车载边缘计算中任务卸载和服务缓存的联合智能优化

刘雷; 陈晨; 冯杰; 裴庆祺; 何辞; 窦志斌

doi:10.11959/j.issn.1000-436x.2021017

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车载边缘计算中任务卸载和服务缓存的联合智能优化

学术论文 | 更新时间：2024-06-05

- 车载边缘计算中任务卸载和服务缓存的联合智能优化
- Joint intelligent optimization of task offloading and service caching for vehicular edge computing
- 通信学报 2021年42卷第1期页码：18-26
- 作者机构：
  
  1. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071
  2. 中国电子科技集团公司第54研究所，河北石家庄 050081
- 作者简介：
  
  [ "刘雷（1987- ），男，河南南阳人，博士，西安电子科技大学讲师，主要研究方向为车载边缘计算。" ]
  [ "陈晨（1977- ），男，陕西西安人，博士，西安电子科技大学教授、博士生导师，主要研究方向为智能交通。" ]
  [ "冯杰（1987- ），女，陕西咸阳人，博士，西安电子科技大学副教授，主要研究方向为边缘智能。" ]
  [ "裴庆祺（1975- ），男，广西百色人，西安电子科技大学教授、博士生导师，主要研究方向为区块链。" ]
  [ "何辞（1983- ），女，湖北武汉人，中国电子科技集团公司第 54 研究所高级工程师，主要研究方向为空天信息网络。" ]
  [ "窦志斌（1980- ），男，山西阳泉人，中国电子科技集团公司第54研究所高级工程师，主要研究方向为空天信息网络。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2020YFB1807500);国家自然科学基金资助项目(62072360);国家自然科学基金资助项目(61571338);国家自然科学基金资助项目(61672131);国家自然科学基金资助项目(62001357);国家自然科学基金资助项目(61901367);嵌入式系统与服务计算教育部重点实验室开放基金资助项目（同济大学）(ESSCKF2019-05);西安市科技计划基金资助项目(20RGZN0005);西安市重点实验室建设基金资助项目(201805052-ZD3CG36)
- DOI：10.11959/j.issn.1000-436x.2021017
  中图分类号： TN92
- 网络首发：2021-01，
  
  纸质出版：2021-01-25
- 稿件说明：
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刘雷, 陈晨, 冯杰, 等. 车载边缘计算中任务卸载和服务缓存的联合智能优化[J]. 通信学报, 2021,42(1):18-26.

Lei LIU, Chen CHEN, Jie FENG, et al. Joint intelligent optimization of task offloading and service caching for vehicular edge computing[J]. Journal on Communications, 2021, 42(1): 18-26.
刘雷, 陈晨, 冯杰, 等. 车载边缘计算中任务卸载和服务缓存的联合智能优化[J]. 通信学报, 2021,42(1):18-26. DOI： 10.11959/j.issn.1000-436x.2021017.

Lei LIU, Chen CHEN, Jie FENG, et al. Joint intelligent optimization of task offloading and service caching for vehicular edge computing[J]. Journal on Communications, 2021, 42(1): 18-26. DOI： 10.11959/j.issn.1000-436x.2021017.

摘要

针对车载环境下有限的网络资源和大量用户需求之间的矛盾，提出了智能驱动的车载边缘计算网络架构，以实现网络资源的全面协同和智能管理。基于该架构，设计了任务卸载和服务缓存的联合优化机制，对用户任务卸载以及计算和缓存资源的调度进行了建模。鉴于车载网络的动态、随机和时变的特性，利用异步分布式强化学习算法，给出了最优的卸载决策和资源管理方案。实验结果表明，与其他算法相比，所提算法取得了明显的性能提升。

Abstract

Given the contradiction between limited network resources and massive user demands in Internet of vehicles

an intelligent vehicular edge computing network architecture was proposed to achieve the comprehensive cooperation and intelligent management of network resources.Based on this architecture

a joint optimization scheme of task offloading and service caching was furtherly devised

which formulated an optimization problem about how to offload tasks and allocate computation and cache resources.In view of the dynamics

randomness and time variation of vehicular networks

an asynchronous distributed reinforcement learning algorithm was employed to obtain the optimal task offloading and resource management policy.Simulation results demonstrate that the proposed algorithm achieves significant performance improvement in comparison with the other schemes.

关键词

Keywords

references

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