面向车联网实时应用场景的任务卸载决策算法

doi:10.13190/j.jbupt.2017-256

北京邮电大学学报 ›› 2018, Vol. 41 ›› Issue (4): 44-50.doi: 10.13190/j.jbupt.2017-256

面向车联网实时应用场景的任务卸载决策算法

彭维平, 苏哲, 宋成, 贾宗璞

河南理工大学计算机科学与技术学院, 河南焦作 454000

收稿日期:2017-12-20 出版日期:2018-08-28 发布日期:2018-10-09
作者简介:彭维平(1979-),男,副教授,硕士生导师;苏哲(1993-),男,硕士生,E-mail:suzhe4285@163.com.
基金资助:
河南省科技攻关项目（182102110333）；河南理工大学博士基金项目（B2012-050）

Task Offloading Decision Algorithm of Real-Time Application for Internet of Vehicles

PENG Wei-ping, SU Zhe, SONG Cheng, JIA Zong-pu

School of Computer Science and Technology, Henan Polytechnic University, Henan Jiaozuo 454000, China

Received:2017-12-20 Online:2018-08-28 Published:2018-10-09

摘要/Abstract

摘要： 为了解决当前车联网中节点处理任务的时效性问题，基于最优节点选取及任务卸载理论，提出了一种新的任务卸载决策方案.该方案对车联网场景下的任务卸载进行建模，构造出具有指向性的节点分布模型，利用最优节点选取算法对该模型进行节点预选取，通过一次或二次任务卸载预判机制，实现节点对任务的卸载决策.与传统的任务卸载决策相比，所提方案在任务卸载全过程中耗时更少，实时性更高.仿真结果验证了所提方法的有效性.

关键词: 车联网, 预选取, 二次任务卸载, 卸载决策

Abstract: In order to solve the problem of the timeliness of nodes processing tasks in the internet of vehicles, a new task offloading decision-making scheme is proposed based on the optimal node selection and task offloading theory. First of all, the task offloading model is built and a directional node distribution model is constructed. Furthermore, the node is pre-selected by using the optimal node selection algorithm. Finally, the task offloading decision is achieved by once or second task offloading pre-judgment mechanism. Compared with the traditional task offloading decision-making, the proposed solution takes less time, and the task Real-time is more higher. Simulation results verify the effectiveness of the proposed method.

Key words: internet of vehicles, pre-selected, second task offloading, offloading decision

中图分类号:

TN929.5

彭维平, 苏哲, 宋成, 贾宗璞. 面向车联网实时应用场景的任务卸载决策算法[J]. 北京邮电大学学报, 2018, 41(4): 44-50.

PENG Wei-ping, SU Zhe, SONG Cheng, JIA Zong-pu. Task Offloading Decision Algorithm of Real-Time Application for Internet of Vehicles[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(4): 44-50.

