| 农业云视频平台虚拟机负荷预测半监督偏最小二乘法模型 |
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| 引用本文： | 高万林,胡慧,徐东波,张港红.农业云视频平台虚拟机负荷预测半监督偏最小二乘法模型[J].农业工程学报,2017,33(Z1):225-230. |
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| 作者姓名： | 高万林 胡慧 徐东波 张港红 |
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| 作者单位： | 中国农业大学信息与电气工程学院,北京,100083 |
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| 基金项目： | National Spark Program Key Project (2015GA600002) |
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| 摘 要： | 为了优化基础设施资源的效率,农业云视频平台虚拟机布局算法需要了解虚拟机当前和未来的资源工作效率,尽可能准确地预知下一步工作,如服务部署,虚拟机的部署、迁移或停止.然而,通常在预测中使用的样本非常小,可用于分析的数据有限.因此,该文研究设计了一个考虑时间因素,基于小数据集学习的滑动窗口模型.此外,鉴于现有的预测算法仍然有很大的改进误差率的空间,该文中采用基于滑动窗口与最小二乘法和半监督学习的数学方法相结合,提出了一种半监督偏最小二乘法(semi-supervised partial least squares,SS-PLS)的方法来计算上述预测.该文中,分析了在虚拟机使用SS-PLS负荷预测的可行性和优势.试验结果表明,基于滑动窗口模型结合SS-PLS,使得预测精度有了显着的改善,即均方根误差为1.77786,平均绝对误差是1.3312,平均绝对误差百分比为0.23836,三者的增量分别5.47%、6.37%、6.12%.该研究可为云平台中虚拟机资源管理和优化提供一种参考方法.
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| 关 键 词： | 负荷 预测 算法 半监督学习 虚拟机 偏最小二乘法 滑窗 |
| 收稿时间： | 2016/12/14 0:00:00 |
| 修稿时间： | 2017/1/15 0:00:00 |
Virtual machine load prediction model for agricultural cloud video platform based on semi-supervised partial least squares |
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| Gao Wanlin,Hu Hui,Xu Dongbo and Zhang Ganghong.Virtual machine load prediction model for agricultural cloud video platform based on semi-supervised partial least squares[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(Z1):225-230. |
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| Authors: | Gao Wanlin Hu Hui Xu Dongbo and Zhang Ganghong |
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| Institution: | College of Information and Electrical Engineering, China Agricultural University, Beijing 100083 China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083 China,College of Information and Electrical Engineering, China Agricultural University, Beijing 100083 China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083 China,College of Information and Electrical Engineering, China Agricultural University, Beijing 100083 China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083 China and College of Information and Electrical Engineering, China Agricultural University, Beijing 100083 China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083 China |
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| Abstract: | Abstract: In order to optimize the infrastructure resource of agricultural cloud video platform efficiently, the virtual machine (VM) placement algorithms need to know the current and future efficiency of VM resource as accurately as possible for potential actions, such as service deployment, VM deployment, migration or cancellation. However, the data available for analysis are limited, as the samples used for prediction are usually very small. In the study, a sliding window model that considering time factor was designed to learn from small set of data. More importantly, the existing prediction algorithms still have much room to reduce the error. So a sliding window based mathematical method was provided to calculate the aforementioned forecasts, which was combined with PLS and semi-supervised learning (semi-supervised partial least squares, SS-PLS). The feasibility and advantages were analyzed in VM load forecasting based on SS-PLS method. Compared with auto regression moving average (ARMA), experimental results showed that the sliding window based model combined with SS-PLS made noticeable improvements to the forecasting accuracies, with root mean square error (RMSE) improved 5.47% to 1.777 86, mean absolute error (MAE) improved 6.37% to 1.331 2, and mean absolute percentage error (MAPE) improved 6.12% to 0.238 36, respectively. The results demonstrated that the proposed algorithm based on semi-supervised partial least squares is accurate in forecasting virtual machine load is effective in terms of the forecast accuracy. |
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| Keywords: | load forecasting algorithms semi-supervised learning virtual machine partial least squares sliding-windows |
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