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基于深度强化学习的多用户计算卸载优化模型和算法

李志华 余自立

李志华, 余自立. 基于深度强化学习的多用户计算卸载优化模型和算法[J]. 电子与信息学报, 2024, 46(4): 1321-1332. doi: 10.11999/JEIT230445
引用本文: 李志华, 余自立. 基于深度强化学习的多用户计算卸载优化模型和算法[J]. 电子与信息学报, 2024, 46(4): 1321-1332. doi: 10.11999/JEIT230445
LI Zhihua, YU Zili. A Multi-user Computation Offloading Optimization Model and Algorithm Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1321-1332. doi: 10.11999/JEIT230445
Citation: LI Zhihua, YU Zili. A Multi-user Computation Offloading Optimization Model and Algorithm Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1321-1332. doi: 10.11999/JEIT230445

基于深度强化学习的多用户计算卸载优化模型和算法

doi: 10.11999/JEIT230445
基金项目: 工业和信息化部智能制造项目(ZH-XZ-180004),中央高校基本科研业务费专项资金(JUSRP211A41, JUSRP42003)
详细信息
    作者简介:

    李志华:男,教授,硕士生导师,研究方向为边缘计算、云计算与云数据中心理论、大数据挖掘、计算成像、信息安全等

    余自立:男,硕士生,研究方向为边缘计算

    通讯作者:

    李志华 zhli@jiangnan.edu.cn

  • 中图分类号: TN929.5;TP181

A Multi-user Computation Offloading Optimization Model and Algorithm Based on Deep Reinforcement Learning

Funds: The Ministry of Industry and Information Technology Manufacturing Project (ZH-XZ-180004), The Fundamental Research Funds for the Central Universities (JUSRP211A41, JUSRP42003)
  • 摘要: 在移动边缘计算(MEC)密集部署场景中,边缘服务器负载的不确定性容易造成边缘服务器过载,从而导致计算卸载过程中时延和能耗显著增加。针对该问题,该文提出一种多用户计算卸载优化模型和基于深度确定性策略梯度(DDPG)的计算卸载算法。首先,考虑时延和能耗的均衡优化建立效用函数,以最大化系统效用作为优化目标,将计算卸载问题转化为混合整数非线性规划问题。然后,针对该问题状态空间大、动作空间中离散和连续型变量共存,对DDPG深度强化学习算法进行离散化改进,基于此提出一种多用户计算卸载优化方法。最后,使用该方法求解非线性规划问题。仿真实验结果表明,与已有算法相比,所提方法能有效降低边缘服务器过载概率,并具有很好的稳定性。
  • 图  1  边缘网络架构

