Abstract
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.
Highlights
The market of cloud service is globally increasing that includes infrastructure services too
The learning based regression models are mainly based on linear discriminant analysis (LDA), neural networks (NNs), and support vector machine (SVM), which have their mechanism to work on time series based analysis [8]
ARIMA model is useful in wide variety of applications for prediction of data
Summary
The market of cloud service is globally increasing that includes infrastructure services too. Migration of VM is proposed using modified precopy algorithm based on two different regression models. The learning based regression models are mainly based on linear discriminant analysis (LDA), neural networks (NNs), and support vector machine (SVM), which have their mechanism to work on time series based analysis [8]. This method is used in various applications of statistics, pattern recognition, and machine learning to find a linear combination of features based on classification Another regression based model, artificial neural networks (ANNs), has few models which are very promising to work on time series models. SVM is successfully applied to solve various real world problems [10,11,12,13] It has many new features and empirical performance compared to neural networks and LDA [5].
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