Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT
  Services
Liekang Zeng Xu Chen Peng Huang Ke Luo Xiaoxi Zhang Zhi Zhou
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.