一句话：把云服务关键故障定义为 QoS 指标越过阈值的极端事件，用混合高斯建模并加极端事件正则化器，提前预测宕机。
云服务故障虽属小概率事件却会造成严重的收入与信誉损失，但关键故障本身稀少，且在可观测数据上缺乏清晰定义，导致提前预测十分困难。本文提出 Outage-Watch，将关键服务宕机定义为由一组指标所刻画的 QoS 恶化：用混合高斯灵活建模 QoS 指标的分布，并引入极端事件正则化器改善分布尾部的学习质量，从而在样本稀少的极端区域仍能有效预测。若任一 QoS 指标越过阈值的概率发生显著变化，即判定即将发生极端事件并预警。作者在真实 SaaS 公司的数据集上评估，平均 AUC 达 0.98，明显优于传统方法；并在企业云服务系统中部署，检出了所有伴随服务指标变化的宕机，把平均检测时间（MTTD）最多缩短 88%，验证了方法的有效性。
Cloud services are omnipresent and critical cloud service failure is a fact of life. In order to retain customers and prevent revenue loss, it is important to provide high reliability guarantees for these services. One way to do this is by predicting outages in advance, which can help in reducing the severity as well as time to recovery. It is difficult to forecast critical failures due to the rarity of these events. Moreover, critical failures are ill-defined in terms of observable data. Our proposed method, Outage-Watch, defines critical service outages as deteriorations in the Quality of Service (QoS) captured by a set of metrics. Outage-Watch detects such outages in advance by using current system state to predict whether the QoS metrics will cross a threshold and initiate an extreme event. A mixture of Gaussian is used to model the distribution of the QoS metrics for flexibility and an extreme event regularizer helps in improving learning in tail of the distribution. An outage is predicted if the probability of any one of the QoS metrics crossing threshold changes significantly. Our evaluation on a real-world SaaS company dataset shows that Outage-Watch significantly outperforms traditional methods with an average AUC of 0.98. Additionally, Outage-Watch detects all the outages exhibiting a change in service metrics and reduces the Mean Time To Detection (MTTD) of outages by up to 88% when deployed in an enterprise cloud-service system, demonstrating efficacy of our proposed method.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2309-17340,
title = {Outage-Watch: Early Prediction of Outages using Extreme Event Regularizer},
author = {Shubham Agarwal and Sarthak Chakraborty and Shaddy Garg and Sumit Bisht and Chahat Jain and Ashritha Gonuguntla and Shiv Saini},
booktitle = {FSE},
year = {2023},
doi = {10.1145/3611643.3616316}
}