参考文献

[1] 田辉, 范绍帅, 吕昕晨, 等. 面向5G需求的移动边缘计算[J]. 北京邮电大学学报, 2017, 40(2):1-10. Tian Hui, Fan Shaoshuai, Lü Xinchen, et al. Mobile edge computing for 5G requirements[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(2):1-10.
[2] Wu H, Huang D, Bouzefrane S. Making offloading decisions resistant to network unavailability for mobile cloud collaboration[C]//International Conference Conference on Collaborative Computing:Networking, Applications and Worksharing.[S. l.]:IEEE, 2013:168-177.
[3] Kosta S, Aucinas A, Pan H, et al. Unleashing the power of mobile cloud computing using ThinkAir[EB/OL]. 2011. http://cn.arxiv.org/abs/1105.3232.
[4] Cuervo E, Balasubramanian A, Cho D K, et al. MAUI:making smartphones last longer with code offload[C]//International Conference on Mobile Systems, Applications, and Services.[S. l.]:DBLP, 2010:49-62.
[5] Zhao P, Tian H, Fan B. Partial critical path based greedy offloading in small cell cloud[C]//Vehicular Technology Conference.[S. l.]:IEEE, 2016:1-5.
[6] Kao Y H, Krishnamachari B, Ra M R, et al. Hermes:latency optimal task assignment for resource-constrained mobile computing[J]. IEEE Transactions on Mobile Computing, 2017, 16(11):3056-3069..
[7] Zhang W, Wen Y, Wu D O. Collaborative task execution in mobile cloud computing under a stochastic wireless channel[J]. IEEE Transactions on Wireless Communications, 2014, 14(1):81-93.
[8] 侯蓉晖, 郭素霞. 车联网场景中时延受限内容传输方案[J]. 北京邮电大学学报, 2017, 40(3):85-90. Hou Ronghui, Guo Suxia. Latency-constrained content dissemination scheme in vehicular networks[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(3):85-90.
[9] Deng M, Tian H, Fan B. Fine-granularity based application offloading policy in cloud-enhanced small cell networks[C]//IEEE International Conference on Communications Workshops.[S. l.]:IEEE, 2016:638-643.
[10] Lü X, Tian H. Adaptive receding horizon offloading strategy under dynamic environment[J]. IEEE Communications Letters, 2016, 20(5):878-881.
[11] Tam H H M, Tuan H D, Ngo D T, et al. Joint load balancing and interference management for small-cell heterogeneous networks with limited backhaul capacity[J]. IEEE Transactions on Wireless Communications, 2017, 16(2):872-884.
[12] Katyal M, Mishra A. A comparative study of load balancing algorithms in cloud computing environment[J]. Computer Science, 2014, 6(1):25-36.

[1]	张佳敏帅莉莎董高雅阳小龙. 面向道路实况的车联网共享消息可信评估模型[J]. 北京邮电大学学报, 2023, 46(4): 40-45.
[2]	彭维平王戈宋成阎俊豪. 面向高移动性车联网场景的预测卸载决策算法[J]. 北京邮电大学学报, 2023, 46(2): 43-49.
[3]	刘琨王圆洁申自浩王辉刘沛骞. 结合区块链的车联网隐私保护可信预测缓存架构[J]. 北京邮电大学学报, 2022, 45(6): 145-150.
[4]	彭维平, 韩宁, 宋成. 边缘计算环境下无证书车联网身份认证方案[J]. 北京邮电大学学报, 2022, 45(1): 46-51.
[5]	吕昕晨, 张晨宇. 面向车联网自动驾驶的边缘智能多源数据处理[J]. 北京邮电大学学报, 2021, 44(2): 102-108.
[6]	皇甫伟, 杨心竹, 王欢, 胡晓彦. 基于出租车轨迹数据的车联网通信机会间隔模型[J]. 北京邮电大学学报, 2019, 42(3): 91-97.
[7]	吴振铨, 叶东东, 余荣, 周文辉, 何昭水. 车联网中基于停车协同的边缘计算卸载方法[J]. 北京邮电大学学报, 2019, 42(2): 108-113.
[8]	肖剑, 李文江, 耿洪杨, 翟英博. 车联网中可抵抗DoS攻击的RFID安全认证协议[J]. 北京邮电大学学报, 2019, 42(2): 114-119.
[9]	刘宴兵, 宋秀丽, 肖永刚. 车联网认证机制和信任模型[J]. 北京邮电大学学报, 2017, 40(3): 1-18.
[10]	侯蓉晖, 郭素霞. 车联网场景中时延受限内容传输方案[J]. 北京邮电大学学报, 2017, 40(3): 85-90.
[11]	王剑, 张子键, 袁坚. 应用于CAN总线的广播认证系统[J]. 北京邮电大学学报, 2015, 38(4): 24-28.
[12]	黄晓敏, 汪洋, 张继良, 张乃通. 车联网无线链路传输数据业务性能评估[J]. 北京邮电大学学报, 2014, 37(s1): 66-71.
[13]	李静林, 刘志晗, 杨放春. 车联网体系结构及其关键技术[J]. 北京邮电大学学报, 2014, 37(6): 95-100.

面向车联网实时应用场景的任务卸载决策算法

Task Offloading Decision Algorithm of Real-Time Application for Internet of Vehicles

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 13

编辑推荐

Metrics

本文评价