The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach
Abstract
The development of mobile devices with improving communication and perceptual capabilities has brought about a proliferation of numerous complex and computation-intensive mobile applications. Mobile devices with limited resources face more severe capacity constraints than ever before. As a new concept of network architecture and an extension of cloud computing, Mobile Edge Computing (MEC) seems to be a promising solution to meet this emerging challenge. However, MEC also has some limitations, such as the high cost of infrastructure deployment and maintenance, as well as the severe pressure that the complex and mutative edge computing environment brings to MEC servers. At this point, how to allocate computing resources and network resources rationally to satisfy the requirements of mobile devices under the changeable MEC conditions has become a great aporia. To combat this issue, we propose a smart, Deep Reinforcement Learning based Resource Allocation (DRLRA) scheme, which can allocate computing and network resources adaptively, reduce the average service time and balance the use of resources under varying MEC environment. Experimental results show that the proposed DRLRA performs better than the traditional OSPF algorithm in the mutative MEC conditions.
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Cites
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Human-level control through deep reinforcement learning
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A Survey on Mobile Edge Computing: The Communication Perspective
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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
Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading
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Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network
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Self-citationEdgeIoT: Mobile Edge Computing for the Internet of Things
Sun, X., & Ansari, N. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.1600492cm · Cited by 694
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Cited by
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
Lei, L., Tan, Y., Zheng, K., Liu, S., Zhang, K., & Shen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988367 · Cited by 298
A Survey of Incentive Mechanism Design for Federated Learning
Zhan, Y., Zhang, J., Hong, Z., Wu, L., Li, P., & Guo, S. (2021) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2021.3063517 · Cited by 284
Kai, C., Zhou, H., Yi, Y., & Huang, W. (2021) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.3018159 · Cited by 249
When Machine Learning Meets Privacy in 6G: A Survey
Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011561 · Cited by 211
Self-citationTask Offloading With Multi-Tier Computing Resources in Next Generation Wireless Networks
Wang, K., Jin, J., Yang, Y., Zhang, T., Nallanathan, A., Tellambura, C., & Jabbari, B. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3227102 · Cited by 95
Metadata
- Authors
- Wang, J., Zhao, L., Liu, J., & Kato, N.
- Venue
- IEEE Transactions on Emerging Topics in Computing
- Published
- 2021-07-01
- Volume
- 9
- Issue
- 3
- Pages
- 1529-1541
- ISSN
- 2168-6750 (ISSN-L)
- 2376-4562
- OpenAlex
- W2920054549
- Cited by
- 451
- References
- 35
- FWCI
- 32.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, J., Zhao, L., Liu, J., & Kato, N. (2021). Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach. IEEE Transactions on Emerging Topics in Computing. https://doi.org/10.1109/tetc.2019.2902661
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