University of Oulu

S. Yu, X. Chen, L. Yang, D. Wu, M. Bennis and J. Zhang, "Intelligent Edge: Leveraging Deep Imitation Learning for Mobile Edge Computation Offloading," in IEEE Wireless Communications, vol. 27, no. 1, pp. 92-99, February 2020, doi: 10.1109/MWC.001.1900232

Intelligent edge : leveraging deep imitation learning for mobile edge computation offloading

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Author: Yu, Shuai1; Chen, Xu1; Yang, Lei2;
Organizations: 1Sun Yat-sen University
2University of Nevada
3University of Oulu
4Arizona State University
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-06-08


In this work, we propose a new deep imitation learning (DIL)-driven edge-cloud computation offloading framework for MEC networks. A key objective for the framework is to minimize the offloading cost in time-varying network environments through optimal behavioral cloning. Specifically, we first introduce our computation offloading model for MEC in detail. Then we make fine-grained offloading decisions for a mobile device, and the problem is formulated as a multi-label classification problem, with local execution cost and remote network resource usage consideration. To minimize the offloading cost, we train our decision making engine by leveraging the deep imitation learning method, and further evaluate its performance through an extensive numerical study. Simulation results show that our proposal outperforms other benchmark policies in offloading accuracy and offloading cost reduction. At last, we discuss the directions and advantages of applying deep learning methods to multiple MEC research areas, including edge data analytics, dynamic resource allocation, security, and privacy, respectively.

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Series: IEEE wireless communications
ISSN: 1536-1284
ISSN-E: 1558-0687
ISSN-L: 1536-1284
Volume: 27
Issue: 1
Pages: 92 - 99
DOI: 10.1109/MWC.001.1900232
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the National Key Research and Development Program of China (No. 2017YFB1001703); the National Science Foundation of China (No. U1711265, No. 61972432); the Fundamental Research Funds for the Central Universities (No. 17lgjc40, 19LGZD37); the Program for Basic and Applied Basic Research Fund of Guangdong (No. 2019A1515010030); the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (No. 2017ZT07X355); the Pearl River Talent Recruitment Program (No. 2017GC010465); the U.S. National Science Foundation (IIS-1838024); and the Guangdong Special Support Program (2017TX04X148).
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