  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.
