《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (10): 3140-3147.DOI: 10.11772/j.issn.1001-9081.2021081490
• 网络与通信 • 上一篇
收稿日期:
2021-08-09
修回日期:
2021-11-18
接受日期:
2021-11-19
发布日期:
2022-01-07
出版日期:
2022-10-10
通讯作者:
李智
作者简介:
第一联系人:李智(1993—),女,陕西渭南人,硕士研究生,主要研究方向:移动通信; 346864982@qq.com基金资助:
Received:
2021-08-09
Revised:
2021-11-18
Accepted:
2021-11-19
Online:
2022-01-07
Published:
2022-10-10
Contact:
Zhi LI
About author:
LI Zhi, born in 1993, M. S. candidate. Her research interests include mobile communication.Supported by:
摘要:
网联车辆节点产生的不同属性的大数据流量计算任务进行传输并卸载时,通常引起通信系统中时延抖动、计算能耗与系统开销大等问题,因此,根据实际通信环境,提出一种C-V2X车联网(IoV)中基于模拟退火算法(SAA)的任务卸载与资源分配方案。首先,根据任务处理优先程度,对处理优先程度较高的任务进行协同卸载计算处理;其次,通过全局搜索最优卸载比例因子的方式,制定了一种基于SAA的任务卸载策略,且分析并优化了任务卸载比例因子;最后,在任务卸载比例因子更新过程中,将系统开销最小化问题转化为功率和计算资源分配凸优化问题,并利用拉格朗日乘子法获取最优解。通过对所提算法与本地卸载、自适应遗传算法等作比较可知,随着计算任务的数据量不断增加,自适应遗传算法比本地卸载的时延、能耗、系统开销分别降低了5.97%、49.40%、49.36%,在此基础上基于SAA的方案较自适应遗传算法的时延、能耗、系统开销再降低了6.35%、92.27%、91.7%;随着计算任务CPU周期数不断增加,自适应遗传算法比本地卸载的时延、能耗、系统开销分别降低了16.4%、49.58%、49.23%,在此基础上基于SAA的方案较自适应遗传算法的时延、能耗、系统开销再降低了19.61%、94.39%、89.88%。实验结果表明,SAA不仅能降低通信系统时延、能耗及系统开销,还可以使结果加速收敛。
中图分类号:
李智, 薛建彬. C‑V2X车联网中基于模拟退火算法的任务卸载与资源分配[J]. 计算机应用, 2022, 42(10): 3140-3147.
Zhi LI, Jianbin XUE. Task offloading and resource allocation based on simulated annealing algorithm in C-V2X internet of vehicles[J]. Journal of Computer Applications, 2022, 42(10): 3140-3147.
1 | 蔺宏良,黄晓鹏. 车联网技术研究综述[J]. 机电工程, 2014, 31(9): 1235-1238. 10.3969/j.issn.1001-4551.2014.09.029 |
LIN H L, HUANG X P. Survey on internet of vehicle technology[J]. Journal of Mechanical and Electrical Engineering, 2014, 31(9): 1235-1238. 10.3969/j.issn.1001-4551.2014.09.029 | |
2 | 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. |
LI J L, LIU Z H, YANG F C. Internet of vehicles: the framework and key technology[J]. Journal of Beijing University of Posts and Telecommunications, 2014, 37(6): 95-100. | |
3 | 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): 1-16. |
LIU Y B, SONG X L, XIAO Y G. Authentication mechanism and trust model for internet of vehicles paradigm[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(3): 1-16. | |
4 | 李佐昭,刘金旭. 移动边缘计算在车联网中的应用[J]. 现代电信科技, 2017, 47(3): 37-41. |
LI Z Z, LIU J X. Application of mobile edge computing in internet of vehicles[J]. Modern Science and Technology of Telecommunications, 2017, 47(3): 37-41. | |
5 | 王秋宁,谢人超,黄韬. 移动边缘计算的移动性管理研究[J]. 中兴通讯技术, 2018, 24(1): 37-41. 10.3969/j.issn.1009-6868.2018.01.008 |
WANG Q N, XIE R C, HUANG T. Mobility management of mobile edge computing[J]. ZTE Technology Journal, 2018, 24(1): 37-41. 10.3969/j.issn.1009-6868.2018.01.008 | |
6 | 李子姝,谢人超,孙礼,等. 移动边缘计算综述[J]. 电信科学, 2018, 34(1): 87-101. 10.11959/j.issn.1000-0801.2018011 |
LI Z S, XIE R C, SUN L, et al. A survey of mobile edge computing[J]. Telecommunications Science, 2018, 34(1): 87-101. 10.11959/j.issn.1000-0801.2018011 | |
7 | 陈军,李昊,张涛. LTE-V2X车联网边缘计算部署方式探讨[J]. 信息通信, 2019(11): 76-77. 10.3969/j.issn.1673-1131.2019.11.035 |
