scholarly journals Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Minal Patel ◽  
Sanjay Chaudhary ◽  
Sanjay Garg
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Virtual Machines ◽  
Arima Model ◽  
Statistical Prediction ◽  
Support Vector ◽  
Virtual Machine Migration ◽  
Data Set ◽  
Support Vector Regression Model ◽  
Statistical Probability

Service can be delivered anywhere and anytime in cloud computing using virtualization. The main issue to handle virtualized resources is to balance ongoing workloads. The migration of virtual machines has two major techniques: (i) reducing dirty pages using CPU scheduling and (ii) compressing memory pages. The available techniques for live migration are not able to predict dirty pages in advance. In the proposed framework, time series based prediction techniques are developed using historical analysis of past data. The time series is generated with transferring of memory pages iteratively. Here, two different regression based models of time series are proposed. The first model is developed using statistical probability based regression model and it is based on ARIMA (autoregressive integrated moving average) model. The second one is developed using statistical learning based regression model and it uses SVR (support vector regression) model. These models are tested on real data set of Xen to compute downtime, total number of pages transferred, and total migration time. The ARIMA model is able to predict dirty pages with 91.74% accuracy and the SVR model is able to predict dirty pages with 94.61% accuracy that is higher than ARIMA.

Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system

2018 ◽  
Vol 233 (7) ◽  
pp. 701-714
Author(s):  
Yumei Liu ◽  
Ningguo Qiao ◽  
Congcong Zhao ◽  
Jiaojiao Zhuang ◽  
Guangdong Tian
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
High Speed ◽  
Support Vector ◽  
High Speed Train ◽  
Support Vector Regression Model ◽  
Vibration Time ◽  
Auto Regression

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.

scholarly journals Robust Support Vector Regression Model in the Presence of Outliers and Leverage Points

2017 ◽  
Vol 11 (8) ◽  
pp. 92
Author(s):  
Waleed Dhhan ◽  
Habshah Midi ◽  
Thaera Alameer
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Data Set ◽  
Support Vector Regression Model ◽  
Leverage Points ◽  
Linear Relationships ◽  
Linear And Nonlinear ◽  
Abnormal Points ◽  
Robust Support Vector Regression

Support vector regression is used to evaluate the linear and non-linear relationships among variables. Although it is non-parametric technique, it is still affected by outliers, because the possibility to select them as support vectors. In this article, we proposed a robust support vector regression for linear and nonlinear target functions. In order to carry out this goal, the support vector regression model with fixed parameters is used to detect and minimize the effects of abnormal points in the data set. The efficiency of the proposed method is investigated by using real and simulation examples.

Time series forecasting by a seasonal support vector regression model

2010 ◽  
Vol 37 (6) ◽  
pp. 4261-4265 ◽  
Author(s):  
Ping-Feng Pai ◽  
Kuo-Ping Lin ◽  
Chi-Shen Lin ◽  
Ping-Teng Chang
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Support Vector Regression ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Support Vector Regression Model

scholarly journals Comparing models to forecast cargo volume at port terminals

2021 ◽  
Vol 19 (3) ◽  
pp. 238-249
Author(s):  
M.R. Nieto ◽  
R. B. Carmona Benitez ◽  
J.N. Martinez
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Explanatory Variables ◽  
San Pedro ◽  
Machine Learning Model ◽  
Using Data ◽  
One Machine

Economic growth has a direct link with the volume of cargo at port terminals. To encourage growth, investment decisions on infrastructure are required that can be performed by the development of econometric models. We compare three time-series models and one machine-learning model to estimate and forecast cargo volume. We apply an ARIMA+GARCH+Bootstrap, a multiplicative Holt-Winters, a support vector regression model, and a time-series model with explanatory variables ARIMAX. The models forecast cargo through the ports of San Pedro using data from 2008 to 2016. The database contains imports and exports of bulk, container, reefer, and ro-ro cargo. Results show that the multiplicative Holt-Winters model is the best method to forecast imports and exports of bulk cargo, while the support vector regression model is the best method to forecast imports and exports of container, reefer, and ro-ro cargo. The Diebold-Mariano Test, the RMSE metric, and the MAPE metric validate the results.

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