Supporting Internet-of-Things Analytics in a Fog Computing Platform

Hua Jun Hong, Pei Hsuan Tsai, An Chieh Cheng, Md Yusuf Sarwar Uddin, Nalini Venkatasubramanian, Cheng Hsin Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

Modern IoT analytics are computational and data intensive. Existing analytics are mostly hosted in cloud data centers, and may suffer from high latency, network congestion, and privacy issues. In this paper, we design, implement, and evaluate a fog computing platform that runs analytics in a distributed way on multiple devices, including IoT devices, edge servers, and data-center servers. We focus on the core optimization problem: making deployment decisions to maximize the number of satisfied IoT analytics. We carefully formulate the deployment problem and design an efficient algorithm, named SSE, to solve it. Moreover, we conduct a detailed measurement study to derive system models of the IoT analytics based on diverse QoS levels and heterogeneous devices to facilitate the optimal deployment decisions. We implement a testbed to conduct experiments, which show that the system models achieve reasonably good accuracy. More importantly, 100% of the deployed IoT analytics satisfy the QoS targets. We also conduct extensive simulations for larger-scale scenarios. The simulation results reveal that our SSE algorithm outperforms a state-of-the-art algorithm by up to 89.4% and 168.3% in terms of the number of satisfied IoT analytics and active devices. In addition, our SSE algorithm reduces CPU, RAM, and network resource consumptions by 18.4%, 12.7%, and 898.3%, respectively, and terminates in polynomial time.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 9th International Conference on Cloud Computing Technology and Science, CloudCom 2017
PublisherIEEE Computer Society
Pages138-145
Number of pages8
ISBN (Electronic)9781538606926
DOIs
Publication statusPublished - 2017 Dec 27
Event9th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2017 - Hong Kong, Hong Kong
Duration: 2017 Dec 112017 Dec 14

Publication series

NameProceedings of the International Conference on Cloud Computing Technology and Science, CloudCom
Volume2017-December
ISSN (Print)2330-2194
ISSN (Electronic)2330-2186

Other

Other9th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2017
CountryHong Kong
CityHong Kong
Period17-12-1117-12-14

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Software
  • Theoretical Computer Science

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  • Cite this

    Hong, H. J., Tsai, P. H., Cheng, A. C., Uddin, M. Y. S., Venkatasubramanian, N., & Hsu, C. H. (2017). Supporting Internet-of-Things Analytics in a Fog Computing Platform. In Proceedings - 2017 IEEE 9th International Conference on Cloud Computing Technology and Science, CloudCom 2017 (pp. 138-145). (Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom; Vol. 2017-December). IEEE Computer Society. https://doi.org/10.1109/CloudCom.2017.45