- Title
- Green demand aware fog computing : a prediction-based dynamic resource provisioning approach
- Creator
- Khadhijah, Pg; Newaz, S.; Rahman, Fatin; Lee, Gyu; Karmakar, Gour; Au, Thien-Wan
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/187248
- Identifier
- vital:17044
- Identifier
-
https://doi.org/10.3390/electronics11040608
- Identifier
- ISBN:2079-9292 (ISSN)
- Abstract
- Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Publisher
- MDPI
- Relation
- Electronics (Switzerland) Vol. 11, no. 4 (2022), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2022 by the authors
- Rights
- Open Access
- Subject
- 4009 Electronics, sensors and digital hardware; Broker; Computational demand; Energy efficiency; Fog computing; Prediction
- Full Text
- Reviewed
- Funder
- This work was supported by Universiti Teknologi Brunei (UTB), Brunei Darussalam.
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