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• 

  图 1  网络模型

  下载: 全尺寸图片 幻灯片
  

  图 2  总成本与距离关系图

  下载: 全尺寸图片 幻灯片
  

  图 3  总成本与车辆数关系图

  下载: 全尺寸图片 幻灯片
  

  图 4  总成本与车辆数及本地计算能力关系图

  下载: 全尺寸图片 幻灯片
  

  图 5  总成本与任务数据大小关系图

  下载: 全尺寸图片 幻灯片
  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

  下载: 导出CSV
•