Research Article Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration Minal Patel, 1 Sanjay Chaudhary, 2 and Sanjay Garg 3 1 Computer Engineering Department, A. D. Patel Institute of Technology, New Vallabh Vidhyanagar, Post Box 52, Vitthal Udyognagar, Anand District, Gujarat 388121, India 2 Institute of Engineering & Technology (IET), Ahmedabad University, Ahmedabad, Gujarat, India 3 Institute of Technology, Nirma University, Ahmedabad, Gujarat, India Correspondence should be addressed to Minal Patel; mppatel.adit@gmail.com Received 20 November 2015; Revised 9 February 2016; Accepted 21 March 2016 Academic Editor: Christos Bouras Copyright © 2016 Minal Patel et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Service can be delivered anywhere and anytime in cloud computing using virtualization. Te main issue to handle virtualized resources is to balance ongoing workloads. Te migration of virtual machines has two major techniques: (i) reducing dirty pages using CPU scheduling and (ii) compressing memory pages. Te 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. Te time series is generated with transferring of memory pages iteratively. Here, two diferent regression based models of time series are proposed. Te frst model is developed using statistical probability based regression model and it is based on ARIMA (autoregressive integrated moving average) model. Te second one is developed using statistical learning based regression model and it uses SVR (support vector regression) model. Tese models are tested on real data set of Xen to compute downtime, total number of pages transferred, and total migration time. Te 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. 1. Introduction Te market of cloud service is globally increasing that includes infrastructure services too. In the computer technol- ogy, infrastructure based computing is considered as abstrac- tions of the hardware like server, storage and networks, and so forth. Te user is able to use the infrastructure by renting instances of a virtual machine. Te user is not allowed to manage underneath cloud infrastructure but has the control over operating system (OS) and storage and user can also deploy various applications. Virtualization is the key technology for cloud to run a virtual set of resources. In Xen, two major types of virtualization are available [1, 2]: (i) full virtualization and (ii) paravirtualization. Live migration is the procedure to migrate VM based on computation algorithm using either precopy approach or postcopy approach. Virtual machine monitor (VMM) program is able to perform live migration on the organization’s public, private, and hybrid cloud [1]. In this paper, live migration of VMs is confgured using memory based migration that can transfer states of memory during iterative process. Te precopy is the most feasible and robust approach used for migration of VM in diferent envi- ronments [3, 4]. Performance of live migration is measured by two main key parameters: downtime (time for which a service is interrupted) and total migration period (time used to copy VM from a source to the desired destination). Te precopy based live migration has been successfully applied using various techniques including compression, CPU scheduling, and time series based analysis. In our proposed work, precopy algorithm is adopted to handle migration of dirty pages using time series predictions mod- els. Forecasting analysis is performed based on past and future values using time series. Time series modeling and forecasting are essential for practical applications. Stochastic based models are the basic models, which are used with time series analysis [5]. Tese models are being applied to calculate the accuracy of time series for forecasting analysis Hindawi Publishing Corporation Journal of Engineering Volume 2016, Article ID 3061674, 9 pages http://dx.doi.org/10.1155/2016/3061674