Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81747
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Title: Energy consumption optimization with a delay threshold in cloud-fog cooperation computing
Authors: Li, GS 
Yan, JH
Chen, L
Wu, JH
Lin, QY
Zhang, Y
Issue Date: 2019
Source: IEEE access, 30 Oct. 2019, v. 7, p. 159688-159697
Abstract: With the rapid development of the Internet of Things (IoT), the number of mobile terminal devices is increasing. Massive data are generated by mobile terminal devices, resulting in high delay and high energy consumption. In most cases, however, a low delay means high energy consumption. To balance energy consumption and delay, we adopt a tradeoff strategy that can realize optimal energy consumption with a delay threshold in this paper. First, we introduce the role of the delay threshold in reducing delay. Then, we describe the delay and energy consumption of the mobile terminal layer, fog node layer and cloud server layer with queue theory. Nonlinear programming is used to solve the energy optimization problem by calculating the optimal workload of each layer. We design a cloud-fog cooperation scheduling algorithm to reduce energy consumption. A task offloading algorithm is also designed to complete tasks when their nodes leave. The experimental results show that the energy consumption is reduced by approximately 22, while the delay is 12.5 less than the first come first served (FCFS) approach.
Keywords: Delays
Task analysis
Cloud computing
Energy consumption
Optimization
Edge computing
Servers
Energy consumption optimization
Fog computing
Tasks scheduling
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2950443
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
The following publication G. Li, J. Yan, L. Chen, J. Wu, Q. Lin and Y. Zhang, "Energy Consumption Optimization With a Delay Threshold in Cloud-Fog Cooperation Computing," in IEEE Access, vol. 7, pp. 159688-159697, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2950443
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