Operation and maintenance of large distributed cloud applications can quickly become unmanageably complex, putting human operators under immense stress when problems occur. Utilizing machine learning for identification and localization of anomalies in such systems supports human experts and enables fast mitigation. However, due to the various inter-dependencies of system components, anomalies do not only affect their origin but propagate through the distributed system. Taking this into account, we present Arvalus and its variant D-Arvalus, a neural graph transformation method that models system components as nodes and their dependencies and placement as edges to improve the identification and localization of anomalies. Given a series of metric KPIs, our method predicts the most likely system state - either normal or an anomaly class - and performs localization when an anomaly is detected. During our experiments, we simulate a distributed cloud application deployment and synthetically inject anomalies. The evaluation shows the generally good prediction performance of Arvalus and reveals the advantage of D-Arvalus which incorporates information about system component dependencies.

翻译：利用机器学习来识别和定位这些系统中的异常现象有助于人类专家,并能够快速缓减。然而,由于系统各组成部分的相互依存性不同,异常现象不仅影响其起源,而且通过分布式系统传播。考虑到这一点,我们介绍Arvalus及其变体D-Arvalus,这是一种神经图变方法,其模型系统组成为节点及其依附性和位置,作为边缘,用以改进异常现象的识别和定位。根据一系列计量 KPI,我们的方法预测最有可能的系统状态――正常或异常等级――并在发现异常时进行定位。在实验期间,我们模拟分布式云应用的部署和合成成形反常。评估显示Arvalus总体良好的预测性,并揭示D-Arvalus的优势,它包含关于系统组成部分依赖性的信息。