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Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks
Abstract
Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.
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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
“Finally, effective optimization algorithms are designed to maximize the utility [91], [108], [249], [252], [253].”
MethodReliable Computation Offloading for Edge-Computing-Enabled Software-Defined IoV
Hou, X., Ren, Z., Wang, J., Cheng, W., Ren, Y., Chen, K.-C., & Zhang, H. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2982292 · Cited by 334
Computation offloading in mobile edge computing networks: A survey
Feng, C., Han, P., Zhang, X., Yang, B., Liu, Y., & Guo, L. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103366 · Cited by 284
Blockchain for Edge of Things: Applications, Opportunities, and Challenges
Gadekallu, T. R., Pham, Q.-V., Nguyen, D. C., Maddikunta, P. K. R., Deepa, N., Prabadevi, B., Pathirana, P. N., Zhao, J., & Hwang, W.-J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3119639 · Cited by 217
Self-citationSun, Z., Sun, G., Liu, Y., Wang, J., & Cao, D. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3239339 · Cited by 213
Metadata
- Authors
- Zhao, J., Li, Q., Gong, Y., & Zhang, K.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2019-08
- Volume
- 68
- Issue
- 8
- Pages
- 7944-7956
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2954039338
- Cited by
- 806
- References
- 0
- FWCI
- 59.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Funders
- Fundamental Research Funds for the Central Universities (2018YJS008)
- National Natural Science Foundation of China (61661021)
- Natural Science Foundation of Beijing Municipality (L182018)
- Southeast University (2017D14)
- Science and Technology Program of Jiangxi Province (20172BCB22016, 20171BBE50057)
- Shenzhen Science and Technology Program (JCYJ20170817110410346)
- Peng Cheng Laboratory (PCL2018KP002)
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