Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration
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...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/3061674 |
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| author | Minal Patel Sanjay Chaudhary Sanjay Garg |
| author_facet | Minal Patel Sanjay Chaudhary Sanjay Garg |
| author_sort | Minal Patel |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a5c8ec11d9e94f6f9cabc34885e9552f |
| institution | Kabale University |
| issn | 2314-4904 2314-4912 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-a5c8ec11d9e94f6f9cabc34885e9552f2025-08-20T03:38:48ZengWileyJournal of Engineering2314-49042314-49122016-01-01201610.1155/2016/30616743061674Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine MigrationMinal Patel0Sanjay Chaudhary1Sanjay Garg2Computer Engineering Department, A. D. Patel Institute of Technology, New Vallabh Vidhyanagar, Post Box 52, Vitthal Udyognagar, Anand District, Gujarat 388121, IndiaInstitute of Engineering & Technology (IET), Ahmedabad University, Ahmedabad, Gujarat, IndiaInstitute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaService 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.http://dx.doi.org/10.1155/2016/3061674 |
| spellingShingle | Minal Patel Sanjay Chaudhary Sanjay Garg Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration Journal of Engineering |
| title | Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration |
| title_full | Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration |
| title_fullStr | Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration |
| title_full_unstemmed | Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration |
| title_short | Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration |
| title_sort | machine learning based statistical prediction model for improving performance of live virtual machine migration |
| url | http://dx.doi.org/10.1155/2016/3061674 |
| work_keys_str_mv | AT minalpatel machinelearningbasedstatisticalpredictionmodelforimprovingperformanceoflivevirtualmachinemigration AT sanjaychaudhary machinelearningbasedstatisticalpredictionmodelforimprovingperformanceoflivevirtualmachinemigration AT sanjaygarg machinelearningbasedstatisticalpredictionmodelforimprovingperformanceoflivevirtualmachinemigration |