  Microservice architecture has sprung up over recent years for managing
enterprise applications, due to its ability to independently deploy and scale
services. Despite its benefits, ensuring the reliability and safety of a
microservice system remains highly challenging. Existing anomaly detection
algorithms based on a single data modality (i.e., metrics, logs, or traces)
fail to fully account for the complex correlations and interactions between
different modalities, leading to false negatives and false alarms, whereas
incorporating more data modalities can offer opportunities for further
performance gain. As a fresh attempt, we propose in this paper a
semi-supervised graph-based anomaly detection method, MSTGAD, which seamlessly
integrates all available data modalities via attentive multi-modal learning.
First, we extract and normalize features from the three modalities, and further
integrate them using a graph, namely MST (microservice system twin) graph,
where each node represents a service instance and the edge indicates the
scheduling relationship between different service instances. The MST graph
provides a virtual representation of the status and scheduling relationships
among service instances of a real-world microservice system. Second, we
construct a transformer-based neural network with both spatial and temporal
attention mechanisms to model the inter-correlations between different
modalities and temporal dependencies between the data points. This enables us
to detect anomalies automatically and accurately in real-time. The source code
of MSTGAD is publicly available at
https://github.com/alipay/microservice_system_twin_graph_based_anomaly_detection.
