Intelligent Resource Allocation Algorithm for Multi-platform Offloading in Vehicular Networks
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摘要:
为了降低计算任务的时延和系统的成本，移动边缘计算(MEC)被用于车辆网络，以进一步改善车辆服务。该文在考虑计算资源的情况下对车辆网络时延问题进行研究，提出一种多平台卸载智能资源分配算法，对计算资源进行分配，以提高下一代车辆网络的性能。该算法首先使用K临近(KNN)算法对计算任务的卸载平台(云计算、移动边缘计算、本地计算)进行选择，然后在考虑非本地计算资源分配和系统复杂性的情况下，使用强化学习方法，以有效解决使用移动边缘计算的车辆网络中的资源分配问题。仿真结果表明，与任务全部卸载到本地或MEC服务器等基准算法相比，提出的多平台卸载智能资源分配算法实现了时延成本的显著降低，平均可节省系统总成本达80%。
Abstract:In order to reduce the delay of computing tasks and the total cost of the system, Mobile Eedge Computing (MEC) technology is applied to vehicular networks to improve further the service quality. The delay problem of vehicular networks is studied with the consideration of computing resources. In order to improve the performance of the next generation vehicular networks, a multi-platform offloading intelligent resource allocation algorithm is proposed to allocate the computing resources. In the proposed algorithm, the K-Nearest Neighbor (KNN) algorithm is used to select the offloading platform (i.e., cloud computing, mobile edge computing, local computing) for computing tasks. For the computing resource allocation problem and system complexity in non-local computing, reinforcement learning is used to solve the optimization problem of resource allocation in vehicular networks using the mobile edge computing technology. Simulation results demonstrate that compared with the baseline algorithms (i.e., all tasks offload to the local or MEC server), the proposed multi-platform offloading intelligent resource allocation algorithm achieves a significant reduction in latency cost, and the average system cost can be saved by 80%.
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算法1 多平台卸载智能资源分配算法 阶段1：初始化 (1) 任务${R_i}$的最大延迟${\tau _{{R_i}}}$，任务大小${B_{{R_i}}}$； (2) 任务的当前位置$P = 1$； (3) ${{Q}}$矩阵，参数$\gamma $，奖赏矩阵${{R}}$； (4) 本地(l)、移动边缘计算(m)、云计算(c)，各平台平 均延迟${\tau _1},{\tau _2},{\tau _3}$，允许最大任务大小${B_1},{B_2},{B_3}$。 阶段2：选择任务卸载位置 计算(${\tau _{{R_i}}},{B_{{R_i}}}$)和(${\tau _1},{B_1}$)的欧式距离$D$ $D = \sqrt {{{({\tau _{{R_i}}} - {\tau _1})}^2} + {{({B_{{R_i}}} - {B_1})}^2}} ,P = 1$ for j=2, 3 do 计算(${\tau _{{R_i}}},{B_{{R_i}}}$)和(${\tau _j},{B_j}$)的欧式距离${d_j}$ if ${d_j} < D$ then $D = {d_j},P = j$ end if end for if P=1 任务卸载到本地 if P=2 or 3 进行阶段3。 阶段3：资源分配 if P=3 then 在云计算服务器中计算任务${R_i}$ end if if P=2 then for 每次迭代 do 随机选择一个状态${s_t}$ for 每一步 do 从状态${s_t}$的可能动作中随机选择动作$a$ 执行动作$a$，计算奖励$r$，进入下一状态$s'$ 计算 $q(s,a) \leftarrow r(s,a) + \gamma \cdot \max [q(s',a')]$ 更新状态$s \leftarrow s'$ until ${{Q}}$矩阵稳定 end for end for end if 
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