mlsanityData to 7 Oct 2026

Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services

arXiv 2307.01684 ↗First posted 4 Jul 2023cs.DC cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 39 of its 95 references that are in the map. First posted on arXiv 4 Jul 2023.

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39 of 95 references.

References · 39

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27 more
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Citing papers per year: 2024: 2.

2 papers.

  1. 2024PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architecturescited by 2
  2. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2