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Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing
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DOI：10.1109/LCOMM.2022.3159742.png)
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摘要
En 中文
In this letter, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading. Although the conventional branch and bound (BnB) approach can be applied to solve this problem, a huge burden of computational complexity arises which limits the application of BnB. To address this issue, we propose an intelligent BnB (IBnB) approach which applies deep learning (DL) to learn the pruning strategy of the BnB approach. By using this learning scheme, the structure of the BnB approach ensures near-optimal performance and meanwhile DL-based pruning strategy significantly reduces the complexity. Numerical results verify that the proposed IBnB approach achieves optimal performance with complexity reduced by over 80%.
Keyword:
Task analysis
Complexity theory
Energy consumption
Training
Optimization
Deep learning
Search problems
Mobile edge computing (MEC)
branch and brand (BnB)
offloading assignment
deep learning (DL)
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks用于无线供电的移动边缘计算网络中在线计算卸载的深度强化学习
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