Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketching

To ensure the performance of online service systems, their status is closely monitored with various software and system metrics. Performance anomalies represent the performance degradation issues (e.g., slow response) of the service systems. When performing anomaly detection over the metrics, existi...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) s. 61 - 72
Hlavní autori: Chen, Zhuangbin, Liu, Jinyang, Su, Yuxin, Zhang, Hongyu, Ling, Xiao, Lyu, Michael R.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: ACM 01.05.2022
Predmet:
Adaptation models
Adaptive learning
Cloud computing
Measurement
online learning
performance anomaly detection
Production
Software
Software algorithms
Time series analysis
ISSN:1558-1225
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:To ensure the performance of online service systems, their status is closely monitored with various software and system metrics. Performance anomalies represent the performance degradation issues (e.g., slow response) of the service systems. When performing anomaly detection over the metrics, existing methods often lack the merit of interpretability, which is vital for engineers and analysts to take remediation actions. Moreover, they are unable to effectively accommodate the ever-changing services in an online fashion. To address these limitations, in this paper, we propose ADSketch, an interpretable and adaptive performance anomaly detection approach based on pattern sketching. ADSketch achieves interpretability by identifying groups of anomalous metric patterns, which represent particular types of performance issues. The underlying issues can then be immediately recognized if similar patterns emerge again. In addition, an adaptive learning algorithm is designed to embrace unprecedented patterns induced by service updates or user behavior changes. The proposed approach is evaluated with public data as well as industrial data collected from a representative online service system in Huawei Cloud. The experimental results show that ADSketch outperforms state-of-the-art approaches by a significant margin, and demonstrate the effectiveness of the online algorithm in new pattern discovery. Furthermore, our approach has been successfully deployed in industrial practice.
ISSN:1558-1225
DOI:10.1145/3510003.3510085