一句话：提出基于 AutoML 的自适应异常检测框架 ADAMAS，通过无监督模型搜索与人在回路专家知识提升云服务性能异常检测。
云服务监控常通过指标分析识别性能问题，但现有方法难以检测跨服务、多样且演化的异常，且技术异常与业务影响间存在鸿沟。论文提出 ADAMAS，一种基于 AutoML 的自适应异常检测框架：设计无监督评估函数以自动搜索最优模型结构和参数，提升跨服务检测能力；引入轻量级人在回路机制融入专家知识，以适应演化异常并弥合预测异常与业务异常的差距；通过监控误预测异常率主动重配置模型，形成持续改进闭环。在 1 个公开和 2 个工业数据集上，ADAMAS 取得 0.891 F1 值，优于所有基线；消融验证了评估函数与专家知识的有效性。
A common practice in the reliability engineering of cloud services involves the collection of monitoring metrics, followed by comprehensive analysis to identify performance issues. However, existing methods often fall short of detecting diverse and evolving anomalies across different services. More-over, there exists a significant gap between the technical and business interpretation of anomalies, i.e., a detected anomaly may not have an actual impact on system performance or user experience. To address these challenges, we propose ADAMAS, an adaptive AutoML-based anomaly detection framework aiming to achieve practical anomaly detection in production cloud systems. To improve the ability to detect cross-service anomalies, we design a novel unsupervised evaluation function to facilitate the automatic searching of the optimal model structure and parameters. ADAMAS also contains a lightweight human-in-the-loop design, which can efficiently incorporate expert knowledge to adapt to the evolving anomaly patterns and bridge the gap between predicted anomalies and actual business exceptions. Fur-thermore, through monitoring the rate of mispredicted anomalies, ADAMAS proactively re-configures the optimal model, forming a continuous loop of system improvement. Extensive evaluation on one public and two industrial datasets shows that ADAMAS outperforms all baseline models with a 0.891 F1-score. The ablation study also proves the effectiveness of the evaluation function design and the incorporation of expert knowledge.
DOI 原文 ·
@inproceedings{paperbot8708,
title = {ADAMAS: Adaptive Domain-Aware Performance Anomaly Detection in Cloud Service Systems},
author = {Wenwei Gu and Jiazhen Gu and Jinyang Liu and Zhuangbin Chen and Jianping Zhang and Jinxi Kuang and Cong Feng and Yongqiang Yang and Michael R. Lyu},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00084}
}