车载边缘计算中任务卸载和服务缓存的联合智能优化

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

通信学报 ›› 2021, Vol. 42 ›› Issue (1): 18-26.doi: 10.11959/j.issn.1000-436x.2021017

所属专题：边缘计算

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¹, 陈晨¹, 冯杰¹, 裴庆祺¹, 何辞², 窦志斌²

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

修回日期:2020-12-03 出版日期:2021-01-25 发布日期:2021-01-01
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。
基金资助:
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);陕西省重点研发计划基金资助项目(2019ZDLGY13-07);陕西省重点研发计划基金资助项目(2019ZDLGY13-04);陕西省重点研发计划基金资助项目(2020JQ-844);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)

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

Lei LIU¹, Chen CHEN¹, Jie FENG¹, Qingqi PEI¹, Ci HE², Zhibin DOU²

¹ State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
² The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China

Revised:2020-12-03 Online:2021-01-25 Published:2021-01-01
Supported by:
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)

摘要/Abstract

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。

关键词: 车联网, 边缘计算, 计算卸载, 服务缓存, 智能优化

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.

Key words: Internet of vehicles, edge computing, computation offloading, service caching, intelligent optimization

中图分类号:

TN92

刘雷, 陈晨, 冯杰, 裴庆祺, 何辞, 窦志斌. 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, Qingqi PEI, Ci HE, Zhibin DOU. Joint intelligent optimization of task offloading and service caching for vehicular edge computing[J]. Journal on Communications, 2021, 42(1): 18-26.

图/表 7

图1

图2

表1

图3

图4

图5

图6

参考文献 24

[1]	中国信息通信研究院. 车联网白皮书[R]. 北京:中国信息通信研究院, 2017.
	CAICT. White paper of Internet of vehicles[R]. Beijing:CAICT, 2017.
[2]	郭辉, 芮兰兰, 高志鹏 . 车辆边缘网络中基于多参数 MDP 模型的动态服务迁移策略[J]. 通信学报, 2020,41(1): 1-14.
	GUO H , RUI L L , GAO Z P . Dynamic service migration strategy based on MDP model with multiple parameter in vehicular edge network[J]. Journal on Communications, 2020,41(1): 1-14.
[3]	张海波, 王子心, 贺晓帆 . SDN 和 MEC 架构下 V2X 卸载与资源分配[J]. 通信学报, 2020,41(1): 114-124.
	ZHANG H B , WANG Z X , HE X F . V2X offloading and resource allocation under SDN and MEC architecture[J]. Journal on Communications, 2020,41(1): 114-124.
[4]	LIU L , CHEN C , QIU T ,et al. A data dissemination scheme based on clustering and probabilistic broadcasting in VANETs[J]. Vehicular Communications, 2018,13: 78-88.
[5]	CHEN C , WANG C , QIU T ,et al. Caching in vehicular named data networking:architecture,schemes and future directions[J]. IEEE Communications Surveys ＆ Tutorials, 2020,22(4): 2378-2407.
[6]	彭鑫, 邓清勇, 田淑娟 ,等. 多信道车联网 V2R/V2V 数据传输调度算法[J]. 通信学报, 2019,40(3): 92-101.
	PENG X , DENG Q Y , TIAN S J ,et al. Data dissemination scheduling algorithm for V2R/V2V in multi-channel VANET[J]. Journal on Communications, 2019,40(3): 92-101.
[7]	FENG J , YU F R , PEI Q ,et al. Cooperative computation offloading and resource allocation for blockchain-enabled mobile edge computing:a deep reinforcement learning approach[J]. IEEE Internet of Things Journal, 2020,7(7): 6214-6228.
[8]	FENG J , YU F R , PEI Q ,et al. Joint optimization of radio and computational resources allocation in blockchain-enabled mobile edge computing systems[J]. IEEE Transactions on Wireless Communications, 2020,19(6): 4321-4334.
[9]	LIU L , CHEN C , PEI Q ,et al. Vehicular edge computing and networking:a survey[J]. Mobile Networks and Applications, 2020,doi:10.1007/s11036-020-01624-1.
[10]	WANG T , TANG M B , CAO Y . Resource optimization protocol based on multicommunity model for intermittently connected mobile networks[J]. IEEE Systems Journal, 2019,14(1): 410-421.
[11]	WANG T , CAO Y , ZHOU Y ,et al. A survey on geographic routing protocols in delay/disruption tolerant networks[J]. International Journal of Distributed Sensor Networks, 2016,12(2): 1-12.
[12]	DAI Y , XU D , MAHARJAN S ,et al. Joint load balancing and offloading in vehicular edge computing and networks[J]. IEEE Internet of Things Journal, 2018,6(3): 4377-4387.
[13]	TAMANI N , BRIK B , LAGRAA N ,et al. On link stability metric and fuzzy quantification for service selection in mobile vehicular cloud[J]. IEEE Transactions on Intelligent Transportation Systems, 2019,21(5): 2050-2062.
[14]	YANG C , LIU Y , CHEN X ,et al. Efficient mobility-aware task offloading for vehicular edge computing networks[J]. IEEE Access, 2019,7: 26652-26664.
[15]	SORKHOH I , EBRAHIMI D , ATALLAH R ,et al. Workload scheduling in vehicular networks with edge cloud capabilities[J]. IEEE Transactions on Vehicular Technology, 2019,68(9): 8472-8486.
[16]	DU J , YU F R , CHU X ,et al. Computation offloading and resource allocation in vehicular networks based on dual-side cost minimization[J]. IEEE Transactions on Vehicular Technology, 2018,68(2): 1079-1092.
[17]	TAREQ M M K , SEMIARI O , SALEHI M A ,et al. Ultra reliable,low latency vehicle-to-infrastructure wireless communications with edge computing[C]// 2018 IEEE Global Communications Conference. Piscataway:IEEE Press, 2018: 1-7.
[18]	ZHANG K , MAO Y , LENG S ,et al. Mobile-edge computing for vehicular networks:a promising network paradigm with predictive off-loading[J]. IEEE Vehicular Technology Magazine, 2017,12(2): 36-44.
[19]	LIU Y , YU H , XIE S ,et al. Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks[J]. IEEE Transactions on Vehicular Technology, 2019,68(11): 11158-11168.
[20]	HE Y , ZHAO N , YIN H . Integrated networking,caching,and computing for connected vehicles:a deep reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2017,67(1): 44-55.
[21]	HU R Q . Mobility-aware edge caching and computing in vehicle networks:a deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2018,67(11): 10190-10203.
[22]	NING Z , ZHANG K , WANG X ,et al. Joint computing and caching in 5G-envisioned Internet of vehicles:a deep reinforcement learning-based traffic control system[J]. IEEE Transactions on Intelligent Transportation Systems, 2020,doi:10.1109/TITS.2020.2970276.
[23]	CHEN C , LIU L , QIU T ,et al. ASGR:an artificial spider-web-based geographic routing in heterogeneous vehicular networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2018,20(5): 1604-1620.
[24]	PENG H , LI D , ABBOUD K ,et al. Performance analysis of IEEE 802.11p DCF for multiplatooning communications with autonomous vehicles[J]. IEEE Transactions on Vehicular Technology, 2016,66(3): 2485-2498.

