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基于压缩动量项的增量型ELM虚拟机能耗预测

邹伟东 夏元清

邹伟东, 夏元清. 基于压缩动量项的增量型ELM虚拟机能耗预测. 自动化学报, 2019, 45(7): 1290-1297. doi: 10.16383/j.aas.c180703
引用本文: 邹伟东, 夏元清. 基于压缩动量项的增量型ELM虚拟机能耗预测. 自动化学报, 2019, 45(7): 1290-1297. doi: 10.16383/j.aas.c180703
ZOU Wei-Dong, XIA Yuan-Qing. Virtual Machine Power Prediction Using Incremental Extreme Learning Machine Based on Compression Driving Amount. ACTA AUTOMATICA SINICA, 2019, 45(7): 1290-1297. doi: 10.16383/j.aas.c180703
Citation: ZOU Wei-Dong, XIA Yuan-Qing. Virtual Machine Power Prediction Using Incremental Extreme Learning Machine Based on Compression Driving Amount. ACTA AUTOMATICA SINICA, 2019, 45(7): 1290-1297. doi: 10.16383/j.aas.c180703

基于压缩动量项的增量型ELM虚拟机能耗预测

doi: 10.16383/j.aas.c180703
基金项目: 

中国博士后科学基金 2018M641217

国家重点研发计划 2018YFB1003700

国家自然科学基金 61836001

详细信息
    作者简介:

    邹伟东  北京理工大学自动化学院博士后.主要研究方向为极限学习机, 云数据中心优化调度管理.E-mail:zouweidong1985@163.com

    通讯作者:

    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

Virtual Machine Power Prediction Using Incremental Extreme Learning Machine Based on Compression Driving Amount

Funds: 

China Postdoctoral Science Foundation 2018M641217

National Key Research and Development Program of China 2018YFB1003700

National Natural Science Foundation of China 61836001

More Information
    Author Bio:

     Postdoctor at the School of Automation, Beijing Institute of Technology. His research interest covers extreme learning machine, cloud data center optimization scheduling and management

    Corresponding author: 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: 在基于基础设施即服务(Infrastructure as a service,IaaS)的云服务模式下,精准的虚拟机能耗预测,对于在众多物理服务器之间进行虚拟机调度策略的制定具有十分重要的意义.针对基于传统的增量型极限学习机(Incremental extreme learning machine,I-ELM)的预测模型存在许多降低虚拟机能耗预测准确性和效率的冗余节点,在现有I-ELM模型中加入压缩动量项将网络训练误差反馈到隐含层的输出中使预测结果更逼近输出样本,能够减少I-ELM的冗余隐含层节点,从而加快I-ELM的网络收敛速度,提高I-ELM的泛化性能.
    1)  本文责任编委 程龙
  • 图  1  基于压缩动量项的增量型极限学习机拓扑结构图

    Fig.  1  Topological structure of CDAI-ELM

    图  2  基于压缩动量项的增量型极限学习机算法流程图

    Fig.  2  Flow diagrams of algorithm for CDAI-ELM

    图  3  基于SVM、KELM、BLS和CDAI-ELM的虚拟机能耗预测曲线

    Fig.  3  Predicted curve for power of virtual machine based on SVM、KELM、BLS和CDAI-ELM

    图  4  4种模型预测结果

    Fig.  4  Predicted results of four models

    表  1  回归数据集

    Table  1  Datasets of regression

    回归数据集 属性 训练数据 测试数据
    Auto MPG 4 853 850
    Automobile 16 8 795 8 774
    BlogFeedback 281 530 500
    Housing 77 153 150
    NoisyOffice 128 468 300
    Facebook metrics 19 300 200
    SML2010 68 336 200
    wiki4HE 26 2 898 2 000
    UJIIndoorLoc 529 2 100 2 077
    YearPredictionMSD 90 2 800 3 075
    下载: 导出CSV

    表  2  相同期望误差下4种算法隐含层节点数方差的比较

    Table  2  Variance of number of hidden layer node for four algorithms under same expected error

    回归数据集 期望误差 I-ELM CI-ELM EM-ELM CDAI-ELM
    Auto MPG 0.11 49.46 5.21 2.76 1.82
    Automobile 0.15 8.82 33.08 3.55 1.95
    BlogFeedback 0.2 28.56 24.87 2.50 1.58
    Housing 0.12 35.95 9.98 2.51 2.26
    NoisyOffice 0.08 46.81 6.28 2.18 1.72
    Facebook metrics 0.06 28.87 10.02 2.28 1.46
    SML2010 0.21 36.89 17.79 2.82 2.31
    wiki4HE 0.13 32.81 8.19 2.88 2.14
    UJIIndoorLoc 0.09 50.71 9.67 3.07 2.51
    YearPredictionMSD 0.08 51.21 12.13 3.51 2.87
    下载: 导出CSV

    表  3  4种算法的测试误差和方差比较

    Table  3  Comparison result of testing error and variance for four algorithms

    回归数据集 I-ELM CI-ELM EM-ELM CDAI-ELM
    误差 方差 误差 方差 误差 方差 误差 方差
    Auto MPG 0.1021 0.0051 0.0952 0.0041 0.0953 0.0052 0.0811 0.0040
    Automobile 0.1323 0.0149 0.1302 0.0129 0.1301 0.0118 0.1257 0.0107
    BlogFeedback 0.1896 0.0121 0.1882 0.0123 0.1712 0.0108 0.1822 0.0109
    Housing 0.1017 0.0064 0.1008 0.0061 0.0985 0.0051 0.0973 0.0061
    NoisyOffice 0.0511 0.0039 0.0481 0.0034 0.0401 0.0029 0.0392 0.0023
    Facebook metrics 0.0642 0.0058 0.0581 0.0041 0.0581 0.0018 0.0585 0.0023
    SML2010 0.1555 0.0158 0.1502 0.0129 0.1461 0.0078 0.1452 0.0074
    wiki4HE 0.1592 0.0311 0.1522 0.0302 0.1468 0.0031 0.1511 0.0051
    UJIIndoorLoc 0.1315 0.0102 0.1291 0.0091 0.1278 0.0072 0.1116 0.0059
    YearPredictionMSD 0.0912 0.0041 0.0902 0.0039 0.0903 0.0040 0.0868 0.0031
    下载: 导出CSV

    表  4  4种算法训练时间的比较(s)

    Table  4  Comparison result of training time for four algorithms (s)

    回归数据集 I-ELM CI-ELM EM-ELM CDAI-ELM
    Auto MPG 0.0124 0.0073 0.0061 0.0052
    Automobile 0.0272 0.0171 0.0182 0.0088
    BlogFeedback 0.0469 0.0391 0.0236 0.0151
    Housing 0.0391 0.0119 0.0182 0.0120
    NoisyOffice 0.0411 0.0051 0.0083 0.0071
    Facebook metrics 0.481 0.0179 0.0171 0.0159
    SML2010 0.0218 0.0107 0.0081 0.0059
    wiki4HE 0.0089 0.0081 0.0298 0.0297
    UJIIndoorLoc 0.0471 0.0091 0.0288 0.0272
    YearPredictionMSD 0.0301 0.0297 0.0271 0.0197
    下载: 导出CSV

    表  5  4种模型训练时间(s)

    Table  5  Training time of four models (s)

    预测模型 训练时间(s)
    SVM 3.4788
    BLS 0.9828
    KELM 1.06
    CDAI-ELM 0.5772
    下载: 导出CSV
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  • 收稿日期:  2018-11-05
  • 录用日期:  2019-03-08
  • 刊出日期:  2019-07-20

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