Research Article
Decoupling Offloading Decision and Resource Allocation via Deep Reinforcement Learning and Sequential Least Squares Programming
@INPROCEEDINGS{10.1007/978-3-030-67720-6_38, author={Zhihao Xuan and Guiyi Wei and Zhengwei Ni and Jifa Zhang}, title={Decoupling Offloading Decision and Resource Allocation via Deep Reinforcement Learning and Sequential Least Squares Programming}, proceedings={Communications and Networking. 15th EAI International Conference, ChinaCom 2020, Shanghai, China, November 20-21, 2020, Proceedings}, proceedings_a={CHINACOM}, year={2021}, month={2}, keywords={Edge computing Offloading Resource allocation Reinforcement learning}, doi={10.1007/978-3-030-67720-6_38} }- Zhihao Xuan
Guiyi Wei
Zhengwei Ni
Jifa Zhang
Year: 2021
Decoupling Offloading Decision and Resource Allocation via Deep Reinforcement Learning and Sequential Least Squares Programming
CHINACOM
Springer
DOI: 10.1007/978-3-030-67720-6_38
Abstract
Edge computing is to generate faster network service response and meet the basic needs of the industry in real-time business, application intelligence, security and privacy protection. This paper studies the mobile edge computing network, where the computing power of the edge server (ES) is limited, and multiple user equipment (UE) can offload the thinking to the ES in order to save energy consumption and computing delay. The ES needs to determine which UEs can upload its tasks and need to allocate computing resources for these UEs, so this problem is highly coupled and difficult to calculate. This paper proposes an algorithm based on deep reinforcement learning and Sequential Least Squares Programming (SLSQP), which decouples and solves the problem. Experiments show that the algorithm works well and can be dynamically adjusted according to environmental changes. The comparison with other algorithms also proves that the algorithm has better results and less time-consuming.
