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Improving fog computing performance via Fog-2-Fog collaboration
Future Generation Computer Systems
  • Mohammed Al-khafajiy, Liverpool John Moores University
  • Thar Baker, Liverpool John Moores University
  • Hilal Al-Libawy, University of Babylon
  • Zakaria Maamar, Zayed University
  • Moayad Aloqaily, Gnowit Inc.
  • Yaser Jararweh, Jordan University of Science and Technology
Document Type
Article
Publication Date
11-1-2019
Abstract

© 2019 Elsevier B.V. In the Internet of Things (IoT) era, a large volume of data is continuously emitted from a plethora of connected devices. The current network paradigm, which relies on centralised data centres (aka Cloud computing), has become inefficient to respond to IoT latency concern. To address this concern, fog computing allows data processing and storage “close” to IoT devices. However, fog is still not efficient due to spatial and temporal distribution of these devices, which leads to fog nodes’ unbalanced loads. This paper proposes a new Fog-2-Fog (F2F) collaboration model that promotes offloading incoming requests among fog nodes, according to their load and processing capabilities, via a novel load balancing known as Fog Resource manAgeMEnt Scheme (FRAMES). A formal mathematical model of F2F and FRAMES has been formulated, and a set of experiments has been carried out demonstrating the technical doability of F2F collaboration. The performance of the proposed fog load balancing model is compared to other load balancing models.

Publisher
Elsevier B.V.
Disciplines
Keywords
  • Fog computing,
  • Fog-2-Fog collaboration,
  • Internet-of-Things,
  • Offloading
Scopus ID
85065852869
Indexed in Scopus
Yes
Open Access
Yes
Open Access Type
Green: A manuscript of this publication is openly available in a repository
http://centaur.reading.ac.uk/88467/1/FGCS_final.pdf
Citation Information
Mohammed Al-khafajiy, Thar Baker, Hilal Al-Libawy, Zakaria Maamar, et al.. "Improving fog computing performance via Fog-2-Fog collaboration" Future Generation Computer Systems Vol. 100 (2019) p. 266 - 280 ISSN: <a href="https://v2.sherpa.ac.uk/id/publication/issn/0167-739X" target="_blank">0167-739X</a>
Available at: http://works.bepress.com/zakaria-maamar/110/