The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling
Abstract
In this paper, we propose an optimization framework of offloading from a single mobile device (MD) to multiple edge devices. We aim to minimize both total tasks' execution latency and the MD's energy consumption by jointly optimizing the task allocation decision and the MD's central process unit (CPU) frequency. This paper considers two cases for the MD, i.e., fixed CPU frequency and elastic CPU frequency. Since these problems are NP-hard, we propose a linear relaxation-based approach and a semidefinite relaxation (SDR)-based approach for the fixed CPU frequency case, and an exhaustive search-based approach and an SDR-based approach for the elastic CPU frequency case. Our simulation results show that the SDR-based algorithms achieve near optimal performance. Performance improvement can be obtained with the proposed scheme in terms of energy consumption and tasks' execution latency when multiple edge devices and elastic CPU frequency are considered. Finally, we show that the MD's flexible CPU range can have an impact on the task allocation.
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A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607
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
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
Cited by
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607
Federated Learning via Over-the-Air Computation
Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2961673 · Cited by 1,084
“This emerging technique is termed as edge ML [4], which is supported by mobile edge computing [5], [6] via pushing the cloud computing services to the network edges.”
MethodScheduling Policies for Federated Learning in Wireless Networks
Yang, H. H., Liu, Z., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2944169 · Cited by 640
Self-citationLearning-Based Computation Offloading for IoT Devices With Energy Harvesting
Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2890685 · Cited by 560
Du, J., Zhao, L., Feng, J., & Chu, X. (2018) · IEEE Transactions on Communications · doi:10.1109/tcomm.2017.2787700 · Cited by 508
Metadata
- Authors
- Thinh, T. Q., Tang, J., La, Q. D., & Quek, T. Q. S.
- Venue
- IEEE Transactions on Communications
- Published
- 2017
- Pages
- 1-1
- ISSN
- 0090-6778 (ISSN-L)
- OpenAlex
- W2608659040
- Cited by
- 866
- References
- 3
- FWCI
- 65.67
- Type
- journal article
- Language
- en
- Open access
- closed
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