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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

张海波 荆昆仑 刘开健 贺晓帆

引用本文: 张海波, 荆昆仑, 刘开健, 贺晓帆. 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. doi: 10.11999/JEIT190304 shu
Citation:  Haibo ZHANG, Kunlun JING, Kaijian LIU, Xiaofan HE. 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. doi: 10.11999/JEIT190304 shu

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

    作者简介: 张海波: 男,1979年生,副教授,研究方向为无线资源管理;
    荆昆仑: 男,1995年生,硕士生,研究方向为移动边缘计算;
    刘开健: 女,1981年生,讲师,研究方向为最优化算法;
    贺晓帆: 男,1985年生,助理教授,研究方向为无线资源优化
    通讯作者: 刘开健,liukj@cqupt.edu.cn
  • 基金项目: 国家自然科学基金(61801065, 61601071),长江学者和创新团队发展计划基金(IRT16R72),重庆市基础与前沿项目(cstc2018jcyjAX0463)

摘要: 在新兴的车联网络中,汽车终端请求卸载的任务对网络带宽、卸载时延等有着更加严苛的需求,而新型通信网络研究中移动边缘计算(MEC)的提出更好地解决了这一挑战。该文着重解决的是汽车终端进行任务卸载时卸载对象的匹配问题。文中引入了软件定义车载网络(SDN-V)对全局变量统一调度,实现了资源控制管理、设备信息采集以及任务信息分析。基于用户任务的差异化性质,定义了重要度的模型,在此基础上,通过设计任务卸载优先级机制算法,实现任务优先级划分。针对多目标优化模型,采用乘子法对非凸优化模型进行求解。仿真结果表明,与其他卸载策略相比,该文所提卸载机制对时延和能耗优化效果明显,能够最大程度地保证用户的效益。

English

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  • 图 1  系统模型图

    图 2  数据大小与能耗关系图

    图 3  任务所需周期与能耗关系图

    图 4  数据大小与时延关系图

    图 5  任务所需周期与时延关系图

    图 6  数据大小与总开销关系图

    图 7  任务所需周期数与总开销关系图

    表 1  任务卸载优先级机制

     (1) 输入:车辆$i$的请求信息为$\{ {C_i},{S_i},t_{{Q_i}}^{\max }\} $,定义$\zeta $的取值,$i \in \{ 1\; 2\; ··· \; n\} $, ${\rm{Im}}{{\rm{p}}_{\rm{i}}}{\rm{ = \{ im}}{{\rm{p}}_{\rm{1}}}{\kern 1pt} {\kern 1pt} {\rm{im}}{{\rm{p}}_{\rm{2}}}\; ···\; {\rm{im}}{{\rm{p}}_{{n}}}\; {\rm{\} }}$
     (2) 输出:降序排列的重要度${\rm{im}}{{\rm{p}}_i}$
     (3) for $i = 1;i < n;i + + $
     (4) 将${C_i},t_{{Q_i}}^{\max }$代入式(9)求出${\rm{im}}{{\rm{p}}_i}$
     (5) ${\rm{Im}}{{\rm{p}}_{\rm{i}}}={\rm{\{ im}}{{\rm{p}}_{\rm{1}}}{\kern 1pt} {\kern 1pt} {\rm{im}}{{\rm{p}}_{\rm{2}}}{\kern 1pt} {\kern 1pt} ···\; {\rm{im}}{{\rm{p}}_{{i}}}{\rm{\} }}$
     (6) for $i = 1:n$ do
     (7) if ${{{\rm Imp}(i) < {\rm Imp}(i + 1)}}$; ${{\rm temp} = {\rm Imp}(i + 1)}$; ${{{\rm Imp}(i + 1) = {\rm Imp}(i)}}{\kern 1pt} {\kern 1pt} {\kern 1pt} ;{{{\rm Imp}(i) = {\rm temp}}}$
     (8) end
    下载: 导出CSV

    表 2  基于Q-学习的任务卸载策略机制

     (1) 输入:车辆$i$的请求信息$\{ {Q_i},{T_i}\} $, ${\tau _{\rm{1}}},{\tau _2},({\rm{0 < }}{\tau _{\rm{1}}} < {\tau _{\rm{2}}})$, $i \in \{ 1\; 2\; ··· \; n\} $, ${\rm{Im}}{{\rm{p}}_{{i}}}{\rm{ = \{ im}}{{\rm{p}}_{\rm{1}}}{\kern 1pt} {\kern 1pt} {\rm{im}}{{\rm{p}}_{\rm{2}}}\; ···\; {\rm{im}}{{\rm{p}}_{{i}}}{\rm{\} }}$
     (2) 输出:${x_i}$, ${\psi _i}$
     (3) if ${\rm{im}}{{\rm{p}}_i} < {\tau _{\rm{1}}}$:${x_i}=0$;${\kern 1pt} {\kern 1pt} {\rm{im}}{{\rm{p}}_i} > {\tau _2}$:${x_i}{\rm{ = 1}}$
     (4) elif ${\tau _{\rm{1}}} < {\rm{im}}{{\rm{p}}_i} < {\tau _{\rm{2}}}$:初始化$g$, ${x_{ij}} = 1$, $\varsigma $, $p$, $\hat Q\left( {{a_i}} \right) = 0,\; {\kern 1pt} t = 0$最大收敛时间${t_{c - \max }}$
     (5) while ${\kern 1pt} t < {t_{c - \max }} + 1$:按照时延约束对车辆用户排序
     (6) for $i = 1:N\; {\kern 1pt} {\kern 1pt} $ do
     (7) 根据贪婪方法选择行为${a_i}$、根据式(15)求出用户奖励
     (8) 更新$\hat { Q}$数值矩阵通过${\hat Q_{t + 1}}\left( {s,a} \right) \leftarrow \left( {1 - \varsigma } \right){\hat Q_t}\left( {s,a} \right) + \varsigma \left( {g + \eta \mathop {\max }\limits_{a'} {{\hat Q}_t}\left( {s',a'} \right)} \right)$, $p \leftarrow \left( {p/\sqrt t } \right)$
     (9) end for;$t = t + 1$;end while
     (10) 利用${\psi _i}$更新目标优化式(7)
     (11) end
    下载: 导出CSV

    表 3  模拟参数表

    参数 数值
    计算任务${Q_i}$ 1~50 MB
    传输带宽$W$ 100 MHz
    汽车用户发射功率${p_i}$ 0.2 W
    任务所需CPU周期数${C_i}$ 0.1~1 GHz
    MEC服务器CPU周期频率${f_{\rm b}}$ 6 GHz
    车辆用户的CPU周期频率${f_v}$ 0.5~1 GHz
    高斯噪声${\sigma ^2}$ –100 dBm
    信道传输距离${d_{mn}}$ 5~500 m
    汽车CPU能耗功率系数${p_{{v} } }$ 80 W/GHz
    电池最大容量 20 kWh
    下载: 导出CSV
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文章相关
  • 通讯作者:  刘开健, liukj@cqupt.edu.cn
  • 收稿日期:  2019-04-30
  • 录用日期:  2019-09-05
  • 网络出版日期:  2019-09-18
  • 刊出日期:  2020-03-01
通讯作者: 陈斌, bchen63@163.com
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