, Qiong, Chen, Liangyin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2022
• Subjects: Accuracy; Adaptive algorithms; adaptive seasonality anomaly detection; Algorithms; Anomalies; Business metrics; Clustering; Energy consumption; Error detection; intricate KPI profiles; KPI anomaly detection; Neural networks; Normal distribution; Quality of service; Seasonal variations; Time series
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12125855

Anomaly detection is the foundation of intelligent operation and maintenance (O&M), and detection objects are evaluated by key performance indicators (KPIs). For almost all computer O&M systems, KPIs are usually the machine-level operating data. Moreover, these high-frequency KPIs show a non-Gaussian distribution and are hard to model, i.e., they are intricate KPI profiles. However, existing anomaly detection techniques are incapable of adapting to intricate KPI profiles. In order to enhance the performance under intricate KPI profiles, this study presents a seasonal adaptive KPI anomaly detection algorithm ASAD (Adaptive Seasonality Anomaly Detection). We also propose a new eBeats clustering algorithm and calendar-based correlation method to further reduce the detection time and error. Through experimental tests, our ASAD algorithm has the best overall performance compared to other KPI anomaly detection methods.