  Performance issues permeate large-scale cloud service systems, which can lead
to huge revenue losses. To ensure reliable performance, it's essential to
accurately identify and localize these issues using service monitoring metrics.
Given the complexity and scale of modern cloud systems, this task can be
challenging and may require extensive expertise and resources beyond the
capacity of individual humans. Some existing methods tackle this problem by
analyzing each metric independently to detect anomalies. However, this could
incur overwhelming alert storms that are difficult for engineers to diagnose
manually. To pursue better performance, not only the temporal patterns of
metrics but also the correlation between metrics (i.e., relational patterns)
should be considered, which can be formulated as a multivariate metrics anomaly
detection problem. However, most of the studies fall short of extracting these
two types of features explicitly. Moreover, there exist some unlabeled
anomalies mixed in the training data, which may hinder the detection
performance. To address these limitations, we propose the Relational- Temporal
Anomaly Detection Model (RTAnomaly) that combines the relational and temporal
information of metrics. RTAnomaly employs a graph attention layer to learn the
dependencies among metrics, which will further help pinpoint the anomalous
metrics that may cause the anomaly effectively. In addition, we exploit the
concept of positive unlabeled learning to address the issue of potential
anomalies in the training data. To evaluate our method, we conduct experiments
on a public dataset and two industrial datasets. RTAnomaly outperforms all the
baseline models by achieving an average F1 score of 0.929 and Hit@3 of 0.920,
demonstrating its superiority.