CHEN J, LI H, ZHANG T. Discussion of edge computing and deployment methods in LTE-V2X internet of vehicles[J]. Information and Communications, 2019(11): 76-77. 10.3969/j.issn.1673-1131.2019.11.035 | |
8 | 刘继军,邹山花,卢先领. MEC中资源分配与卸载决策联合优化策略[J]. 计算机科学与探索, 2021, 15(5): 848-858. |
LIU J J, ZOU S H, LU X L. Joint optimization scheme of resource allocation and offloading decision in mobile edge computing[J]. Journal of Frontier of Computer Science and Technology, 2021, 15(5): 848-858. | |
9 | 谭友钰,陈蕾,周明拓,等. 动态雾计算网络中基于在线学习的任务卸载算法[J]. 中国科学院大学学报, 2020, 37(5): 688-698. 10.7523/j.issn.2095-6134.2020.05.014 |
TAN Y Y, CHEN L, ZHOU M T, et al. Online learning-based task offloading algorithms for dynamic fog networks[J]. Journal of University of Chinese Academy of Sciences, 2020, 37(5): 688-698. 10.7523/j.issn.2095-6134.2020.05.014 | |
10 | 陈龙险. 移动边缘计算中高能效任务卸载决策[J]. 信息技术, 2020, 44(10): 127-132. 10.13274/j.cnki.hdzj.2020.10.024 |
CHEN L X. High energy-efficient task offloading decision in mobile edge computing[J]. Information Technology, 2020, 44(10): 127-132. 10.13274/j.cnki.hdzj.2020.10.024 | |
11 | TRAN T X, POMPILI D. Joint task offloading and resource allocation for multi-server mobile-edge computing networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(1): 856-868. 10.1109/tvt.2018.2881191 |
12 | NGUYEN T D T, NGUYEN V, PHAM V N, et al. Modeling data redundancy and cost-aware task allocation in MEC-enabled Internet-of-Vehicles applications[J]. IEEE Internet of Things Journal, 2021, 8(3): 1687-1701. 10.1109/jiot.2020.3015534 |
13 | 余翔,陈晓东,王政,等. 基于LTE-V2X的车联网资源分配算法[J]. 计算机工程, 2021, 47(2): 188-193. 10.19678/j.issn.1000-3428.0056935 |
YU X, CHEN X D, WANG Z, et al. Resource allocation algorithm for internet of vehicles based on LTE-V2X[J]. Computer Engineering, 2021, 47(2): 188-193. 10.19678/j.issn.1000-3428.0056935 | |
14 | 薛建彬,刘星星,丁雪乾. 移动边缘计算中基于能量收集的能效优化方案[J]. 北京邮电大学学报, 2020, 43(5): 15-20. |
XUE J B, LIU X X, DING X Q. Energy efficiency optimization scheme based on energy harvesting in mobile edge computing[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(5): 15-20. | |
15 | 吴振铨,叶东东,余荣,等. 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. |
WU Z Q, YE D D, YU R, et al. Edge computing offloading with parked vehicular collaboration in internet of vehicles[J]. Journal of Beijing University of Posts and Telecommunications, 2019, 42(2): 108-113. | |
16 | 杨天,杨军. 移动边缘计算中的卸载决策与资源分配策略[J]. 计算机工程, 2021, 47(2): 19-25. |
YANG T, YANG J. Offloading decision and resource allocation strategy in mobile edge computing[J]. Computer Engineering, 2021, 47(2): 19-25. | |
17 | CHEN X, JIAO L, LI W Z, et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM Transactions on Networking, 2016, 24(5): 2795-2808. 10.1109/tnet.2015.2487344 |
18 | 路亚. MEC多服务器启发式联合任务卸载和资源分配策略[J]. 计算机应用与软件, 2020, 37(10): 77-84. 10.3969/j.issn.1000-386x.2020.10.013 |
