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Contents:


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  2. Journal of Engineering
  3. Michael C. Mozer (Editor of TIme Series Prediction Comparison with Linear, HMM, and SVM Models)

The analysis of different workloads is given in [ 14 ], which has been dealing with the methods of reducing dirty pages. Live migration is evaluated based on stop-and-copy condition of Xen. Downtime and total migration time are evaluated based on methods of stunning rouge processes and freeing page cache pages. In [ 15 ], performance and energy modeling for live migration of virtual machines are discussed. The base model is derived to evaluate network traffic and downtime.

LRU algorithm [ 16 ] is performed on the process migration based writable working set prediction algorithm. The algorithm is evaluated on different workloads to compare results. The proposed algorithm is able to reduce total data transferred during migration and total migration time over Xen. Reuse distance algorithm [ 18 ] keeps the track of modified pages using two different arrays: Two types of memory based migration system are used for iterative process analysis: In generic process, workloads have been able to deal with those pages which are not being modified frequently.

This algorithm also works efficiently with memory intensive applications.

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Cui and Song presented matrix bitmap algorithm [ 19 ], which works on the policy to predict next page based on bitmap structure. This structure will give the statistics for pages that are to be either sent or not sent in the current iteration. The survey of live virtual machine migration is shown in [ 20 ].


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A new technique for efficient live migration of multiple virtual machines is developed based on queuing models [ 21 ]. Time series based precopy approach [ 22 ] and Kalman filter [ 23 ] are designed on past observations and prediction of future data. In these approaches, frequently updated pages are sent in the last round and data are being computed for specific time interval to check the state of memory pages. It has been observed that dirty pages analysis is the key issue to make effective migration.

For this, following are the issues to handle dirty pages effectively at the time of migration process: Basic methods are based on reduction of dirty pages using CPU scheduling [ 14 ]. These methods have their bottleneck for the performance of live migration systems [ 16 , 19 ]. Other methods of improved precopy algorithms [ 4 , 19 ] are based on either LRU or compression algorithms which have their limitations to handle dirty pages.

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Our proposed work is based on prediction using time series analysis given in [ 23 — 26 ]. These techniques are successfully applied to VM migration, including context based prediction CBP algorithm, hidden Markov model, and Kalman filter technique. In this section, the working of precopy algorithm has been described in detail. The precopy algorithm is modified by forecasting of pages that is known as improved precopy algorithm.

The migration of virtual machine can be considered to be consisting of two phases [ 14 ]. During the first phase, a certain number of iterations are taking place. The second phase is called service dead phase that suspends the virtual machine to copy all remaining dirty pages in the last iteration. After this phase, the migration process will be completed to start activities on migrated VM: Equations 1 and 2 represent migration time of single iteration and total migration time, respectively. The proposed framework based on Xen 4.

This framework is extended with additional module called prediction module. As the pages are to be predicted iteratively, both the number of pages to transfer and network traffic can be reduced. Stop-and-copy condition shown in Figure 1 is described below [ 27 ]: Until stop-and-copy condition is satisfied, migration process will calculate precopy time of each iteration given by 1.

When stop-and-copy condition is false, total migration time will be calculated by 2. The performance of live virtual machine migration depends on the following factors: Dirty rate and data rate are the two main parameters to improve the performance of any live migration system. In existing system of Xen, dirty pages are being transferred iteratively using simple LRU based technique which has its limitation to manage large reuse distance. Due to this mechanism, dirty pages are being sent repeatedly instead of sending them at last with their updated copy.

Figure 2 has been improved by us and the modification is described as follows. As per precopy algorithm, updated memory pages of VM are identified by shadow page table and this process sets a flag in dirty bitmap [ 17 ]. At the beginning of each iteration, the related bit value is sent to the migration module as shown in Figure 2.

Bitmaps and shadow page table entries are cleared in each next round. In the modified precopy algorithm, characteristics of pages are being measured using historical analysis. Pages are predicted using regression based model. Those predicted pages, which are likely to become dirty pages, are not being sent during current iteration and, this way, remaining pages are iteratively sent to the destination until stop-and-copy condition becomes false as shown in Figure 1.

In Figure 2 , migration module presented in Dom 0 is the main component that is used to perform a live migration of VMs. Prediction module is proposed in existing framework shown in Figure 2 with the following models: The accuracy of these models are tested based on the prediction of dirty pages mechanisms. Time series has attracted a research community for several decades due to its dynamic nature into modeling of data.

It collects data based on past observations, which is able to show the real world working model of a series [ 6 , 25 ]. Successful time series forecasting is the act of predicting future by analysis of past values. Due to the deterministic nature of Markov models and also limitations with regression models of LDA and neural networks NNs , the new regression based models have been proposed here.

ARIMA is built by three submodels: It is also very useful for seasonal time series based forecasting. It is also applied in many fields named regression, signal processing, estimation, and time series analysis. SVM is a practical approach for the structural risk minimization SRM principle, which has been shown to be superior to the conventional NN based learning algorithms [ 5 ]. ARIMA model is performed with time series having stationary data and it is also used to forecast the training data. Time series [ 8 ] is defined as , where is a random variable over a set of data points and represents the time elapsed having values , and so forth.

It can be designed based on single variable or multiple variables and also based on continuous or discrete signal. ARIMA has three phases [ 8 ]: In identification phase, stationary series is identified first. The parameters of time series are estimated using the suitable order of ARIMA in second phase of model. Forecast function is used for prediction of various model fitting functions. The lowest AIC is considered to be closer to a real data. It is used to quantify the goodness of the statistical data. The pages, which are frequently dirtied, called high dirty pages have to stay in WWS.

