research-article

Fograph: Enabling Real-Time Deep Graph Inference with Fog Computing

Authors:

Xu ChenAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1774 - 1784

https://doi.org/10.1145/3485447.3511982

Published: 25 April 2022 Publication History

Abstract

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, the traditional model serving paradigm resorts to the cloud by fully uploading the geo-distributed input data to the remote datacenter. 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 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 environment. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and vanilla fog deployment by up to 5.39 × execution speedup and 6.84 × throughput improvement.

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Cited By

Chen GLi KAbdelmoniem AYou LChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Exploring Representational Similarity Analysis to Protect Federated Learning from Data PoisoningCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3651503(525-528)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651503
Hu HLiu FPei QYuan YXu ZWang LChua TNgo CKa-Wei Lee RKumar RLauw H(2024)λGrapher: A Resource-Efficient Serverless System for GNN Serving through Graph SharingProceedings of the ACM on Web Conference 202410.1145/3589334.3645383(2826-2835)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645383
Xu AHu ZZhang XXiao HZheng HChen BZheng MZhong PKang YLi K(2024)QDRL: Queue-Aware Online DRL for Computation Offloading in Industrial Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2023.331613911:5(7772-7786)Online publication date: 1-Mar-2024
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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
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Lionel Médini
Université Lyon 1, France

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https://dl.acm.org/doi/10.1145/3589335.3651503
Hu HLiu FPei QYuan YXu ZWang LChua TNgo CKa-Wei Lee RKumar RLauw H(2024)λGrapher: A Resource-Efficient Serverless System for GNN Serving through Graph SharingProceedings of the ACM on Web Conference 202410.1145/3589334.3645383(2826-2835)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645383
Xu AHu ZZhang XXiao HZheng HChen BZheng MZhong PKang YLi K(2024)QDRL: Queue-Aware Online DRL for Computation Offloading in Industrial Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2023.331613911:5(7772-7786)Online publication date: 1-Mar-2024
https://doi.org/10.1109/JIOT.2023.3316139
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