ACE-GNN accepted by IEEE TCAD: adaptive GNN co-inference scheduling for dynamic edge environments

👏 Paper title: ACE-GNN: Adaptive GNN Co-Inference with System-Aware Scheduling in Dynamic Edge Environments.

ACE-GNN improves GNN co-inference in dynamic edge environments, where bandwidth, device load, and multi-device access patterns can change during deployment. Static partitioning and fixed pipeline strategies may work well under one condition but become inefficient when the system state shifts.

The framework builds system-level awareness into runtime optimization. It predicts performance under changing edge conditions, searches for efficient execution schemes, and adaptively schedules between pipeline parallelism and data parallelism. Together with batch inference and communication middleware, ACE-GNN improves stability, latency, and energy efficiency for device-edge GNN serving.

ACE-GNN is designed for environments where static deployment choices quickly become suboptimal. Edge bandwidth can fluctuate, device load can change, and the graph structure itself can create unpredictable communication patterns.

By adapting scheduling decisions at runtime, the framework improves robustness for real-world GNN serving. It also demonstrates that efficient co-inference requires both model-level awareness and system-level scheduling, especially when multiple devices and edge resources collaborate.