Cooperative service caching and peer offloading in Internet of vehicles based on multi-agent meta-reinforcement learning

被引：0
|
作者：
Ning Z. [1 ,2 ]
Zhang K. [2 ]
Wang X. [1 ]
Guo L. [1 ]
机构：
[1] School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing
[2] School of Software, Dalian University of Technology, Dalian
来源：
Tongxin Xuebao/Journal on Communications | 2021年 / 42卷 / 06期
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
Cooperative offloading; Edge service caching; Internet of vehicles; Meta-reinforcement learning;
D O I：
10.11959/j.issn.1000-436x.2021104
中图分类号：
学科分类号：
摘要：
In order to reduce computation complexity, a two-layer mutli-RSU (road side unit) service caching and peer offloading algorithm (MPO) was proposed to decouple the optimization problem. In the designed MPO, the outer layer utilized multi-agent meta-reinforcement learning, which established long short-term memory (LSTM) network as the meta-agent to balance decisions of local agents and accelerate learning progress. The inner layer utilized lagrange multiplier method to achieve optimal decision for RSU peer offloading. Experimental results based on real traffic data in Hangzhou demonstrate that the proposed method outperforms other methods and remains robust under large-scale workloads. © 2021, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：118 / 130
页数：12
相关论文
共 22 条
  • [1] NING Z L, DONG P R, WANG X J, Et al., Mobile edge computing enabled 5G health monitoring for Internet of medical things: a decentralized game theoretic approach, IEEE Journal on Selected Areas in Communications, 39, 2, pp. 463-478, (2021)
  • [2] XIE R C, LIAN X F, JIA Q M, Et al., Survey on computation offloading in mobile edge computing, Journal on Communications, 39, 11, pp. 138-155, (2018)
  • [3] ZHUANG W H, YE Q, LYU F, Et al., SDN/NFV-empowered future IoV with enhanced communication, computing, and caching, Proceedings of the IEEE, 108, 2, pp. 274-291, (2020)
  • [4] WANG X J, NING Z L, GUO S., Multi-agent imitation learning for pervasive edge computing: a decentralized computation offloading algorithm, IEEE Transactions on Parallel and Distributed Systems, 32, 2, pp. 411-425, (2021)
  • [5] LIU L, CHEN C, FENG J, Et al., Joint intelligent optimization of task offloading and service caching for vehicular edge computing, Journal on Communications, 42, 1, pp. 18-26, (2021)
  • [6] CHEN Z Q, DUAN L Y, WANG S Q, Et al., Toward knowledge as a service over networks: a deep learning model communication paradigm, IEEE Journal on Selected Areas in Communications, 37, 6, pp. 1349-1363, (2019)
  • [7] ZHANG Y, ZHANG K, CAO J Y., Internet of vehicles empowered by edge intelligence, Chinese Journal on Internet of Things, 2, 4, pp. 40-48, (2018)
  • [8] WANG X J, NING Z L, GUO S, Et al., Imitation learning enabled task scheduling for online vehicular edge computing, IEEE Transactions on Mobile Computing, 99, (2020)
  • [9] NING Z L, DONG P R, WANG X J, Et al., When deep reinforcement learning meets 5G-enabled vehicular networks: a distributed offloading framework for traffic big data, IEEE Transactions on Industrial Informatics, 16, 2, pp. 1352-1361, (2020)
  • [10] NING Z L, DONG P R, WANG X J, Et al., Deep reinforcement learning for vehicular edge computing: an intelligent offloading system, ACM Transactions on Intelligent Systems and Technology, 10, 6, (2019)
123