Joint intelligent optimization of task offloading and service caching for vehicular edge computing

Lei LIU; Chen CHEN; Jie FENG; Qingqi PEI; Ci HE; Zhibin DOU

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

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Joint intelligent optimization of task offloading and service caching for vehicular edge computing

Papers | 更新时间：2024-06-05

- Joint intelligent optimization of task offloading and service caching for vehicular edge computing
- Journal on Communications Vol. 42, Issue 1, Pages: 18-26(2021)
- 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：
- 基金信息：
  
  The National Key Research and Development Program of China(2020YFB1807500);The National Natural Science Foundation of China(62072360);The National Natural Science Foundation of China(61571338);The National Natural Science Foundation of China(61672131);The National Natural Science Foundation of China(62001357);The National Natural Science Foundation of China(61901367);The Key Laboratory of Embedded System and Service Computing (Tongji University)(ESSCKF2019-05);Ministry of Education, Xi’an Science and Technology Plan(20RGZN0005);The Xi’an Key Laboratory of Mobile Edge Computing and Security(201805052-ZD3CG36)
- DOI：10.11959/j.issn.1000-436x.2021017
  CLC： TN92
- Online First：2021-01，
  
  Published：25 January 2021
- 稿件说明：
移动端阅览
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：

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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