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.

References

[1]

Sergi Abadal, Akshay Jain, Robert Guirado, Jorge López-Alonso, and Eduard Alarcón. 2021. Computing graph neural networks: A survey from algorithms to accelerators. ACM Computing Surveys (CSUR) 54, 9 (2021), 1–38.

Digital Library

[2]

Zainab Abbas, Vasiliki Kalavri, Paris Carbone, and Vladimir Vlassov. 2018. Streaming graph partitioning: an experimental study. Proceedings of the VLDB Endowment 11, 11 (2018), 1590–1603.

Digital Library

[3]

James Bruno, Edward G Coffman Jr, and Ravi Sethi. 1974. Scheduling independent tasks to reduce mean finishing time. Commun. ACM 17, 7 (1974), 382–387.

Digital Library

[4]

Zhenkun Cai, Xiao Yan, Yidi Wu, Kaihao Ma, James Cheng, and Fan Yu. 2021. DGCL: an efficient communication library for distributed GNN training. In Proceedings of the Sixteenth European Conference on Computer Systems. 130–144.

Digital Library

[5]

Deepayan Chakrabarti, Yiping Zhan, and Christos Faloutsos. 2004. R-MAT: A recursive model for graph mining. In Proceedings of the 2004 SIAM International Conference on Data Mining. SIAM, 442–446.

[6]

Buru Chang, Gwanghoon Jang, Seoyoon Kim, and Jaewoo Kang. 2020. Learning graph-based geographical latent representation for point-of-interest recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 135–144.

Digital Library

[7]

Chao Chen, Karl Petty, Alexander Skabardonis, Pravin Varaiya, and Zhanfeng Jia. 2001. Freeway performance measurement system: mining loop detector data. Transportation Research Record 1748, 1 (2001), 96–102.

[8]

Ching-Han Chen, Ming-Yi Lin, and Chung-Chi Liu. 2018. Edge computing gateway of the industrial internet of things using multiple collaborative microcontrollers. IEEE Network 32, 1 (2018), 24–32.

[9]

Jianguo Chen, Kenli Li, Qingying Deng, Keqin Li, and S Yu Philip. 2019. Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Transactions on Industrial Informatics(2019).

[10]

James Cipar, Qirong Ho, Jin Kyu Kim, Seunghak Lee, Gregory R Ganger, Garth Gibson, Kimberly Keeton, and Eric Xing. 2013. Solving the straggler problem with bounded staleness. In 14th Workshop on Hot Topics in Operating Systems (HotOS {XIV}).

[11]

LZ4 community. [n.d.]. LZ4 Homepage. [EB/OL]. https://lz4.github.io/lz4/ Accessed March 25, 2021.

[12]

Daniel Crankshaw, Gur-Eyal Sela, Xiangxi Mo, Corey Zumar, Ion Stoica, Joseph Gonzalez, and Alexey Tumanov. 2020. InferLine: latency-aware provisioning and scaling for prediction serving pipelines. In Proceedings of the 11th ACM Symposium on Cloud Computing. 477–491.

Digital Library

[13]

Daniel Crankshaw, Xin Wang, Guilio Zhou, Michael J Franklin, Joseph E Gonzalez, and Ion Stoica. 2017. Clipper: A low-latency online prediction serving system. In 14th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 17). 613–627.

[14]

Deepjyoti Deka, Saurav Talukdar, Michael Chertkov, and Murti V Salapaka. 2020. Graphical models in meshed distribution grids: Topology estimation, change detection & limitations. IEEE Transactions on Smart Grid 11, 5 (2020), 4299–4310.

[15]

Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, and Philip S Yu. 2020. Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM’20).

Digital Library

[16]

Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In The World Wide Web Conference. 417–426.

Digital Library

[17]

Boyuan Feng, Yuke Wang, Xu Li, Shu Yang, Xueqiao Peng, and Yufei Ding. 2020. SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 1044–1052.