    图  2  没有对状态进行归一化处理或离散化动作空间时的性能对比

    图  3  不同算法随迭代次数变化时的性能对比

    图  4  不同算法随$s$变化时的性能对比

    图  5  不同UE数量下的时延对比图

    图  6  不同UE数量下的能耗对比图

    图  7  不同算法随UE数量变化时的性能对比

    表  1  变量符号及其含义

    变量符号 含义 变量符号 含义
    ${U_i}$ 用户设备编号 ${P_i}$ ${U_i}$的传输功率
    ${M_j}$ MEC服务器编号 ${\sigma ^2}$ 环境高斯白噪声
    $t$ 时隙编号 ${v_i}$ ${U_i}$的移动速度
    ${f_i}$ ${U_i}$的CPU总频率 ${x_{i,j}}$ ${U_i}$的关联策略
    $\varphi $ ${U_i}$的功率系数 $\lambda _i^t$ 任务$\varOmega _i^t$的卸载率
    $ D_i^t $ 任务大小 $F_i^t$ ${U_i}$分配到的计算资源
    $s$ 单位任务所需计算资源 ${F_{\max }}$ 单个边缘服务器总频率
    ${L_j}$ ${M_j}$的工作负载量
    下载: 导出CSV
    算法1 状态归一化算法
     输入: Unnormalized variables: ${{\boldsymbol{s}}_t} = ({\boldsymbol{p}}_1^t,\cdots ,{\boldsymbol{p}}_N^t,D_1^t,\cdots ,D_N^t,L_1^t,\cdots ,L_M^t)$, Scale factors: $\rho = ({\rho _x},{\rho _y},{\rho _w},{\rho _l})$
     输出: Normalized variables: $ ({{\boldsymbol{p}}'}_1^t,\cdots ,{{\boldsymbol{p}}'}_N^t,{D'}_1^t,\cdots ,{D'}_N^t,{L'}_1^t,\cdots ,{L'}_M^t) $
     (1) ${x'}_i^t = x_i^t*{\rho _x},\forall i$, ${y'}_i^t = y_i^t*{\rho _y},\forall i$, ${D'}_i^t = D_i^t*{\rho _w},\;\forall i$, ${L'}_j^t = L_j^t*{\rho _l},\:\forall j$ //*对状态进行归一化处理
     (2) return ${\hat s_t} = ({{\boldsymbol{p}}'}_1^t,\cdots ,{{\boldsymbol{p}}'}_N^t,{D'}_1^t,\cdots ,{D'}_N^t,{L'}_1^t,\cdots ,{L'}_M^t)$
    下载: 导出CSV
    算法2 动作编码算法
     输入: $a$ //*连续动作
     输出: ${a_{{\text{dis}}}}$ //*对应的离散编码
     (1) ${a_{{\text{num}}}} = K$
     (2) ${a_{\min }} = 0,{a_{\max }} = 1$
     (3) $ {\varDelta _a} = ({a_{\max }} - {a_{\min }})/({a_{{\text{num}}}} - 1) $
     (4) for each $a$ do
     (5) $a' = \left\lfloor {\dfrac{{a - {a_{\min }}}}{{{\varDelta _a}}}} \right\rfloor $ //*动作编码
     (6) ${a_{{\text{dis}}}} = \max (0,\min ({a_{{\text{num}}}} - 1,a'))$
     (7) end for
     (8) return ${a_{{\text{dis}}}}$
    下载: 导出CSV
    算法3 多用户计算卸载优化方法
     输入: Actor learning rate ${\alpha _{{\mathrm{Actor}}}}$, critic learning rate ${\alpha _{{\mathrm{Critic}}}}$, Soft update factor $\tau $.
     输出:$a,Q$ //*卸载决策(任务卸载率、分配的计算资源和关联策略),卸载效用
     (1) $\mu ({s_t}|{\theta _\mu })$$ \leftarrow $${\theta _\mu }$ and $Q({{\boldsymbol{s}}_t},{{\boldsymbol{a}}_t}|{\theta _Q})$$ \leftarrow $${\theta _Q}$, ${\theta '_\mu } \leftarrow {\theta _\mu }$ and ${\theta '_Q} \leftarrow {\theta _Q}$ //*初始化主网络和目标网络
     Initialize the experience replay buffer ${B_m}$ //*初始化经验重放缓冲区暂存经验元组
     (2) for episode=1 to $L$do
     (3)  Initialize system environment
     (4)  for slot=1 to $T$do
     (5)   ${\hat {\boldsymbol{s}}_t} \leftarrow $ SN(${{\boldsymbol{s}}_t},\rho $) //*调用算法1对状态${{\boldsymbol{s}}_t}$预处理
     (6)   Get the action from equation 式(25)
     (7)   ${{\boldsymbol{a}}'_t} \leftarrow $AE(${{\boldsymbol{a}}_t}$) //*调用算法2离散化动作
     (8)   perform action ${{\boldsymbol{a}}'_t}$and observer next state ${{\boldsymbol{s}}_{t + 1}}$, Get reward with equation 式(19)
     (9)   ${\hat {\boldsymbol{s}}_{t + 1}} \leftarrow $SN(${{\boldsymbol{s}}_{t + 1}},\rho $) //*调用算法1对状态${{\boldsymbol{s}}_{t + 1}}$预处理
     (10)   if ${B_m}$is not full then
     (11)    Store transition $({{\boldsymbol{s}}_t},{{\boldsymbol{a}}_t},{r_t},{\hat {\boldsymbol{s}}_{t + 1}})$ in replay buffer ${B_m}$
     (12)   else
     (13)    Randomly sample a mini-batch from ${B_m}$
     (14)    Calculate target value ${y_t}$ with equation 式(21)
     (15)    Use equation 式(20) to minimize the loss and update the ${\theta _Q}$
     (16)    Update the ${\theta _\mu }$ by the sampled policy gradient with equation 式(22)
     (17)    Soft update the $ {\theta '_\mu } $ and ${\theta '_Q}$ according to equation 式(23) and 式(24)
     (18)   end if
     (19) end for
     (20) Use equation 式(15) to get offloading utility $Q$
     (21) end for
     (22) return $a,Q$
    下载: 导出CSV

    表  2  仿真参数设置

    符号 定义
    $B$ 40 信道总带宽(MHz)
    $N$ {5,10,20,30,40} 用户设备数
    $K$ 5 MEC服务器个数
    ${v_i}$ [0,5] ${U_i}$的移动速度(m/s)
    ${P_i}$ 100 ${U_i}$的传输功率(mW)
    ${\sigma ^2}$ –100 环境高斯白噪声(dBm)
    ${f_i}$ 0.5 ${U_i}$的CPU总频率(GHz)
    ${F_{\max }}$ 10 单个MEC服务器总频率(GHz)
    $\varphi $ 10–26 功率系数
    $D_i^t$ (1.5,2) 任务$\varOmega _i^t$的大小(Mbit)
    $s$ 500 所需计算资源(cycle/bit)
    下载: 导出CSV

    表  3  不同${\beta _{\mathrm{t}}}$和${\beta _{\mathrm{e}}}$下的实验结果

    ${\beta _{\rm t}}$和${\beta _{\rm e}}$的取值 时延(s) 能耗(J) 平均卸载效用
    ${\beta _{\rm t}} = 0.1,{\beta _{\rm e}} = 0.9$ 369 394 0.528
    ${\beta _{\rm t}} = 0.2,{\beta _{\rm e}} = 0.8$ 343 412 0.534
    ${\beta _{\rm t}} = 0.3,{\beta _{\rm e}} = 0.7$ 337 431 0.544
    ${\beta _{\rm t}} = 0.4,{\beta _{\rm e}} = 0.6$ 321 441 0.549
    ${\beta _{\rm t}} = 0.5,{\beta _{\rm e}} = 0.5$ 308 456 0.554
    ${\beta _{\rm t}} = 0.6,{\beta _{\rm e}} = 0.4$ 293 464 0.575
    ${\beta _{\rm t}} = 0.7,{\beta _{\rm e}} = 0.3$ 277 479 0.578
    ${\beta _{\rm t}} = 0.8,{\beta _{\rm e}} = 0.2$ 256 488 0.613
    ${\beta _{\rm t}} = 0.9,{\beta _{\rm e}} = 0.1$ 245 511 0.628
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-18
  • 修回日期:  2023-11-03
  • 网络出版日期:  2023-11-14
  • 刊出日期:  2024-04-24

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