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Deep learning-based computation offloading with energy and performance optimization
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DOI：10.1186/s13638-020-01678-5.png)
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摘要
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With the benefit of partially or entirely offloading computations to a nearby server, mobile edge computing gives user equipment (UE) more powerful capability to run computationally intensive applications. However, a critical challenge emerged: how to select the optimal set of components to offload considering the UE performance as well as its battery usage constraints. In this paper, we propose a novel energy and performance efficient deep learning based offloading algorithm. The optimal offloading schemes of components based on remaining energy and its performance can be determined by our proposed algorithm. All of these considerations are modeled as a cost function; then, a deep learning network is trained to compute the solution by which the optimal offloading scheme can be determined. Experimental results show that the proposed method is superior to existing methods in terms of energy and performance constraints.
Keyword:
Computation offloading
Deep learning
Mobile edge computing
Energy and performance optimization
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1.9
论文数:
161
被引数:
3.6K
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