Results are shown in Experiment 1. The program initially takes an input file in the CSV format to generate time series. Performance of live migration is measured by two main key parameters: The precopy based live migration has been successfully applied using various techniques including compression, CPU scheduling, and time series based analysis.

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In our proposed work, precopy algorithm is adopted to handle migration of dirty pages using time series predictions models. 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 ]. These models are being applied to calculate the accuracy of time series for forecasting analysis to solve real world problems.

This model is classified by ARIMA , where is known for autoregressive components, is used for integrated components, and is known for moving average components. In this paper, 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. It is used for classification and regression based modeling. The other regression based model named SVM [ 9 ], developed by Vapnik, is a powerful technique for classification, regression, and outlier detection with an intuitive model representation.

SVM is successfully applied to solve various real world problems [ 10 — 13 ]. It has many new features and empirical performance compared to neural networks and LDA [ 5 ]. The main objective of this paper is to forecast memory pages which get dirtied during ongoing iterations. The regressive analyses using statistical prediction model and learning based prediction model have the ability to get optimal accuracy for live migration system.

Michael C. Mozer (Editor of TIme Series Prediction Comparison with Linear, HMM, and SVM Models)

We need to observe that statistical learning model has higher accuracy than statistical probability model. The major contributions of our proposed live migration system are stated below: The migration cost will be estimated based on these parameters. Section 2 provides the detailed analysis of live migration techniques.

In Section 3 , the framework of improved precopy algorithm of Xen is proposed. Finally, in Section 5 , experiments and results are shown using these models. Live migration algorithms are surveyed in this section. Time series based live migration algorithms are also recently applied in this area. Statistical, probabilistic, and learning based regression models are new dimension to enhance live migration systems. The analysis of different workloads is given in [ 14 ], which has been dealing with the methods of reducing dirty pages.

Live migration is evaluated based on stop-and-copy condition of Xen. Downtime and total migration time are evaluated based on methods of stunning rouge processes and freeing page cache pages. In [ 15 ], performance and energy modeling for live migration of virtual machines are discussed. The base model is derived to evaluate network traffic and downtime. LRU algorithm [ 16 ] is performed on the process migration based writable working set prediction algorithm.

The algorithm is evaluated on different workloads to compare results. The proposed algorithm is able to reduce total data transferred during migration and total migration time over Xen. Reuse distance algorithm [ 18 ] keeps the track of modified pages using two different arrays: Two types of memory based migration system are used for iterative process analysis: In generic process, workloads have been able to deal with those pages which are not being modified frequently.

This algorithm also works efficiently with memory intensive applications. Cui and Song presented matrix bitmap algorithm [ 19 ], which works on the policy to predict next page based on bitmap structure. This structure will give the statistics for pages that are to be either sent or not sent in the current iteration. The survey of live virtual machine migration is shown in [ 20 ].

A new technique for efficient live migration of multiple virtual machines is developed based on queuing models [ 21 ]. Time series based precopy approach [ 22 ] and Kalman filter [ 23 ] are designed on past observations and prediction of future data.

In these approaches, frequently updated pages are sent in the last round and data are being computed for specific time interval to check the state of memory pages. It has been observed that dirty pages analysis is the key issue to make effective migration. For this, following are the issues to handle dirty pages effectively at the time of migration process: Basic methods are based on reduction of dirty pages using CPU scheduling [ 14 ].

These methods have their bottleneck for the performance of live migration systems [ 16 , 19 ]. Other methods of improved precopy algorithms [ 4 , 19 ] are based on either LRU or compression algorithms which have their limitations to handle dirty pages. Our proposed work is based on prediction using time series analysis given in [ 23 — 26 ]. These techniques are successfully applied to VM migration, including context based prediction CBP algorithm, hidden Markov model, and Kalman filter technique. In this section, the working of precopy algorithm has been described in detail.

The precopy algorithm is modified by forecasting of pages that is known as improved precopy algorithm. The migration of virtual machine can be considered to be consisting of two phases [ 14 ]. During the first phase, a certain number of iterations are taking place. The second phase is called service dead phase that suspends the virtual machine to copy all remaining dirty pages in the last iteration.

After this phase, the migration process will be completed to start activities on migrated VM: Equations 1 and 2 represent migration time of single iteration and total migration time, respectively. The proposed framework based on Xen 4. This framework is extended with additional module called prediction module. As the pages are to be predicted iteratively, both the number of pages to transfer and network traffic can be reduced.

Stop-and-copy condition shown in Figure 1 is described below [ 27 ]: Until stop-and-copy condition is satisfied, migration process will calculate precopy time of each iteration given by 1. When stop-and-copy condition is false, total migration time will be calculated by 2. The performance of live virtual machine migration depends on the following factors: Dirty rate and data rate are the two main parameters to improve the performance of any live migration system. In existing system of Xen, dirty pages are being transferred iteratively using simple LRU based technique which has its limitation to manage large reuse distance.

Due to this mechanism, dirty pages are being sent repeatedly instead of sending them at last with their updated copy. Figure 2 has been improved by us and the modification is described as follows. As per precopy algorithm, updated memory pages of VM are identified by shadow page table and this process sets a flag in dirty bitmap [ 17 ].

At the beginning of each iteration, the related bit value is sent to the migration module as shown in Figure 2. Three sets of data are examined including Google and Apple Inc. The results show that though the stock market and weather are still difficult to predict, the dairy industry can be predicted with a much greater degree of certainty. Furthermore the results indicate that with further research the financial industries and weather forecasters could viably include Support Vector Machines in their arsenal.