The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee
Abstract
Cooperation between the fog and the cloud in mobile cloud computing environments could offer improved offloading services to smart mobile user equipment (UE) with computation intensive tasks. In this paper, we tackle the computation offloading problem in a mixed fog/cloud system by jointly optimizing the offloading decisions and the allocation of computation resource, transmit power, and radio bandwidth while guaranteeing user fairness and maximum tolerable delay. This optimization problem is formulated to minimize the maximal weighted cost of delay and energy consumption (EC) among all UEs, which is a mixed-integer non-linear programming problem. Due to the NP-hardness of the problem, we propose a low-complexity suboptimal algorithm to solve it, where the offloading decisions are obtained via semidefinite relaxation and randomization, and the resource allocation is obtained using fractional programming theory and Lagrangian dual decomposition. Simulation results are presented to verify the convergence performance of our proposed algorithms and their achieved fairness among UEs, and the performance gains in terms of delay, EC, and the number of beneficial UEs over existing algorithms.
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Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733
The case for vm-based cloudlets in mobile computing
Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009) · IEEE Pervasive Computing · doi:10.1109/mprv.2009.82 · Cited by 3,660
On Nonlinear Fractional Programming
Dinkelbach, W. (1967) · Management Science · doi:10.1287/mnsc.13.7.492 · Cited by 3,139
MAUI: making smartphones last longer with code offload
Cuervo, E., Balasubramanian, A., Cho, D.-K., Wolman, A., Saroiu, S., Chandra, R., & Bahl, P. (2010) · Proceedings of the 8th international conference on Mobile systems, applications, and services · doi:10.1145/1814433.1814441 · Cited by 2,281
Dynamic computation offloading for mobile-edge computing with energy harvesting devices
Mao, Y., Zhang, J., & Letaief, K. B. (2016) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2016.2611964 · Cited by 1,705
Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling
Wang, Y., Sheng, M., Wang, X., Wang, L., & Li, J. (2016) · IEEE Transactions on Communications · doi:10.1109/tcomm.2016.2599530 · Cited by 1,002
Fog Computing: A Taxonomy, Survey and Future Directions
Mahmud, R., Kotagiri, R., & Buyya, R. (2018) · Internet of Things · doi:10.1007/978-981-10-5861-5_5 · Cited by 921
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
Sardellitti, S., Scutari, G., & Barbarossa, S. (2015) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2015.2448520 · Cited by 891
Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges
Tran, T. X., Hajisami, A., Pandey, P., & Pompili, D. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600863 · Cited by 874
Cited by
Resource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588
“Generally, the return process has been neglected in many works since the processing result is usually very tiny [67]–[69].”
BackgroundJourney from cloud of things to fog of things: survey, new trends, and research directions
Chakraborty, A., Kumar, M., Chaurasia, N., & Gill, S. S. (2023) · Software: Practice and Experience · doi:10.1002/spe.3157 · Cited by 31
Metadata
- Authors
- Du, J., Zhao, L., Feng, J., & Chu, X.
- Venue
- IEEE Transactions on Communications
- Published
- 2018-04
- Volume
- 66
- Issue
- 4
- Pages
- 1594-1608
- ISSN
- 0090-6778 (ISSN-L)
- OpenAlex
- W2777781531
- Cited by
- 508
- References
- 28
- FWCI
- 37.24
- Type
- journal article
- Language
- en
- Open access
- closed
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- Du, J., Zhao, L., Feng, J., & Chu, X. (2018). Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee. IEEE Transactions on Communications. https://doi.org/10.1109/tcomm.2017.2787700
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