Macro-Micro Collaborative Learning for Logical Data Center Microservice Indicators Forecasting

As microservice architecture is evolving toward Logical Data Center (LDC), accurate forecasting of the microservices indicators can support reasonable resource allocation, thereby ensuring the availability and reliability of cloud service. From a macro perspective, due to the architecture hierarchy, microservices exhibit: 1) collaborative relationships derived from shared functionalities, 2) backup relationships between replicas, and 3) dynamic correlation driven by cooperation. From a micro perspective, there exist causal relationships among indicators within a microservice. That is, workload will first impact system consumption, such as CPU and memory usage, then affect service quality like system latency. Based on these insights, we propose MaMiClif, a macro-micro collaborative learning framework for LDC microservice indicators forecasting. MaMiClif constructs Macro Graph and Micro Matrix to model the microservices dependencies and the causality of indicators. To learn fine-grained indicator dependencies, Indicator-Centric Embedding is leveraged to generate representations for indicator series. We use Heterogeneous Graph Convolution to update workload representations based on the Macro Graph, and adopt Causal Sparse Self-attention to integrate causal strength into the self-attention calculation, enabling a comprehensive exploration of dependencies among indicators. Experiments on two datasets, including LDC_MS, which was collected from the LDC system of Ant Group, demonstrate the effectiveness of MaMiClif.