车辆间计算任务卸载算法与系统级仿真验证

曾启程; 孙宇璇; 周盛

doi:10.11959/j.issn.2096-3750.2019.00120

您当前的位置：

首页 >

文章列表页 >

车辆间计算任务卸载算法与系统级仿真验证

理论与技术 | 更新时间：2024-06-05

- 车辆间计算任务卸载算法与系统级仿真验证
- Computation task offloading algorithm and system level simulation for vehicles
- 物联网学报 2019年3卷第3期页码：62-69
- 作者机构：
- 作者简介：
  
  [ "曾启程（1997- ），男，江西吉安人，清华大学博士生，主要研究方向为移动边缘计算、编码计算及相关应用。" ]
  [ "孙宇璇（1993- ），女，辽宁大连人，清华大学博士生，主要研究方向为移动边缘计算、编码计算及分布式机器学习。" ]
  [ "周盛（1983- ），男，上海人，博士，清华大学副教授、博士生导师，主要研究方向为绿色无线通信网、车联网、无线边缘计算和智能。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61871254);国家自然科学基金资助项目;The National Natural Science Foundation of China(91638204)
- DOI：10.11959/j.issn.2096-3750.2019.00120
  中图分类号： TN929.5
- 纸质出版日期：2019-09-30，
  
  网络出版日期：2019-09，
- 稿件说明：
移动端阅览
曾启程, 孙宇璇, 周盛. 车辆间计算任务卸载算法与系统级仿真验证[J]. 物联网学报, 2019,3(3):62-69.

QICHENG ZENG, YUXUAN SUN, SHENG ZHOU. Computation task offloading algorithm and system level simulation for vehicles. [J]. Chinese journal on internet of things, 2019, 3(3): 62-69.
曾启程, 孙宇璇, 周盛. 车辆间计算任务卸载算法与系统级仿真验证[J]. 物联网学报, 2019,3(3):62-69. DOI： 10.11959/j.issn.2096-3750.2019.00120.

QICHENG ZENG, YUXUAN SUN, SHENG ZHOU. Computation task offloading algorithm and system level simulation for vehicles. [J]. Chinese journal on internet of things, 2019, 3(3): 62-69. DOI： 10.11959/j.issn.2096-3750.2019.00120.

摘要

随着自动驾驶技术和车联网的发展，越来越多的车辆将具备强大的计算能力，并通过无线网络实现互联。这些计算资源不仅能够应用于自动驾驶中，也可以提供广泛的边缘计算服务。针对车辆间的计算卸载场景，以最小化平均卸载时延为目标，提出了基于在线学习的分布式计算任务卸载算法。进一步搭建了系统级仿真平台，分别在真实的高速公路和城市街区道路环境下，评估了车辆密度、任务卸载份数对平均卸载时延的影响，为不同交通环境下的服务资源分配部署提供了参考。

Abstract

With the development of autonomous driving and vehicular network

more and more vehicles will have powerful computing capabilities and connection with each other via wireless network.These computing resources can not only be applied to automatic driving

but also provide a wide range of edge computing services.Aiming at the task offloading among vehicles

a distributed task offloading algorithm based on online learning was proposed to minimize the average offloading delay.Furthermore

a system-level simulation platform was built to evaluate the impact of vehicle density and number of tasks on the average offloading delay in both highway and urban scenarios.The results provide a reference for the resource allocation and deployment of task offloading in different traffic situations.

关键词

车联网Veins计算任务卸载系统级仿真

Keywords

Internet of vehiclesVeinscomputation task offloadingsystem level simulation

references

LEE E, LEE E K, GERLA M ,et al. Vehicular cloud networking:architecture and design principles[J]. IEEE Communications Magazine, 2014,52(2): 148-155.

HU Y C, PATEL M, SABELLA D ,et al. Mobile edge computing—a key technology towards 5G[J]. ETSI White Paper, 2015,11(11): 1-16.

