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A lightweight and cost effective edge intelligence architecture based on containerization technology

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An Author Correction to this article was published on 22 November 2019

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Abstract

The integration of Cloud computing and Internet of Things led to rapid growth in the edge computing field. This would not be achievable without combining the data centers’ managing systems with much more restrained technologies. One significantly effective and lightweight solution to this issue is presented by the Docker technology. It is able to manage virtualization process and can therefore be used to distribute, deploy and manage cloud and edge applications assigned into the clusters. In our study, this technology was represented by the Raspberry Pi devices, which are convenient thanks to their low cost, robust applicability and lightweight nature. Our application scenario focuses on identification of the human activities. In this paper, we suggest and evaluate an architecture on the basis of the distributed edge/cloud integration paradigm. We explain all of its advantages which lie in the combination of affordability and several other benefits provided by the fact that data processing is conducted by the edge devices instead of the central server. To recognize and identify human activity, the Regularized Extreme Leaning Machine (RELM) was engaged in our architecture. Our study presents detailed information about our use case scenario and the experimental simulation we performed.

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Change history

  • 22 November 2019

    The original version of this article unfortunately contained a mistake. In the originally published version, the acknowledgments information is missing. The article���s acknowledgments information is provided below.

Notes

  1. https://docs.docker.com/engine/docker-overview/

  2. https://www.digitalocean.com

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Authors and Affiliations

  1. Research Chair of Pervasive and Mobile Computing, Riyadh, Saudi Arabia

    Mabrook Al-Rakhami, Mohammad Mehedi Hassan & Atif Alamri

  2. Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Mabrook Al-Rakhami & Mohammed Alsahli

  3. Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Abdu Gumaei

  4. National Research Council of Italy, Institute for High Performance Computing and Networking, Calabria, Italy

    Antonio Guerrieri & Giancarlo Fortino

  5. Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, Calabria, Italy

    Giancarlo Fortino

Authors
  1. Mabrook Al-Rakhami
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  2. Abdu Gumaei
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  3. Mohammed Alsahli
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  4. Mohammad Mehedi Hassan
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  5. Atif Alamri
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  6. Antonio Guerrieri
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  7. Giancarlo Fortino
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Correspondence to Mabrook Al-Rakhami.

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This article is part of the Topical Collection: Special Issue on Smart Computing and Cyber Technology for Cyberization

Guest Editors: Xiaokang Zhou, Flavia C. Delicato, Kevin Wang, and Runhe Huang

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Al-Rakhami, M., Gumaei, A., Alsahli, M. et al. A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web 23, 1341–1360 (2020). https://doi.org/10.1007/s11280-019-00692-y

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  • DOI: https://doi.org/10.1007/s11280-019-00692-y

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