New Method of Task Offloading in Mobile Edge Computing for Vehicles Based on Simulated Annealing Mechanism
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摘要: 在车联网(IOV)环境中，如果将车辆的计算任务都放置在云平台执行，无法满足对于信息处理的实时性，考虑移动边缘计算技术以及任务卸载策略，将用户的计算任务卸载到靠近设备边缘的服务器去执行。但是在密集的环境下，如果所有的任务都卸载到附近的边缘服务器去执行，同样会给边缘服务器带来巨大的负载。该文提出基于模拟退火机制的车辆用户移动边缘计算任务卸载新方法，通过定义用户的任务计算卸载效用，综合考虑时耗和能耗，结合模拟退火机制，根据当前道路的密集程度对系统卸载效用进行优化，改变用户的卸载决策，选择在本地执行或者卸载到边缘服务器上执行，使得在给定的环境下的所有用户都能得到满足低时延高质量的服务。仿真结果表明，该算法在减少用户任务计算时间的同时降低了能量消耗。Abstract: For Internet Of Vehicles(IOV), if all the computing tasks of vehicles are placed on the cloud platform, it can not meet the real-time requirement of information processing. Considering the mobile edge computing technology and task offloading method, the computing tasks are offloaded to the server near the edge of the device. However, in a dense environment, if all the tasks are offloaded, it would also bring large pressure to the edge server. A new method for offloading mobile edge computing tasks for vehicle users based on simulated annealing mechanism is proposed in this paper. By defining the user's task to calculate the offloading utility, comprehensively considering the time consumption and energy consumption, combining with simulated annealing, the utility of system offloading is optimized according to the current road density, and the user's offloading decision is changed. The offloading is executed locally or on the edge server, so that all users in a given environment can get high-quality service with low delay. The simulation results show that the algorithm can reduce the user task computing time and energy consumption at the same time.
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Key words:
- Internet Of Vehicles(IOV) /
- Edge computing /
- Task offloading /
- Optimal /
- Simulated annealing mechanism
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表 1 变量符号及含义
变量符号 含义 $ V $ 用户 $ M $ 服务器 $ {T}_{v} $ 用户计算任务 $ {t}_{v}^{\mathrm{l}\mathrm{o}\mathrm{c}} $ 用户在本地执行任务时耗 $ {e}_{v}^{\mathrm{l}\mathrm{o}\mathrm{c}} $ 用户在本地执行任务能耗 $ {E}_{v,m}^{n} $ 用户选择服务器上任意频带n进行传输 ${{\boldsymbol{p}}}_{v}^{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{n} }$ 用户输出功率 $ {C}_{v,m} $ 信息传送速率 $ {t}_{v}^{\text{tran}} $ 用户将任务上传所需时间 $ {t}_{v}^{\mathrm{c}\mathrm{o}\mathrm{m}} $ 任务在服务器的执行时间 $ {t}_{v}^{\mathrm{o}\mathrm{f}\mathrm{f}} $ 任务卸载到边缘的时间消耗 $ {e}_{v}^{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{n}} $ 任务卸载到边缘的时间能耗 $ {Q}_{v} $ 用户卸载效用 $ {\lambda }_{1} $ 时耗偏好 $ {\lambda }_{2} $ 能耗偏好 
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表 2 生成初始温度(算法1)
输入：v, m, n //初始用户数量，边缘服务器数量，每个边缘服务器子频带数量 offload(v,m,n)=zeros(v,m,n) //所有用户都没有卸载任务 Qv_old=0; //此时所有用户的卸载效用为0 for user=1:v for server=1:m for sub_server=1:n //每个边缘服务器配置7个子频带
$ {Q}_{v}=\left({\lambda }_{1}\dfrac{{t}_{v}^{\mathrm{l}\mathrm{o}\mathrm{c}}-{t}_{v}^{\mathrm{o}\mathrm{f}\mathrm{f}}}{{t}_{v}^{\mathrm{l}\mathrm{o}\mathrm{c}}}+{\lambda }_{2}\dfrac{{e}_{v}^{\mathrm{l}\mathrm{o}\mathrm{c}}-{e}_{v}^{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{n}}}{{e}_{v}^{\mathrm{l}\mathrm{o}\mathrm{c}}}\right){\displaystyle\sum }_{m\in M}{E}_{v,m},\forall v\in V $//根据式(12)，用户选择满足自身卸载效用最大的方式执行任务 if (Qv_new>Qv_old) offload(v,m,n)=0 else offload(v,m,n)=1 end //如果卸载效用低于之前的效用则不进行卸载 end end end 输出：offload(v,m,n) //得到用户初始的任务卸载策略
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表 3 状态转换(算法2)
输入：offload(v, m, n) //当前任务卸载策略，初始用户数量，边缘服务器数量，每个边
缘服务器子频带数量v_change=unidrnd(v) //选择改变执行方式的用户 [thism,thisn]=find(v,m,n) //找到用户当前的任务执行方式 if(change>rand) //改变任务在本地/服务器执行 offload(v,thism,thisn)=1-offload(v,thism,thisn) else if (offload(v,thism,thisn)==1) if(M_change>rand) nowm =unidrnd(m) //改变所选的卸载的服务器 offload(v,thism,thisn)=0 offload(v,nowm,thisn)=1 else //改变当前服务器所选择的子频带 nown=unidrnd(n) offload(v,thism,thisn)=0 offload(v,thism,nown)=1 end end 输出：offload(v, m, n) //新的卸载策略
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表 4 基于模拟退火机制的边缘计算任务卸载算法(算法3)
输入：v, m, n, paras //初始用户数量，边缘服务器数量，每个边缘服务器子频带数
量，其他设定参数Offload=Offload_old=getORI(v,m,n) //根据算法1，得到初始卸载策略 Q=Q_old=getQ(offload_old,paras) //根据式(15)，计算出卸载效用，并看作当前“最优解” while(T>T_end && i<i_end) //如果没有达到设定的终止温度，在规定的迭代次数范围内 for usr=1:k //定义每次改变策略的用户数量为k offload_new=changeoff(v,m,n) Q_new=getQ(offload_new,paras) deltQ=Q_new-Qold if(delQ>0) //如果本次策略卸载效用高于上一次，则接受本次策略 offload_old=offload_new Q_old=Q_new if(Q_new>Q) //如果本次策略卸载效用高于目前的最优解，则替代之前的解 Q=Q_new offload=offload_new end else if getP>rand //根据式(16)计算降温概率并判断是否接受本次策略 offload_old=offload_new Q_old=Q_new end end T=aT //根据降温系数改变温度 end 输出：Q,Offload //当达到终止温度或者规定的迭代次数，终止退火，此时得到
的用户卸载效用即是函数的最优解。
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表 5 仿真参数
仿真参数 取值 服务器个数(个) 4,9,16,25 车流量大小(辆) 30–Vmax
V3max = 110每个服务器的子频带数量(个) 7 计算任务大小(kb) 480 服务器间距(km) 1 能耗系数(J/bit) $ {5.0\times 10}^{-27} $ 服务器带宽(MHz) 75 终止温度 $ {10}^{-9} $ 温度下降率 0.97 领域解空间大小 5 权重$ {\mathit{\lambda }}_{1}{,\mathit{\lambda }}_{2} $ 1/2,1/2
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