SHIH Y Y, CHUNG W H, PANG A C ,et al. Enabling low-latency applications in fog-radio access networks[J]. IEEE Network, 2016,31(1): 52-58.

HOU X S, LI Y, CHEN M ,et al. Vehicular fog computing:a viewpoint of vehicles as the infrastructures[J]. IEEE Transactions on Vehicular Technology, 2016,65(6): 3860-3873.

ABDELHAMID S, HASSANEIN H S, TAKAHARA G . Vehicle as a resource (VaaR)[J]. IEEE Network, 2015,29(1): 12-17.

BITAM S, MELLOUK A, ZEADALLY S . VANET-cloud:a generic cloud computing model for vehicular Ad Hoc networks[J]. IEEE Wireless Communications, 2015,22(1): 96-102.

JANG I, CHOO S J, KIM M ,et al. The software-defined vehicular cloud:a new level of sharing the road[J]. IEEE Vehicular Technology Magazine, 2017,12(2): 78-88.

ZHOU S, SUN Y X, JIANG Z Y ,et al. Exploiting moving intelligence:delay-optimized computation offloading in vehicular fog networks[J]. IEEE Communications Magazine, 2019,57(5): 49-55.

SUN Y, GUO X Y, SONG J H ,et al. Adaptive learning-based task offloading for vehicular edge computing systems[J]. IEEE Transactions on Vehicular Technology, 2019,68(4): 3061-3074.

SUN Y X, SONG J H, ZHOU S ,et al. Task replication for vehicular edge computing:a combinatorial multi-armed bandit based approach[C]// 2018 IEEE Global Communications Conference (GLOBECOM). IEEE, 2018: 1-7.

ZHENG K, MENG H L, CHATZIMISIOS P ,et al. An SMDP-based resource allocation in vehicular cloud computing systems[J]. IEEE Transactions on Industrial Electronics, 2015,62(12): 7920-7928.

FENG J Y, LIU Z, WU C L ,et al. AVE:autonomous vehicular edge computing framework with ACO-based scheduling[J]. IEEE Transactions on Vehicular Technology, 2017,66(12): 10660-10675.

DORIGO M, GAMBARDELLA L M . Ant colony system:a cooperative learning approach to the traveling salesman problem[J]. IEEE Transactions on Evolutionary Computation, 1997,1(1): 53-66.

JIANG Z Y, ZHOU S, GUO X Y ,et al. Task replication for deadline-constrained vehicular cloud computing:optimal policy,performance analysis,and implications on road traffic[J]. IEEE Internet of Things Journal, 2017,5(1): 93-107.

GRUNDMANN M, KWATRA V, HAN M ,et al. Efficient hierarchical graph-based video segmentation[C]// 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010: 2141-2148.

CHEN W, WANG Y J, YUAN Y . Combinatorial multi-armed bandit:general framework and applications[C]// International Conference on Machine Learning, 2013(28): 151-159.

CODECA L, FRANK R, ENGEL T . Luxembourg SUMO traffic (lust) scenario:24 hours of mobility for vehicular networking research[C]// 2015 IEEE Vehicular Networking Conference (VNC). IEEE, 2015: 1-8.

KRAU S . Microscopic modeling of traffic flow:investigation of collision free vehicle dynamics[D]. Centre for Aerospace and Space, 1998.

SOMMER C, DRESSLER F . Using the right two-ray model? a measurement based evaluation of PHY models in VANETs[C]// Proceedings of 17th ACM International Conference on Mobile Computing and Networking (MobiCom 2011). ACM, 2011: 1-3.

浏览量

862

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

车联网边缘计算场景下基于改进型NSGA-Ⅱ算法的边缘服务器部署决策

车联网边缘智能：概念、架构、问题、实施和展望

面向车联网的认知雷达通信复合波形设计

结合聚类与CMAB的群智感知车联网任务分配方法

动态时空数据驱动的认知车联网频谱感知与共享技术研究