Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, traditional model serving paradigms usually resort to the cloud by fully uploading geo-distributed input data to remote datacenters. However, our empirical measurements reveal the significant communication overhead of such cloud-based serving and highlight the profound potential in applying the emerging fog computing. To maximize the architectural benefits brought by fog computing, in this paper, we present Fograph, a novel distributed real-time GNN inference framework that leverages diverse and dynamic resources of multiple fog nodes in proximity to IoT data sources. By introducing heterogeneity-aware execution planning and GNN-specific compression techniques, Fograph tailors its design to well accommodate the unique characteristics of GNN serving in fog environments. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and fog deployment by up to 5.39x execution speedup and 6.84x throughput improvement.

翻译：图神经网络（GNNs）凭借其在图结构上提取潜在表示的卓越能力，在众多应用中引起了广泛兴趣。为了在物联网驱动的智能应用中提供基于GNN的服务，传统模型服务范式通常依赖云端，将地理分布式输入数据完全上传至远程数据中心。然而，我们的实证测量揭示了这种基于云服务的显著通信开销，并凸显了应用新兴雾计算的巨大潜力。为最大化雾计算带来的架构优势，本文提出了Fograph——一种新型分布式实时GNN推理框架，该框架利用靠近物联网数据源的多个雾节点的多样化和动态资源。通过引入异构感知执行规划和GNN特定压缩技术，Fograph的设计针对雾环境中GNN服务的独特特性进行了定制化的适配。基于原型系统的评估和案例研究表明，与最先进的云服务和雾部署相比，Fograph实现了高达5.39倍的执行加速和6.84倍的吞吐量提升。