LU Y. Heuristic joint task offloading and resource allocation strategy for MEC multi-server[J]. Computer Applications and Software, 2020, 37(10): 77-84. 10.3969/j.issn.1000-386x.2020.10.013 | |
19 | HAMZAH H, LE D C, KIM M, et al. Location-aware task offloading for MEC-based high mobility service[C]// Proceedings of the 2021 International Conference on Information Networking. Piscataway: IEEE, 2021: 708-711. 10.1109/icoin50884.2021.9333924 |
20 | 张海波,荆昆仑,刘开健,等. 车联网中一种基于软件定义网络与移动边缘计算的卸载策略[J]. 电子与信息学报, 2020, 42(3): 645-652. 10.11999/JEIT190304 |
ZHANG H B, JING K L, LIU K J, et al. An offloading mechanism based on software defined network and mobile edge computing in vehicular networks[J]. Journal of Electronics and Information Technology, 2020, 42(3): 645-652. 10.11999/JEIT190304 | |
21 | 闫伟,申滨,刘笑笑. 基于自适应遗传算法的MEC任务卸载及资源分配[J]. 电子技术应用, 2020, 46(8): 95-100. |
YAN W, SHEN B, LIU X X. Offloading and resource allocation of MEC based on adaptive genetic algorithm[J]. Application of Electronic Technique, 2020, 46(8): 95-100. | |
22 | 陈山枝,时岩,胡金玲. 蜂窝车联网(C-V2X)综述[J]. 中国科学基金, 2020, 34(2): 179-185. |
CHEN S Z, SHI Y, HU J L. Cellular Vehicle to Everything (C-V2X): a review[J]. Bulletin of National Natural Science Foundation of China, 2020, 34(2): 179-185. | |
23 | 葛雨明. 我国LTE-V2X标准化及测试验证进展[J]. 移动通信, 2019, 43(11): 36-39. 10.3969/j.issn.1006-1010.2019.11.006 |
GE Y M. Progress on LTE-V2X standardization and testing in China[J]. Mobile Communications, 2019, 43(11): 36-39. 10.3969/j.issn.1006-1010.2019.11.006 | |
24 | 杨波,张莹,蔡之骏,等. 基于LTE-V的车载V2X系统研究[J]. 移动通信, 2019, 43(11): 75-80. 10.3969/j.issn.1006-1010.2019.11.012 |
YANG B, ZHANG Y, CAI Z J, et al. Research on the vehicle V2X system based on LTE-V[J]. Mobile Communications, 2019, 43(11): 75-80. 10.3969/j.issn.1006-1010.2019.11.012 | |
25 | 李华,杨燕玲. 基于LTE-V2X的车联网关键技术研究[J]. 广东通信技术, 2019, 39(11): 61-63. |
LI H, YANG Y L. Research on the key technologies of the internet of vehicles based on LTE-V2X[J]. Guangdong Communication Technology, 2019, 39(11): 61-63. | |
26 | WANG C M, YU F R, LIANG C C, et al. Joint computation offloading and interference management in wireless cellular networks with mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2017, 66(8): 7432-7445. 10.1109/tvt.2017.2672701 |
27 | 刘玮,张翼鹏,关旭迎,等. C-V2X车联网城市级规模示范应用[J]. 电信科学, 2020, 36(4): 27-35. |
LIU W, ZHANG Y P, GUAN X Y, et al. C-V2X city-scale demonstration application[J]. Telecommunications Science, 2020, 36(4): 27-35. | |
28 | 李邱苹,赵军辉,贡毅. 移动边缘计算中的计算卸载和资源管理方案[J]. 电信科学, 2019, 35(3): 36-46. |
LI Q P, ZHAO J H, GONG Y. Computation offloading and resource management scheme in mobile edge computing[J]. Telecommunications Science, 2019, 35(3): 36-46. | |
29 | YOU C S, HUANG K B, CHAE H, et al. Energy-efficient resource allocation for mobile-edge computation offloading[J]. IEEE Transactions on Wireless Communications, 2017, 16(3): 1397-1411. 10.1109/twc.2016.2633522 |
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