Microservice Scheduling With Spatiotemporal Learning in Dynamic and GPU-CPU Heterogeneous Edge Environment

Edge intelligence is rapidly evolving to support computation-intensive applications, yet scheduling containerized microservices in GPU-CPU heterogeneous edge environments remains challenging due to highly dynamic resource availability. Two critical bottlenecks hinder performance: the distinct hardware preferences of microservices, particularly between CPU and GPU architectures, which lead to significant performance gaps when placements are mismatched, and the non-trivial latency of container image downloads. To address these coupled challenges, we propose CHES, a Container-aware Heterogeneous Edge Scheduling framework. Unlike conventional heuristics that evaluate node resources and task sequences independently, our method leverages spatiotemporal learning to capture their joint impact on system performance. Specifically, we design a lightweight CNN to extract spatial correlations from heterogeneous node features, including compute capacity and bandwidth, and employ a GRU to model temporal dependencies among sequential microservice arrivals, where earlier scheduling decisions directly affect resource availability and image cache states for subsequent tasks. These representations are integrated into a Soft Actor-Critic (SAC) agent, enabling an adaptive policy that balances immediate execution latency with long-term image availability costs. Extensive experiments demonstrate that CHES significantly outperforms baselines by effectively aligning hardware-sensitive microservices with optimal nodes and mitigating cold-start overheads in dynamic workloads.