ACE-GNN: Adaptive GNN Co-Inference with System-Aware Scheduling in Dynamic Edge Environments

TL;DR

ACE-GNN introduces an adaptive co-inference framework for GNNs in dynamic edge environments, optimizing performance and stability through system-aware scheduling and novel runtime strategies.

Contribution

It is the first framework to adapt GNN co-inference dynamically in edge settings, combining system-level prediction, adaptive scheduling, and efficient communication.

Findings

Achieves up to 12.7x speedup over baseline methods.

Reduces energy consumption by 82.3%.

Improves energy efficiency by 11.7 times compared to Fograph.

Abstract

The device-edge co-inference paradigm effectively bridges the gap between the high resource demands of Graph Neural Networks (GNNs) and limited device resources, making it a promising solution for advancing edge GNN applications. Existing research enhances GNN co-inference by leveraging offline model splitting and pipeline parallelism (PP), which enables more efficient computation and resource utilization during inference. However, the performance of these static deployment methods is significantly affected by environmental dynamics such as network fluctuations and multi-device access, which remain unaddressed. We present ACE-GNN, the first Adaptive GNN Co-inference framework tailored for dynamic Edge environments, to boost system performance and stability. ACE-GNN achieves performance awareness for complex multi-device access edge systems via system-level abstraction and two novel prediction methods, enabling rapid runtime scheme optimization. Moreover, we introduce a data parallelism (DP) mechanism in the runtime optimization space, enabling adaptive scheduling between PP and DP to leverage their distinct advantages and maintain stable system performance. Also, an efficient batch inference strategy and specialized communication middleware are implemented to further improve performance. Extensive experiments across diverse applications and edge settings demonstrate that ACE-GNN achieves a speedup of up to 12.7x and an energy savings of 82.3% compared to GCoDE, as well as 11.7 better energy efficiency than Fograph.