Research on Content-aware Classification Offloading Algorithm Based on Mobile Edge Calculation in the Internet of Vehicles
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摘要: 随着智能交通的快速发展，车辆终端产生大量需要实时处理的数据消息，而在有限资源上的竞争将会增加消息处理的时延，且对终端设备造成很大的能量消耗。针对时延和能量损耗的均衡关系，该文提出一种基于移动边缘计算(MEC)的内容感知分类卸载算法。首先根据层次分析法对安全消息进行优先级划分，然后建立时延和能量损耗的最优任务卸载模型，通过给时延和能量损耗赋予不同的权重系数构造关系模型，并利用拉格朗日松弛法将非凸问题转化为凸问题，从而结合次梯度投影法和贪婪算法得到问题的可行解。性能评估结果表明，该算法在一定程度上改善了消息处理时延和能量损耗。Abstract: With the rapid development of intelligent transportation, vehicle terminals generate a large number of data messages that need to be processed in real time. Competition on limited resources will increase the delay of message processing and energy consumption for terminal equipment. For the equilibrium relationship between delay and energy loss, this paper proposes a content-aware classification offloading algorithm based on Mobile Edge Computing (MEC). Firstly, the security message is prioritized according to the analytic hierarchy process, and then the optimal task unloading model of delay and energy loss is established. The relational model is established by assigning different weight coefficients to delay and energy loss. The Lagrangian relaxation method is used to transform the non-convex problem into a convex problem, which combines the sub-gradient projection method and the greedy algorithm to obtain the feasible solution. The performance evaluation results show that the algorithm improves the message processing delay and energy loss to some extent.
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Key words:
- Mobile Edge Computing (MEC) /
- Computational offloading /
- Message priority /
- Delay
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表 1 任务队列调度算法
(1) 输入消息的数据大小、消息所需的CPU周期、截止期限要求
和消息的优先级别${b_j}$, Cj, Tj 和Pj；
(2) for 边缘服务器中的每个安全消息Mj；(3) if pj=3，则 (4) 将消息Mj放置在QH队列中； (5) 构建层次分析矩阵A$ = {({a_{ij}})_{n \times n}}$； (6) 计算影响因素所对应的权重矢量$U_r^k$； (7) 根据层次分析矩阵获得其权重所对应的特征值
${{\varLambda}} {\rm{ = [}}{\lambda _1}, {\lambda _2},{\lambda _3}{{\rm{]}}^{\rm{T}}}$；(8) 通过${\mathbf{PV} }{\rm{ = } }\varDelta \times \varLambda $得到每个消息的优先级向量，即消息的
优先级值；(9) 根据PV值的大小在QH队列中按顺序排列； (10) else if pj=2，则 (11) 将消息Mi放置在QM队列中； (12) 重复步骤(4)—步骤(7)； (13) 根据PV值的大小在QM队列中按顺序排列； (14) else if pj=1，则 (15) 将消息Mj放置在QL队列中； (16) 重复步骤(4)—步骤(7)； (17) 根据PV值的大小在QL队列中按顺序排列； (18) End if; (19) End for; (20) End 
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表 2 消息任务卸载策略
(1) 输入：任务集$M$，边缘计算服务器集
$I$，分配的通信带宽为wij，分配的计算速率由vij；(2) 输出：分配系数$x$和目标函数值${z^ * }$； (3) for $i \in I$和$j \in M$； (4) 初始化拉格朗日乘数${\lambda ^0},{\lambda ^1},{\lambda ^2},{\lambda ^3}$，并根据式(11)求得传
输功率${p_{i,j}}$；(5) 计算${W_{i,j}}$和${V_{i,j}}$，设${z^ * }$=0； (6) if ${W_{i,j}} < {W_i}$和${V_{i,j}} < {V_i}$： (7) $x$=1； (8) else (9) $x$=0； (10) End if; (11) 利用$x$更新目标函数式(15)； (12) 根据$g(\lambda )$的次梯度投影更新拉格朗日乘数，并利用
KKT条件更新传输功率${p_{i,j}}$；(13) End for; (14) End。
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