[18]

Matthias Fey and Jan E. Lenssen. [n.d.]. PyTorch Geometric Github Repository. [EB/OL]. https://github.com/rusty1s/pytorch_geometric Accessed March 25, 2021.

[19]

Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.

[20]

Swapnil Gandhi and Anand Padmanabha Iyer. 2021. P3: Distributed Deep Graph Learning at Scale. In 15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21). 551–568.

[21]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 855–864.

Digital Library

[22]

Arpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao, Antoine Kaufmann, Ymir Vigfusson, and Jonathan Mace. 2020. Serving DNNs like Clockwork: Performance Predictability from the Bottom Up. In 14th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 20). 443–462.

[23]

Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. [n.d.]. ASTGCN Github Repository. [EB/OL]. https://github.com/wanhuaiyu/ASTGCN-r-pytorch Accessed March 25, 2021.

[24]

Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 922–929.

Digital Library

[25]

Zhiwei Guo and Heng Wang. 2020. A deep graph neural network-based mechanism for social recommendations. IEEE Transactions on Industrial Informatics 17, 4 (2020), 2776–2783.

[26]

Ramyad Hadidi, Jiashen Cao, Matthew Woodward, Michael S Ryoo, and Hyesoon Kim. 2018. Distributed perception by collaborative robots. IEEE Robotics and Automation Letters 3, 4 (2018), 3709–3716.

[27]

William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025–1035.

[28]

Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, and Yida Wang. 2020. Featgraph: A flexible and efficient backend for graph neural network systems. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 1–13.

[29]

Rongzhou Huang, Chuyin Huang, Yubao Liu, Genan Dai, and Weiyang Kong. 2020. LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020. ijcai.org, 2355–2361.

[30]

Abhinav Jangda, Sandeep Polisetty, Arjun Guha, and Marco Serafini. 2021. Accelerating graph sampling for graph machine learning using gpus. In Proceedings of the Sixteenth European Conference on Computer Systems. 311–326.

Digital Library

[31]

Hyeongseok Jeon, Dongsuk Kum, and Junwon Choi. 2020. SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network. In IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ.

Digital Library

[32]

Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken. 2020. Improving the accuracy, scalability, and performance of graph neural networks with roc. Proceedings of Machine Learning and Systems 2 (2020), 187–198.

[33]

Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, and Alex Aiken. 2020. Redundancy-Free Computation for Graph Neural Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 997–1005.

Digital Library

[34]

Weiwei Jiang and Jiayun Luo. 2021. Graph Neural Network for Traffic Forecasting: A Survey. arXiv preprint arXiv:2101.11174(2021).

[35]

George Karypis and Vipin Kumar. 1997. METIS: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. (1997).

[36]

Abdullah Khanfor, Amal Nammouchi, Hakim Ghazzai, Ye Yang, Mohammad R Haider, and Yehia Massoud. 2020. Graph Neural Networks-based Clustering for Social Internet of Things. In 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 1056–1059.

[37]

Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net.

[38]

Stefanos Laskaridis, Stylianos I Venieris, Mario Almeida, Ilias Leontiadis, and Nicholas D Lane. 2020. SPINN: synergistic progressive inference of neural networks over device and cloud. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. 1–15.

Digital Library

[39]

En Li, Liekang Zeng, Zhi Zhou, and Xu Chen. 2019. Edge AI: On-demand accelerating deep neural network inference via edge computing. IEEE Transactions on Wireless Communications 19, 1(2019), 447–457.

[40]

Qingbiao Li, Weizhe Lin, Zhe Liu, and Amanda Prorok. 2021. Message-aware graph attention networks for large-scale multi-robot path planning. IEEE Robotics and Automation Letters 6, 3 (2021), 5533–5540.

[41]

Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.

