一句话：提出 ADSketch，用模式草图实现可解释且能自适应服务变化的在线性能异常检测。
在线服务系统通过多种软件与系统指标持续监控其运行状态，性能异常则表现为响应变慢等性能退化问题。现有的指标异常检测方法普遍缺乏可解释性，使工程师和分析人员难以据此采取修复措施；同时它们也无法以在线方式有效适应不断变化的服务。针对这些局限，本文提出 ADSketch，一种基于模式草图（pattern sketching）的可解释、自适应性能异常检测方法。ADSketch 通过识别成组的异常指标模式来获得可解释性，这些模式对应特定类型的性能问题，当相似模式再次出现时即可立刻识别出底层问题；此外，方法设计了自适应学习算法，以接纳由服务更新或用户行为变化所带来的前所未见的模式。作者在公开数据以及从华为云某代表性在线服务系统采集的工业数据上进行了评估，结果表明 ADSketch 显著优于当前最先进的方法，并验证了在线算法在新模式发现方面的有效性；该方法还已在工业实践中成功部署。
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.
@inproceedings{paperbot6956,
title = {Adaptive performance anomaly detection for online service systems via pattern sketching},
author = {Zhuangbin Chen and Jinyang Liu and Yuxin Su and Hongyu Zhang and Xiao Ling and Yongqiang Yang and Michael R. Lyu},
booktitle = {ICSE},
year = {2022},
doi = {10.1145/3510003.3510085}
}