基于压缩因子的宽度学习系统的虚拟机性能预测

邹伟东; 夏元清

doi:10.16383/j.aas.c190307

基于压缩因子的宽度学习系统的虚拟机性能预测

doi: 10.16383/j.aas.c190307

邹伟东^1,,
夏元清^1,

1.
北京理工大学自动化学院北京 100081

基金项目: 国家重点研发计划(2018YFB1003700), 国家自然科学基金(61836001)资助

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. 主要研究方向为宽度学习系统, 云数据中心优化调度管理. E-mail: zouweidong1985@163.com

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

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出版历程
- 收稿日期: 2019-04-17
- 录用日期: 2019-07-30
- 网络出版日期: 2022-02-18
- 刊出日期: 2022-03-25

Virtual Machine Performance Prediction Using Broad Learning System Based on Compression Factor

ZOU Wei-Dong^1
,,
XIA Yuan-Qing^1
,

1.
School of Automation, Beijing Institute of Technology, Beijing 100081

Funds: Supported by National Key Research and Development Program of China (2018YFB1003700) and National Natural Science Foundation of China (61836001)

More Information

Author Bio:
ZOU Wei-Dong　Postdoctoral fellow at the School of Automation, Beijing Institute of Technology. His research interest covers broad learning system, cloud data center optimization scheduling and management

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, and flight control and networked cooperative control for integration of space, air and earth. Corresponding author of this paper

摘要

摘要: 在基于基础设施即服务的云服务模式下, 精准的虚拟机性能预测, 对于用户在众多资源提供商之间进行虚拟机租用策略的制定具有十分重要的意义. 针对基于宽度学习系统(Broad learning system, BLS)的预测模型存在许多降低虚拟机性能预测准确性和效率的冗余节点, 通过引入压缩因子, 构建基于压缩因子的宽度学习系统, 使预测结果更逼近输出样本, 能够减少BLS的冗余特征节点与增强节点, 从而加快BLS的网络收敛速度, 提高BLS的泛化性能.
- 虚拟机性能预测 /
- 宽度学习系统 /
- 压缩因子 /
- 网络收敛速度 /
- 泛化性能
Abstract: In cloud service models which is based on IaaS, from the user＇s perspective, how to accurately predict performance of virtual machine is very important for making renting strategy of virtual machines between many physical servers. However, broad learning system (BLS) includes too many redundant feature nodes and enhancement nodes, resulting in decreased efficiency and accuracy of virtual machine performance prediction. Connecting compression factor to BLS, the paper builds intelligent prediction model of BLS based on compression factor (CF-BLS), and uses the model for predicting virtual machine performance.
- Performance prediction of virtual machine /
- broad learning system (BLS) /
- compression factor /
- convergence rate of network /
- generalization performance

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图 1 Combined Cycle Power Plant数据集对CF-BLS与BLS算法的RMSE和PCC曲线

Fig. 1 Curves for RMSE and PCC of Combined Cycle Power Plant dataset based on CF-BLS and BLS

下载: 全尺寸图片幻灯片

图 4 Wine Quality数据集对CF-BLS与BLS算法的RMSE和PCC曲线

Fig. 4 Curves for RMSE and PCC of Wine Quality dataset based on CF-BLS and BLS

下载: 全尺寸图片幻灯片

图 2 Energy Efficiency数据集对CF-BLS与BLS算法的RMSE和PCC曲线

Fig. 2 Curves for RMSE and PCC of Energy Efficiency dataset based on CF-BLS and BLS

下载: 全尺寸图片幻灯片

图 3 Forest Fires数据集对CF-BLS与BLS算法的RMSE和PCC曲线

Fig. 3 Curves for RMSE and PCC of Forest Fires dataset based on CF-BLS and BLS

下载: 全尺寸图片幻灯片

图 5 两种模型的预测结果(100个增强节点)

Fig. 5 Predicted results of two model (100 enhancement nodes)

下载: 全尺寸图片幻灯片

图 6 两种模型的预测结果(100个特征节点)

Fig. 6 Predicted results of two model (100 feature nodes)

下载: 全尺寸图片幻灯片

图 7 基于CF-BLS, BLS, FBLS和HELM的虚拟机性能预测曲线

Fig. 7 Predicted curves for performance of virtual machine based on CF-BLS, BLS, FBLS, and HELM

下载: 全尺寸图片幻灯片

图 8 CF-BLS, BLS, FBLS和HELM模型的预测结果

Fig. 8 Predicted results of CF-BLS, BLS, FBLS, and HELM

下载: 全尺寸图片幻灯片

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

Combined cycle power plant 4 4795 4773
Energy efficiency 8 468 300
Forest fires 13 259 258
Wine quality 12 2898 2000

下载: 导出CSV

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