[42]

Zhiqi Lin, Cheng Li, Youshan Miao, Yunxin Liu, and Yinlong Xu. 2020. PaGraph: Scaling GNN training on large graphs via computation-aware caching. In Proceedings of the 11th ACM Symposium on Cloud Computing. 401–415.

Digital Library

[43]

Husong Liu, Shengliang Lu, Xinyu Chen, and Bingsheng He. 2020. G3: when graph neural networks meet parallel graph processing systems on GPUs. Proceedings of the VLDB Endowment 13, 12 (2020), 2813–2816.

Digital Library

[44]

László Lovász and Michael D Plummer. 2009. Matching theory. Vol. 367. American Mathematical Soc.

[45]

Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, and Yafei Dai. 2019. Neugraph: parallel deep neural network computation on large graphs. In 2019 USENIX Annual Technical Conference (USENIX ATC 19). 443–458.

[46]

Jiachen Mao, Xiang Chen, Kent W Nixon, Christopher Krieger, and Yiran Chen. 2017. Modnn: Local distributed mobile computing system for deep neural network. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017. IEEE, 1396–1401.

[47]

Claudio Marche, Luigi Atzori, Virginia Pilloni, and Michele Nitti. 2020. How to exploit the Social Internet of Things: Query Generation Model and Device Profiles’ Dataset. Computer Networks (2020), 107248.

[48]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In International Conference on Learning Representations, 2013, Workshop Track Proceedings.

[49]

Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, and Shivaram Venkataraman. 2021. Marius: Learning Massive Graph Embeddings on a Single Machine. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21). USENIX Association. 533–549.

[50]

Damian Owerko, Fernando Gama, and Alejandro Ribeiro. 2018. Predicting power outages using graph neural networks. In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 743–747.

[51]

Anil Pacaci and M Tamer Özsu. 2019. Experimental analysis of streaming algorithms for graph partitioning. In Proceedings of the 2019 International Conference on Management of Data. 1375–1392.

Digital Library

[52]

Shebuti Rayana and Leman Akoglu. 2015. Collective opinion spam detection: Bridging review networks and metadata. In Proceedings of the 21th acm sigkdd international conference on knowledge discovery and data mining. 985–994.

Digital Library

[53]

Mahadev Satyanarayanan, Paramvir Bahl, Ramón Caceres, and Nigel Davies. 2009. The case for vm-based cloudlets in mobile computing. IEEE pervasive Computing 8, 4 (2009), 14–23.

Digital Library

[54]

Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. 2016. Edge computing: Vision and challenges. IEEE internet of things journal 3, 5 (2016), 637–646.

[55]

Shyam Anil Tailor, Javier Fernández-Marqués, and Nicholas Donald Lane. 2021. Degree-Quant: Quantization-Aware Training for Graph Neural Networks. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net.

[56]

John Thorpe, Yifan Qiao, Jonathan Eyolfson, Shen Teng, Guanzhou Hu, Zhihao Jia, Jinliang Wei, Keval Vora, Ravi Netravali, Miryung Kim, 2021. Dorylus: affordable, scalable, and accurate GNN training with distributed CPU servers and serverless threads. In 15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21). 495–514.

[57]

Chao Tian, Lingxiao Ma, Zhi Yang, and Yafei Dai. 2020. PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network. In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 936–945.

[58]

Ekaterina Tolstaya, Fernando Gama, James Paulos, George Pappas, Vijay Kumar, and Alejandro Ribeiro. 2020. Learning decentralized controllers for robot swarms with graph neural networks. In Conference on Robot Learning. PMLR, 671–682.

[59]

Leslie G Valiant. 1990. A bridging model for parallel computation. Commun. ACM 33, 8 (1990), 103–111.

Digital Library

[60]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.

[61]

Lei Wang, Qiang Yin, Chao Tian, Jianbang Yang, Rong Chen, Wenyuan Yu, Zihang Yao, and Jingren Zhou. 2021. FlexGraph: a flexible and efficient distributed framework for GNN training. In Proceedings of the Sixteenth European Conference on Computer Systems. 67–82.

