Spatial-Temporal Heterogenous Graph Contrastive Learning for Microservice Workload Prediction

With the widely adoption of microservice architecture in the cloud computing industry, accurate prediction of workloads, especially CPU cores, can support reasonable resource allocation, thereby optimizing the resource utilization of the system. However, workload prediction is challenging in two dimensions. In the temporal dimension, workload series 1) has non-stationary characteristics, leading to poor predictability; 2) has a multi-periodic nature with entangled temporal patterns; 3) may be influenced by dynamic system states like response time and number of requests. In the spatial dimension, when regarding microservices as nodes in a distributed system, there is no topology caused by physical connections, but exists complex similarity dependencies. Extracting robust spatial features from these dependencies presents difficulties. To address these, we propose STEAM, a Spatio Temporal Heterogenous Graph Contrastive Learning for Microservice Workload Prediction. STEAM leverages non-stationary decomposition self-attention to extract temporal features from non-stationary and multi-periodic workload series, while the decoupled embedding is used to capture system state information of microservices. By treating microservices as nodes and constructing a similarity graph, STEAM effectively models the similarity relationships between microservices. To reduce the prior interference caused by the similarity threshold and improve the robustness, STEAM constructs two heterogeneous augmentation views and uses contrastive learning to extract the shared consistent spatial features. The multi-scale learning is adopted to model the long- and short-term temporal features, forming a spatio-temporal stacking structure. Experiments on two datasets, including MS dataset obtained from Ant Group, which is one of the world’s largest cloud service providers, demonstrate the superiority of STEAM.