The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Multiobjective Optimization for Computation Offloading in Fog Computing
Abstract
Fog computing system is an emergent architecture for providing computing, storage, control, and networking capabilities for realizing Internet of Things. In the fog computing system, the mobile devices (MDs) can offload its data or computational expensive tasks to the fog node within its proximity, instead of distant cloud. Although offloading can reduce energy consumption at the MDs, it may also incur a larger execution delay including transmission time between the MDs and the fog/cloud servers, and waiting and execution time at the servers. Therefore, how to balance the energy consumption and delay performance is of research importance. Moreover, based on the energy consumption and delay, how to design a cost model for the MDs to enjoy the fog and cloud services is also important. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay, and payment cost of offloading processes in a fog computing system. Specifically, three queuing models are applied, respectively, to the MD, fog, and cloud centers, and the data rate and power consumption of the wireless link are explicitly considered. Based on the theoretical analysis, a multiobjective optimization problem is formulated with a joint objective to minimize the energy consumption, execution delay, and payment cost by finding the optimal offloading probability and transmit power for each MD. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over several existed schemes are observed.
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Cites
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Efficient multi-user computation offloading for mobile-edge cloud computing
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Fog and IoT: An Overview of Research Opportunities
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Fog Computing: Helping the Internet of Things Realize Its Potential
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Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
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Optimal Workload Allocation in Fog-Cloud Computing Towards Balanced Delay and Power Consumption
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Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks
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Cited by
Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems
Zhou, F., Wu, Y., Hu, R. Q., & Qian, Y. (2018) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2018.2864426 · Cited by 851
Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing
Li, L., Ota, K., & Dong, M. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2842821 · Cited by 466
Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks
Yang, Z., Pan, C., Wang, K., & Shikh-Bahaei, M. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2927313 · Cited by 451
Resource management approaches in fog computing: a comprehensive review
Ghobaei-Arani, M., Souri, A., & Rahmanian, A. A. (2020) · Journal of Grid Computing · doi:10.1007/s10723-019-09491-1 · Cited by 365
Blockchain and IoT-based cognitive edge framework for sharing economy services in a smart city
Rahman, M. A., Rashid, M. M., Hossain, M. S., Hassanain, E., Alhamid, M. F., & Guizani, M. (2019) · IEEE Access · doi:10.1109/access.2019.2896065 · Cited by 333
Metadata
- Authors
- Liu, L., Chang, Z., Guo, X., Mao, S., & Ristaniemi, T.
- Venue
- IEEE Internet of Things Journal
- Published
- 2018-02
- Volume
- 5
- Issue
- 1
- Pages
- 283-294
- ISSN
- 2327-4662 (ISSN-L)
- OpenAlex
- W2773498880
- Cited by
- 512
- References
- 23
- FWCI
- 35.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, L., Chang, Z., Guo, X., Mao, S., & Ristaniemi, T. (2018). Multiobjective Optimization for Computation Offloading in Fog Computing. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2017.2780236
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