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.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 61572440 and Grant 61502428, in part by the Zhejiang Provincial Natural Science Foundation of China under Grants LR17F010002 and LR16F010003, in part by the Young Talent Cultivation Project of Zhejiang Association for Science and Technology under Grant 2016YCGC011.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Huang, L., Feng, X., Qian, L., Wu, Y. (2018). Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Mobile Edge Computing. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_4
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DOI: https://doi.org/10.1007/978-3-030-00557-3_4
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