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Proactive Mobile Fog Computing Using Work Stealing: Data Processing at the Edge

Proactive Mobile Fog Computing Using Work Stealing: Data Processing at the Edge

SANDER SOO, Chii Chang, Seng W. Loke, Satish Narayana Srirama
Copyright: © 2018 |Pages: 20
ISBN13: 9781522556497|ISBN10: 1522556494|EISBN13: 9781522556503
DOI: 10.4018/978-1-5225-5649-7.ch014
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MLA

SOO, SANDER, et al. "Proactive Mobile Fog Computing Using Work Stealing: Data Processing at the Edge." Fog Computing: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2018, pp. 264-283. https://doi.org/10.4018/978-1-5225-5649-7.ch014

APA

SOO, S., Chang, C., Loke, S. W., & Srirama, S. N. (2018). Proactive Mobile Fog Computing Using Work Stealing: Data Processing at the Edge. In I. Management Association (Ed.), Fog Computing: Breakthroughs in Research and Practice (pp. 264-283). IGI Global. https://doi.org/10.4018/978-1-5225-5649-7.ch014

Chicago

SOO, SANDER, et al. "Proactive Mobile Fog Computing Using Work Stealing: Data Processing at the Edge." In Fog Computing: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 264-283. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5649-7.ch014

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Abstract

A common design of the Internet of Things (IoT) system relies on distant Cloud for management and processing, which faces the challenge of latency, especially when the application requires rapid response in the edge network. Therefore, researchers have proposed the Fog computing architecture, which distributes the computational data processing tasks to the edge network nodes located in the vicinity of data sources and end-users to reduce the latency. Although the Fog computing architecture is promising, it still faces a challenge in mobility when the tasks come from ubiquitous mobile applications in which the data sources are moving objects. In order to address the challenge, this article proposes a proactive Fog service provisioning framework, which hastens the task distribution process in Mobile Fog use cases. Further, the proposed framework provides an optimization scheme in task allocation based on runtime context information. A proof-of-concept prototype has been implemented and tested on real devices.

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