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Meta-Learning Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks
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DOI：10.1109/LCOMM.2020.3048075.png)
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Abstract
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Deep learning-based algorithms provide a promising solution to efficiently generate offloading decisions in mobile edge computing (MEC) networks. However, considering dynamic MEC devices or offloading tasks, most of them require large-scale training data and long training time to retrain the deep neural networks (DNNs). In this letter, we propose a MEta-Learning-based computation Offloading (MELO) algorithm for dynamic computation tasks in MEC networks. Specifically, it learns from historical MEC task scenarios and adapts to a new MEC task scenario with a few training samples. Numerical results show that the proposed algorithm can adapt to a new MEC task scenario and achieve 99% accuracy via 1-step fine-tuning using only 10 training samples.
Keywords:
Task analysis
Training
Servers
Delays
Heuristic algorithms
Computational modeling
Wireless communication
Mobile-edge computing
meta-learning
deep learning
computation offloading
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