System States Forecasting of Microservices Based on Spatio-Temporal Relationships

In the AIOps realm, precise system state forecasting is essential, particularly within microservices architectures, where may have dynamic deployments, varied call paths, and cascading effects complicate spatio-temporal relationships. Existing time series forecasting methods, which emphasize temporal patterns, fall short in capturing the critical spatial dimensions. Spatio-temporal graph methods, while useful, often overlook temporal trends and the length of forecast horizons. Furthermore, existing research about microservices tends to undervalue the role of network metrics and topological structures in reflecting system dynamics. This paper presents STMformer, a novel model designed for microservices state forecasting, adept at managing multi-node and multivariate time series based on diverse spatio-temporal relationships. It harnesses dynamic network connections and topological insights to model complex spatio-temporal interactions and incorporates a PatchCrossAttention module for global cascading effect analysis. Based on a microservices-based dataset we collect with our developed tool, we demonstrated that STMformer outperformed existing methods, reducing MAE by 8.6% and MSE by 2.2% in forecasting tasks. The source code is available at https://github.com/xuyifeiiie/STMformer.