Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing

• 作者: Da Guo FRichard Yu Yifei Wei Zhaoying Wang
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: MOBILE EDGE COMPUTING COMPUTATION OFFLOADING resource allocation DEEP reinforcement learning
• 年，卷（期）: 2019,（4）
• 页码范围: 89-104
• 页数: 16页
• 分类号: TN9

摘要

To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia recently.This paper focuses on mobile users’computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy.Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown,we use the modelfree reinforcement learning(RL)framework to formulate and tackle the computation offloading problem.Each mobile user learns through interactions with the environment and the estimate of its performance in the form of value function,then it chooses the overhead-aware optimal computation offloading action(local computing or edge computing)based on its state.The state spaces are high-dimensional in our work and value function is unrealistic to estimate.Consequently,we use deep reinforcement learning algorithm,which combines RL method Q-learning with the deep neural network(DNN)to approximate the value functions for complicated control applications,and the optimal policy will be obtained when the value function reaches convergence.Simulation results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.