Digital Library

[62]

Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, 2019. Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315(2019).

[63]

Xiaofei Wang, Yiwen Han, Victor CM Leung, Dusit Niyato, Xueqiang Yan, and Xu Chen. 2020. Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys & Tutorials 22, 2 (2020), 869–904.

[64]

Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. 2020. Traffic flow prediction via spatial temporal graph neural network. In Proceedings of The Web Conference 2020. 1082–1092.

Digital Library

[65]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems (2020).

[66]

Mengwei Xu, Zhe Fu, Xiao Ma, Li Zhang, Yanan Li, Feng Qian, Shangguang Wang, Ke Li, Jingyu Yang, and Xuanzhe Liu. 2021. From Cloud to Edge: A First Look at Public Edge Platforms. ACM Internet Measurement Conference 2021(2021), 0–18.

Digital Library

[67]

Mingyu Yan, Lei Deng, Xing Hu, Ling Liang, Yujing Feng, Xiaochun Ye, Zhimin Zhang, Dongrui Fan, and Yuan Xie. 2020. Hygcn: A gcn accelerator with hybrid architecture. In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE, 15–29.

[68]

Jiexia Ye, Juanjuan Zhao, Kejiang Ye, and Chengzhong Xu. 2020. How to build a graph-based deep learning architecture in traffic domain: A survey. IEEE Transactions on Intelligent Transportation Systems (2020).

[69]

Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3634–3640.

[70]

Zixuan Yuan, Hao Liu, Yanchi Liu, Denghui Zhang, Fei Yi, Nengjun Zhu, and Hui Xiong. 2020. Spatio-temporal dual graph attention network for query-poi matching. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 629–638.

Digital Library

[71]

Liekang Zeng, Xu Chen, Zhi Zhou, Lei Yang, and Junshan Zhang. 2020. CoEdge: Cooperative DNN Inference With Adaptive Workload Partitioning Over Heterogeneous Edge Devices. IEEE/ACM Transactions on Networking(2020).

[72]

Liekang Zeng, En Li, Zhi Zhou, and Xu Chen. 2019. Boomerang: On-demand cooperative deep neural network inference for edge intelligence on the industrial Internet of Things. IEEE Network 33, 5 (2019), 96–103.

Digital Library

[73]

Dalong Zhang, Xin Huang, Ziqi Liu, Jun Zhou, Zhiyang Hu, Xianzheng Song, Zhibang Ge, Lin Wang, Zhiqiang Zhang, and Yuan Qi. [n.d.]. AGL: A Scalable System for Industrial-purpose Graph Machine Learning. Proceedings of the VLDB Endowment 13, 12 ([n. d.]).

[74]

Zhuoran Zhao, Kamyar Mirzazad Barijough, and Andreas Gerstlauer. 2018. Deepthings: Distributed adaptive deep learning inference on resource-constrained iot edge clusters. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 37, 11(2018), 2348–2359.

[75]

Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, and George Karypis. 2020. Distdgl: distributed graph neural network training for billion-scale graphs. In 2020 IEEE/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3). IEEE, 36–44.

[76]

Ting Zhong, Shengming Zhang, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, and Jin Wu. 2020. Hybrid graph convolutional networks with multi-head attention for location recommendation. World Wide Web 23, 6 (2020), 3125–3151.

Digital Library

[77]

Hao Zhou, Dongchun Ren, Huaxia Xia, Mingyu Fan, Xu Yang, and Hai Huang. 2021. AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction. Neurocomputing 445(2021), 298–308.

[78]

Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI Open 1(2020), 57–81.

[79]

Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang. 2019. Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107, 8 (2019), 1738–1762.

[80]

Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, and Jingren Zhou. 2019. AliGraph: a comprehensive graph neural network platform. Proceedings of the VLDB Endowment 12, 12 (2019), 2094–2105.

