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基于移动边缘计算的GI/GI/1排队建模与调度算法

张珂 张利国

张珂, 张利国. 基于移动边缘计算的GI/GI/1排队建模与调度算法. 自动化学报, 2022, 48(7): 1737−1746 doi: 10.16383/j.aas.c190902
引用本文: 张珂, 张利国. 基于移动边缘计算的GI/GI/1排队建模与调度算法. 自动化学报, 2022, 48(7): 1737−1746 doi: 10.16383/j.aas.c190902
Zhang Ke, Zhang Li-Guo. GI/GI/1 queuing model and task scheduling for mobile edge computing. Acta Automatica Sinica, 2022, 48(7): 1737−1746 doi: 10.16383/j.aas.c190902
Citation: Zhang Ke, Zhang Li-Guo. GI/GI/1 queuing model and task scheduling for mobile edge computing. Acta Automatica Sinica, 2022, 48(7): 1737−1746 doi: 10.16383/j.aas.c190902

基于移动边缘计算的GI/GI/1排队建模与调度算法

doi: 10.16383/j.aas.c190902
基金项目: 国家自然科学基金(61873007), 北京市自然科学基金(1182001)资助
详细信息
    作者简介:

    张珂:北京工业大学信息学部控制科学与工程专业硕士研究生. 2017年获得河南理工大学自动化专业学士学位. 主要研究方向为智能交通系统, 移动边缘计算.E-mail: zhgke766@126.com

    张利国:北京工业大学信息学部控制科学与工程专业教授. 2011–2012年国家公派美国加州大学伯克利分校访问学者. 主要研究方向为混杂系统, 智能交通系统和分布式参数控制系统. 本文通信作者.E-mail: zhangliguo@bjut.edu.cn

GI/GI/1 Queuing Model and Task Scheduling for Mobile Edge Computing

Funds: Supported by National Natural Science Foundation of China (61873007) and Beijing Natural Science Foundation (1182001)
More Information
    Author Bio:

    ZHANG Ke Master student in the control science and engineering, at Faculty of Information Technology, Beijing University of Technology. He received his bachelor degree from Henan Polytechnic University in 2017. His research interest covers intelligent transportation systems, mobile edge computing

    ZHANG Li-Guo Professor in the control science and engineering, at Faculty of Information Technology, Beijing University of Technology. He was a visiting scholar (2011–2012) of University of California, Berkeley. His research interest covers hybrid systems, intelligent transportation systems, and control of distributed parameter systems. Corresponding author of this paper

  • 摘要: 针对车联网环境下路侧边缘计算节点部署不均衡、服务密度小、实时调度计算压力大等问题, 提出一种基于智能车移动边缘计算(Mobile edge computing, MEC)的任务排队建模与调度算法, 提供弹性计算服务, 将具备感知、计算、控制功能的智能车作为移动边缘计算服务器, 设计了车联网环境下的MEC体系架构. 首先基于虚拟化技术对智能车进行虚拟化抽象, 利用排队论对虚拟车任务构建了GI/GI/1排队模型. 然后基于云平台Voronoi分配算法对虚拟车任务进行分配绑定, 进而实现了智能车的优化调度与分布式弹性服务, 解决了边缘计算任务分配不均衡等问题. 最后通过城市交通路网中的车辆污染排放的实时计算实验, 验证了该方法的有效性.
  • 图  1  车联网环境下的MEC体系架构

    Fig.  1  The MEC architecture for vehicle networks

    图  2  虚拟车任务虚拟截止时间计算示意图

    Fig.  2  Virtual vehicle task's deadline time calculation schematic diagram

    图  3  基于Voronoi分配算法的任务队列调度模型图

    Fig.  3  Task queue scheduling model diagram based on Voronoi allocation algorithm

    图  4  MEC调度系统仿真模式类图

    Fig.  4  Simulation mode class diagram of MEC scheduling system

    图  5  基于Voronoi算法的任务分配结果示意图

    Fig.  5  Allocation result based on Voronoi algorithm

    图  6  FCFS, FDFS, CFS调度算法下智能车服务时间对比

    Fig.  6  Service time comparison under scheduling algorithm of FCFS, FDFS, CFS

    图  7  交通污染排放计算的智能车调度策略

    Fig.  7  Intelligent vehicle scheduling strategy for traffic pollutant emission computing

