STGNN-Based Microservice Autoscaling and Placement for Fast QoS Recovery at the Edge
Yue Wu, Haitao Zhang, Yepeng Zhang · 2025
Edge computing enables low-latency data processing by placing resources closer to end users, and integrating microservices enhances scalability and responsiveness. However, maintaining QoS in dynamic, resource-constrained edge environments remains challenging. Existing methods often rely on local load monitoring, overlooking cross-layer load propagation, which leads to delayed autoscaling and prolonged QoS violations. To address this, we propose a Load Propagation-aware Autoscaling and Placement (LPAP) approach for proactive QoS management. LPAP anticipates performance degradation by dynamically adjusting scaling and placement based on both direct and indirect caller impacts and varying resource consumption patterns. It employs a Spatio-Temporal Graph Neural Network (STGNN) to estimate influence coefficients that quantify the impact of invocation edges on microservice performance. These coefficients guide an improved Actor-Critic algorithm that incorporates them into the Temporal Difference (TD) error and reward function to accelerate learning. Experiments show that LPAP effectively reduces QoS degradation duration and improves response times under dynamic workloads.