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基于多维度特征融合的云工作流任务执行时间预测方法

李慧芳 黄姜杭 徐光浩 夏元清

李慧芳, 黄姜杭, 徐光浩, 夏元清. 基于多维度特征融合的云工作流任务执行时间预测方法. 自动化学报, 2021, x(x): 1−12
引用本文: 李慧芳, 黄姜杭, 徐光浩, 夏元清. 基于多维度特征融合的云工作流任务执行时间预测方法. 自动化学报, 2021, x(x): 1−12
Li Hui-Fang, Huang Jiang-Hang, Xu Guang-Hao, Xia Yuan-Qing. Multi-dimensional feature fusion based runtime prediction approach for cloud workflow tasks. Acta Automatica Sinica, 2021, x(x): 1−12
Citation: Li Hui-Fang, Huang Jiang-Hang, Xu Guang-Hao, Xia Yuan-Qing. Multi-dimensional feature fusion based runtime prediction approach for cloud workflow tasks. Acta Automatica Sinica, 2021, x(x): 1−12

基于多维度特征融合的云工作流任务执行时间预测方法

基金项目: 国家重点研发计划项目(2018YFB1003700), 国家自然科学基金项目(61836001)资助
详细信息
    作者简介:

    李慧芳:北京理工大学自动化学院副教授, 主要研究方向为Petri网, 工作流, 云计算, 任务调度, 故障诊断以及深度学习的应用. E-mail: huifang@bit.edu.cn

    黄姜杭:北京理工大学自动化学院硕士研究生. 主要研究方向为工作流, 云计算, 任务调度. E-mail: 3220190687@bit.edu.cn

    徐光浩:北京理工大学自动化学院硕士研究生. 主要研究方向为工作流, 云计算, 任务调度. E-mail: 3220200812@bit.edu.cn

    夏元清:北京理工大学自动化学院教授. 主要研究方向为云控制, 云数据中心优化调度管理, 智能交通, 模型预测控制, 自抗扰控制, 飞行器控制和空天地一体化网络协同控制. 本文通信作者. E-mail: xia_yuanqing@bit.edu.cn

  • 中图分类号: 10.16383/j.aas.c210123

Multi-Dimensional Feature Fusion based Runtime Prediction Approach for Cloud Workflow Tasks

Funds: Supported by the National Key Research and Development Program of China (2018YFB1003700), the National Natural Science Foundation of China (61836001)
More Information
    Author Bio:

    LI Hui-Fang Associate Professor at the School of Automation, Beijing Institute of Technology. Her research interests include Petri nets, Workflows, Cloud computing, Task scheduling, Fault diagnosis and applied deep learning

    HUANG Jiang-Hang Master student at the School of Automation, Beijing Institute of Technology. His research interests include Workflows, Cloud computing, Task scheduling

    XU Guang-Hao Master student at the School of Automation, Beijing Institute of Technology. His research interests include Workflows, Cloud computing, Task scheduling

    XIA Yuan-Qing Professor at the School of Automation, Beijing Institute of Technology. His research interest covers cloud control, cloud data center optimization scheduling and management, intelligent transportation, model predictive control, active disturbance rejection control, flight control and networked cooperative control for integration of space, air and earth. Corresponding author of this paper

  • 摘要: 任务执行时间估计是云数据中心环境下工作流调度的前提. 本文针对现有工作流任务执行时间预测方法缺乏类别型和数值型数据特征的有效提取问题, 提出了基于多维度特征融合的预测方法. 首先, 通过构建具有注意力机制的堆叠残差循环网络, 将类别型数据从高维稀疏的特征空间映射到低维稠密的特征空间, 以增强类别型数据的解析能力, 有效提取类别型特征. 其次, 采用极限梯度提升算法对数值型数据进行离散化编码, 通过对稠密空间的输入向量进行稀疏化处理, 提高了数值型特征的非线性表达能力. 在此基础上, 设计多维异质特征融合策略, 将所提取的类别型、数值型特征与样本的原始输入特征进行融合, 建立基于多维融合特征的预测模型, 实现了云工作流任务执行时间的精准预测. 为了验证本文方法的有效性和优越性, 我们在真实云数据中心集群数据集上进行了仿真实验. 结果表明相对于已有的基准算法, 本文方法具有较好的预测精度, 可用于大数据驱动的云工作流任务执行时间预测.
  • 图  1  基于多维度特征融合的云工作流任务执行时间预测模型

    Fig.  1  Multi-dimensional feature fusion based runtime prediction model for cloud workflow tasks

    图  2  基于SARR的类别型特征提取器

    Fig.  2  The SARR based Categorical feature extractor

    图  3  基于XGB的数值型特征提取器

    Fig.  3  The XGB based Numerical feature extractor

    图  4  不同方法的MAE

    Fig.  4  MAE comparisons among different methods

    图  6  不同方法的RMSLE

    Fig.  6  RMSLE comparisons among different methods

    图  7  不同方法的R2

    Fig.  7  R2 comparisons among different methods

    图  5  不同方法的RMSE

    Fig.  5  RMAE comparisons among different methods

    表  1  预测精度的差值

    Table  1  The difference of Prediction performance

    i$ \delta _i^{MAE}$$\delta _i^{RMSE} $$ \delta _i^{RMSLE}$$\delta _i^{R2} $
    DIN1.4391.8250.6790.006
    DCN0.2864.0430.0480.014
    DeepFM0.3731.8110.0430.009
    W&D0.8103.5760.1410.012
    TSA0.9426.4080.0300.025
    GBDT+LR1.2572.1430.1170.007
    下载: 导出CSV

    表  2  预测精度提升的比例(%)

    Table  2  The proportion of performance improvement (%)

    i$ \eta _i^{MAE}$$\eta _i^{RMSE} $$ \eta _i^{RMSLE}$$\eta _i^{R2} $
    DIN36.9422.0682.600.61
    DCN10.4336.9516.491.43
    DeepFM13.1818.8515.030.92
    W&D24.8034.1436.721.22
    TSA27.7248.1610.992.59
    GBDT+LR33.8523.7032.500.71
    下载: 导出CSV
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  • 收稿日期:  2021-02-25
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