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Towards an Elastic Fog Computing Framework for IoT Big Data Analytics Applications

Pham, Manh Linh and Nguyen, Truong Thang and Hoang, Tien Quang (2021) Towards an Elastic Fog Computing Framework for IoT Big Data Analytics Applications. Wireless Communications and Mobile Computing, 2021 (383364). pp. 1-16. ISSN 1530-8677

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Abstract

IoT applications have been being moved to the cloud during the last decade in order to reduce operating costs and provide more scalable services to users. However, IoT latency-sensitive big data streaming systems (e.g., smart home application) is not suitable with the cloud and needs another model to fit in. Fog computing, aiming at bringing computation, communication, and storage resources from “cloud to ground” closest to smart end-devices, seems to be a complementary appropriate proposal for such type of application. Although there are various research efforts and solutions for deploying and conducting elasticity of IoT big data analytics applications on the cloud, similar work on fog computing is not many. This article firstly introduces AutoFog, a fog-computing framework, which provides holistic deployment and an elasticity solution for fog-based IoT big data analytics applications including a novel mechanism for elasticity provision. Secondly, the article also points out requirements that a framework of IoT big data analytics application on fog environment should support. Finally, through a realistic smart home use case, extensive experiments were conducted to validate typical aspects of our proposed framework.

Item Type: Article
Uncontrolled Keywords: fog computing, Internet of Things, elasticity, big data analytics, smart home
Subjects: Information Technology (IT)
Scopus-indexed journals
ISI-indexed journals
Divisions: Center of Multidisciplinary Integrated Technologies for Field Monitoring (FIMO)
Faculty of Information Technology (FIT)
Depositing User: Dr. Mạnh Linh Phạm
Date Deposited: 15 Sep 2021 02:50
Last Modified: 15 Sep 2021 02:50
URI: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/4605

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