  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.
