Research Article
Deep Reinforcement Learning Based Computation Offloading for Mobility-Aware Edge Computing
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@INPROCEEDINGS{10.1007/978-3-030-41114-5_5, author={Minyan Shi and Rui Wang and Erwu Liu and Zhixin Xu and Longwei Wang}, title={Deep Reinforcement Learning Based Computation Offloading for Mobility-Aware Edge Computing}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part I}, proceedings_a={CHINACOM}, year={2020}, month={2}, keywords={Mobile Edge Computing Computation offloading Mobility Deep reinforcement learning}, doi={10.1007/978-3-030-41114-5_5} }- Minyan Shi
Rui Wang
Erwu Liu
Zhixin Xu
Longwei Wang
Year: 2020
Deep Reinforcement Learning Based Computation Offloading for Mobility-Aware Edge Computing
CHINACOM
Springer
DOI: 10.1007/978-3-030-41114-5_5
Abstract
Mobile Edge Computing (MEC) has become the most likely network architecture to solve the problems of mobile devices in terms of resource storage, computing performance and energy efficiency. In this paper, we first model the MEC system with the exploitation of mobility prediction. Considering the user’s mobility, the deadline constraint and the limited resources in MEC servers, we propose a deep reinforcement learning approach named deep deterministic policy gradient (DDPG) to learn the power allocation policies for MEC servers users. Then, the aim of the policy is to minimize the overall cost of the MEC system. Finally, simulation results are illustrated that our proposed algorithm achieves performance gains.
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