    表  1  VSP排放等级与平均排放清单

    Table  1  VSP modes and the average modal emission rates of each

    VSP 等级 VSP mode ${\rm{C} }{ {\rm{O} }_2}\left( {\rm{g/s}} \right)$ ${\rm{CO} }\left( {\rm{g/s}} \right)$ ${\rm{N} }{ {\rm{O} }_X}\left( {\rm{g/s}} \right)$ ${\rm{HC} }\left( {\rm{g/s}} \right)$
    ${\rm{VSP}} < -2$ 1 1.54369 0.01103 0.00101 0.00090
    $-2\le {\rm{VSP}} < 0$ 2 1.60441 0.00872 0.00104 0.00090
    $0\le {\rm{VSP}} < 1$ 3 1.13083 0.00468 0.00042 0.00084
    $ \cdot \cdot \cdot $ $ \cdot \cdot \cdot $ $ \cdot \cdot \cdot $ $ \cdot \cdot \cdot $ $ \cdot \cdot \cdot $ $ \cdot \cdot \cdot $
    $28\le {\rm{VSP} } < 33$ 12 7.61770 0.24781 0.01438 0.00457
    $33\le {\rm{VSP}} < 39$ 13 8.32244 0.41307 0.01597 0.00570
    $39\le {\rm{VSP}}$ 14 8.47503 0.62466 0.01672 0.00716
    下载: 导出CSV

    表  2  VVs的任务计算参数

    Table  2  VVs calculation parameters

    $Tas{{k}_{i}}\left( j \right)$ $T_{i}^{\rm{arr}}\left( j \right)$ $task_{i}^{X }\left( j \right)$ $T_{i}^{V{\rm{tra}}}\left( j \right)$ $T_{i}^{s}\left( j \right)$ $T_{i}^{V{\rm{ser}}}\left( j \right)$ $T_{i}^{Vd}\left( j \right)$
    $i=2,\;j=2$ 9:09:02 (39.8726, 116.466) 89 169 258 9:13:20
    $i=3,\;j=3$ 9:13:37 (39.8702, 116.476) 110 80 190 9:16:47
    $i=7,\;j=1$ 9:16:52 (39.875, 116.475) 200 60 260 9:21:12
    $i=9,\;j=6$ 9:21:14 (39.8787, 116.471) 0 52 52 9:22:06
    $i=12,\;j=3$ 9:22:06 (39.8767, 116.466) 78 60 138 9:24:24
    $i=15,\;j=4$ 9:24:34 (39.8705, 116.466) 40 43 83 9:25:57
    下载: 导出CSV

    表  3  IV的实际运行参数

    Table  3  Actual operating parameters of IV

    $Tas{{k}_{m}}\left( n \right)$ $T_{m}^{I{\rm{tra}}}\left( n \right)$ $T_{m}^{Is}\left( n \right)$ $T_{m}^{I{\rm{ser}}}\left( n \right)$ $T_{m}^{\rm{finish}}\left( n \right)$ ${\rm{C O}}_{2} \left({\rm{g} } \right)$ ${\rm{CO}}\left({\rm{g} } \right)$ ${\rm{NO}}_{X} \left({\rm{g} }\right)$ ${\rm{HC}}\left({\rm{g} }\right)$
    $m=5,\;n=1$ 95 178 273 9:13:35 992.78 6.76 0.81 0.51
    $m=5,\;n=2$ 117 76 193 9:16:50 1144.42 7.76 0.79 0.63
    $m=5,\;n=3$ 202 58 260 9:21:12 426.80 2.96 0.29 0.24
    $m=5,\;n=4$ 0 50 50 9:22:04 590.63 4.11 0.41 0.33
    $m=5,\;n=5$ 83 63 146 9:24:32 1658.42 11.41 1.14 0.92
    $m=5,\;n=6$ 37 39 46 9:25:20 868.51 5.92 0.61 0.48
    下载: 导出CSV
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  • 收稿日期:  2019-12-31
  • 录用日期:  2020-04-07
  • 刊出日期:  2022-07-01

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