参数	分布
边缘服务器计算能力/（Gcycle·s^-1）	[2,6]
边缘服务器缓存能力/MB	200,1 000]
车辆数目/辆	25、35、45
车辆计算能力/（Mcycle·s^-1）	[100,500]
任务计算强度/（cycle·bit^-1）	297.62

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

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 24

相关文章 15

Metrics

推荐阅读 0

[1]	张海波, 兰凯, 陈舟, 王汝言, 邹灿, 王明月. 车联网中基于环的匿名高效批量认证与组密钥协商协议[J]. 通信学报, 2023, 44(6): 103-116.
[2]	张海波, 曹钰坤, 刘开健, 王汝言. 车联网中基于区块链的分布式信任管理方案[J]. 通信学报, 2023, 44(5): 148-157.
[3]	刘雪娇, 钟强, 夏莹杰. 基于双层分片区块链的车联网跨信任域高效认证方案[J]. 通信学报, 2023, 44(5): 213-223.
[4]	鲁蔚锋, 李宁, 徐佳, 徐力杰, 徐建. 多接入边缘计算中相关性任务的联合调度算法[J]. 通信学报, 2023, 44(4): 87-98.
[5]	苏新, 张桂福, 行鸿彦, Zenghui Wang. 基于平衡生成对抗网络的海洋气象传感网入侵检测研究[J]. 通信学报, 2023, 44(4): 124-136.
[6]	谢人超, 文雯, 唐琴琴, 刘云龙, 谢高畅, 黄韬. 轨道交通移动边缘计算网络安全综述[J]. 通信学报, 2023, 44(4): 201-215.
[7]	余雪勇, 邱礼翔, 宋家宁, 朱洪波. 无人机辅助边缘计算中安全通信与能效优化策略[J]. 通信学报, 2023, 44(3): 45-54.
[8]	刘雪娇, 曹天聪, 夏莹杰. 区块链架构下高效的车联网跨域数据安全共享研究[J]. 通信学报, 2023, 44(3): 186-197.
[9]	张雷, 王玉, 田建杰, 张琳, 章天骄. 基于IRS辅助的MIMO车联网系统联合波束成形设计[J]. 通信学报, 2023, 44(2): 59-69.
[10]	范伟, 彭诚, 朱大立, 王雨晴. 移动边缘计算网络下基于静态贝叶斯博弈的入侵响应策略研究[J]. 通信学报, 2023, 44(2): 70-81.
[11]	赵庶旭, 韦萍, 王小龙. 多任务并发边缘计算环境中最优联盟结构生成策略[J]. 通信学报, 2023, 44(2): 172-184.
[12]	龙隆, 刘子辰, 陆在旺, 张玉成, 李蕾. 移动边缘网络下服务缓存与资源分配联合优化策略[J]. 通信学报, 2023, 44(1): 64-74.
[13]	曾嵘, 杭潇. 车联网环境下可重构智能反射面辅助无线信道估计算法[J]. 通信学报, 2022, 43(8): 142-150.
[14]	张宇, 程旻. NDN中边缘计算与缓存的联合优化[J]. 通信学报, 2022, 43(8): 164-175.
[15]	程翔, 张浩天, 杨宗辉, 黄子蔚, 李思江, 余安澜. 车联网通信感知一体化研究：现状与发展趋势[J]. 通信学报, 2022, 43(8): 188-202.