文献类型：期刊文献

英文题名：Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning

作者：Lu, Haifeng[1];Gu, Chunhua[1];Luo, Fei[1];Ding, Weichao[1];Liu, Xinping[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：102

起止页码：847

外文期刊名：FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

收录：;EI(收录号：20193907473089);WOS:【SCI-EXPANDED(收录号:WOS:000501936300069)】；

基金：The work was supported by the National Natural Science Foundation (NSF) under grants (No.61472139)

语种：英文

外文关键词：Mobile edge computing; Task offloading; Deep reinforcement learning; LSTM network; Candidate network

摘要：With the maturity of 5G technology and the popularity of intelligent terminal devices, the traditional cloud computing service model cannot deal with the explosive growth of business data quickly. Therefore, the purpose of mobile edge computing (MEC) is to effectively solve problems such as latency and network load. In this paper, deep reinforcement learning (DRL) is first proposed to solve the offloading problem of multiple service nodes for the cluster and multiple dependencies for mobile tasks in large-scale heterogeneous MEC. Then the paper uses the LSTM network layer and the candidate network set to improve the DQN algorithm in combination with the actual environment of the MEC. Finally, the task offloading problem is simulated by using iFogSim and Google Cluster Trace. The simulation results show that the offloading strategy based on the improved IDRQN algorithm has better performance in energy consumption, load balancing, latency and average execution time than other algorithms. (C) 2019 Elsevier B.V. All rights reserved.