The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning
Open accessAbstract
Multi-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency. A fundamental problem in MEC is how to efficiently offload heterogeneous tasks of mobile applications from user equipment (UE) to MEC hosts. Recently, many deep reinforcement learning (DRL)-based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts. However, these methods have weak adaptability to new environments because they have low sample efficiency and need full retraining to learn updated policies for new environments. To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small number of gradient updates and samples. We model mobile applications as Directed Acyclic Graphs (DAGs) and the offloading policy by a custom sequence-to-sequence (seq2seq) neural network. To efficiently train the seq2seq network, we propose a method that synergizes the first order approximation and clipped surrogate objective. The experimental results demonstrate that this new offloading method can reduce the latency by up to 25 percent compared to three baselines while being able to adapt fast to new environments.
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Cites
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Cited by
Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT
Zhou, X., Xu, X., Liang, W., Zeng, Z., & Yan, Z. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3077449 · Cited by 332
Chai, F., Zhang, Q., Yao, H., Xin, X., Gao, R., & Guizani, M. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3238771 · Cited by 331
A Survey on Task Offloading in Multi-access Edge Computing
Islam, A., Debnath, A., Ghose, M., & Chakraborty, S. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102225 · Cited by 271
Wang, X., Garg, S., Lin, H., Hu, J., Kaddoum, G., Jalil Piran, M., & Hossain, M. S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3074382 · Cited by 268
Self-citationGoudarzi, M., Palaniswami, M., & Buyya, R. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3123165 · Cited by 155
Metadata
- Authors
- Wang, J., Hu, J., Min, G., Zomaya, A. Y., & Georgalas, N.
- Venue
- IEEE Transactions on Parallel and Distributed Systems
- Published
- 2021-01-01
- Volume
- 32
- Issue
- 1
- Pages
- 242-253
- ISSN
- 1045-9219 (ISSN-L)
- 1558-2183
- 2161-9883
- OpenAlex
- W3047538493
- Cited by
- 414
- References
- 37
- FWCI
- 22.51
- Type
- journal article
- Language
- en
- Open access
- green
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