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Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings

Research Article

Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Mobile Edge Computing

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  • @INPROCEEDINGS{10.1007/978-3-030-00557-3_4,
        author={Liang Huang and Xu Feng and Liping Qian and Yuan Wu},
        title={Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Mobile Edge Computing},
        proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings},
        proceedings_a={MLICOM},
        year={2018},
        month={10},
        keywords={Mobile edge computing Deep reinforcement learning Task offloading Resource allocation Deep Q-learning},
        doi={10.1007/978-3-030-00557-3_4}
    }
    
  • Liang Huang
    Xu Feng
    Liping Qian
    Yuan Wu
    Year: 2018
    Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Mobile Edge Computing
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-00557-3_4
Liang Huang1,*, Xu Feng1,*, Liping Qian1,*, Yuan Wu1,*
  • 1: Zhejiang University of Technology
*Contact email: lianghuang@zjut.edu.cn, xfeng_zjut@163.com, lpqian@zjut.edu.cn, iewuy@zjut.edu.cn

Abstract

We consider a mobile edge computing system that every user has multiple tasks being offloaded to edge server via wireless networks. Our goal is to acquire a satisfactory task offloading and resource allocation decision for each user so as to minimize energy consumption and delay. In this paper, we propose a deep reinforcement learning-based approach to solve joint task offloading and resource allocation problems. Simulation results show that the proposed deep Q-learning-based algorithm can achieve near-optimal performance.

Keywords
Mobile edge computing Deep reinforcement learning Task offloading Resource allocation Deep Q-learning
Published
2018-10-12
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-00557-3_4
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