Digital Library

Cited By

Cong RZhao ZWang MMin GLiu JMo J(2025)Task-Aware Service Placement for Distributed Learning in Wireless Edge NetworksIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2025.353962036:4(731-744)Online publication date: Apr-2025
https://doi.org/10.1109/TPDS.2025.3539620
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 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 Web Conference 202410.1145/3589334.3645383(2826-2835)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645383
Show More Cited By

Index Terms

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

Index terms have been assigned to the content through auto-classification.

Recommendations

A Pattern for Fog Computing
VikingPLoP '16: Proceedings of the 10th Travelling Conference on Pattern Languages of Programs

Fog Computing is a new variety of the cloud computing paradigm that brings virtualized cloud services to the edge of the network to control the devices in the IoT. We present a pattern for fog computing which describes its architecture, including its ...
Fog Computing: Issues and Challenges in Security and Forensics
COMPSAC '15: Proceedings of the 2015 IEEE 39th Annual Computer Software and Applications Conference - Volume 03

Although Fog Computing is defined as the extension of the Cloud Computing paradigm, its distinctive characteristics in the location sensitivity, wireless connectivity, and geographical accessibility create new security and forensics issues and ...
Fog computing: from architecture to edge computing and big data processing

Cloud computing plays a vital role in processing a large amount of data. However, with the arrival of the Internet of Things, huge data are generated from these devices. Thus, there is the need for bringing characteristics of cloud closer to the request ...

Comments

Information & Contributors

Information

Published In

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
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WWW '22

Sponsor:

SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
953
Total Downloads

Downloads (Last 12 months)168
Downloads (Last 6 weeks)16

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Cong RZhao ZWang MMin GLiu JMo J(2025)Task-Aware Service Placement for Distributed Learning in Wireless Edge NetworksIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2025.353962036:4(731-744)Online publication date: Apr-2025
https://doi.org/10.1109/TPDS.2025.3539620
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 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 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
Li HMa MLiu YZhao PLi SLi ZChintalapati MDang YBansal CRajmohan SLin QZhang D(2024)Can We Trust Auto-Mitigation? Improving Cloud Failure Prediction with Uncertain Positive Learning2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE62328.2024.00054(499-510)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ISSRE62328.2024.00054
Mal MGong CZeng LYang Y(2024)MOGR: Multi-task Offloading via Graph Representation in Heterogeneous Computing NetworkICC 2024 - IEEE International Conference on Communications10.1109/ICC51166.2024.10622588(1237-1242)Online publication date: 9-Jun-2024
https://doi.org/10.1109/ICC51166.2024.10622588
Zeng LChen XHuang PLuo KZhang XZhou Z(2023)Serving Graph Neural Networks With Distributed Fog Servers for Smart IoT ServicesIEEE/ACM Transactions on Networking10.1109/TNET.2023.329305232:1(550-565)Online publication date: 20-Jul-2023
https://dl.acm.org/doi/10.1109/TNET.2023.3293052
Pagliari APierre G(2023)TransScale: Combined-Approach Elasticity for Stream Processing in Fog Environments2023 11th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud)10.1109/MobileCloud58788.2023.00009(17-24)Online publication date: Jul-2023
https://doi.org/10.1109/MobileCloud58788.2023.00009
Zeng LYang CHuang PZhou ZYu SChen X(2023)GNN at the Edge: Cost-Efficient Graph Neural Network Processing Over Distributed Edge ServersIEEE Journal on Selected Areas in Communications10.1109/JSAC.2022.322942241:3(720-739)Online publication date: Mar-2023
https://doi.org/10.1109/JSAC.2022.3229422
Yin DDong FChen BShen DZhou RGuo XHuang Z(2023)WAEVSR: Enabling Collaborative Live Video Super-Resolution in Wide-Area MEC Environment2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)10.1109/IWQoS57198.2023.10188713(1-11)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWQoS57198.2023.10188713

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten