跳到主要导航 跳到搜索 跳到主要内容

Deep Reinforcement Learning-Based Joint Task Offloading in Cloud-Edge-End Cooperation Environments

  • Chao Fang*
  • , Hang Xu
  • , Yulong Bai
  • , Tianyi Zhang
  • , Yihui Yang
  • , Zhaoming Hu
  • *此作品的通讯作者
  • Purple Mountain Laboratories
  • Beijing University of Technology

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

To meet the service requirements for delay-sensitive tasks and improve content delivery, in this paper, a deep reinforcement learning (DRL)-based joint offloading scheme of computing and content tasks in cloud-edge-end cooperation networks is proposed. We use a minimum delay model to describe the task offloading problem, where the same requests can be aggregated to lighten the server load and the in-network caching is considered. We design a new DRL algorithm to achieve intelligent task offloading by making cooperative caching and routing decisions. The simulation results show that the proposed model has obvious advantage significantly better than the existing models in the cloudedge-end cooperation environments.

源语言英语
主期刊名Proceedings - 2022 2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022
出版商Institute of Electrical and Electronics Engineers Inc.
524-530
页数7
ISBN(电子版)9781665454766
DOI
出版状态已出版 - 2022
活动2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022 - Wuhan, 中国
期限: 19 8月 202221 8月 2022

出版系列

姓名Proceedings - 2022 2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022

会议

会议2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022
国家/地区中国
Wuhan
时期19/08/2221/08/22

指纹

探究 'Deep Reinforcement Learning-Based Joint Task Offloading in Cloud-Edge-End Cooperation Environments' 的科研主题。它们共同构成独一无二的指纹。

引用此