Deep learning-based computation offloading with energy and performance optimization

EURASIP Journal on Wireless Communications and Networking

Houpeng Wang¹  Suzhi Cao¹  Yongsheng Gong¹  Congmin Lv¹  Lei Yan¹ 

[1] Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences;

关键词: Computation offloading;    Deep learning;    Mobile edge computing;    Energy and performance optimization;   

DOI  :  10.1186/s13638-020-01678-5

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown