Live memory migration with matrix bitmap algorithm

Cui, Wentian; Song, Meina

doi:10.1109/sws.2010.5607439

Cited by 16 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

Section: Literature Surveymentioning

confidence: 99%

“…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].…”

Section: Literature Surveymentioning

confidence: 99%

“…Other methods of improved precopy algorithms [4,19] are based on either LRU or compression algorithms which have their limitations to handle dirty pages.…”

Section: Literature Surveymentioning

confidence: 99%

See 2 more Smart Citations

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

Patel¹,

Chaudhary

Garg

2016

Journal of Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Surveymentioning

confidence: 99%

“…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].…”

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

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

Patel¹,

Chaudhary

Garg

2016

Journal of Engineering

View full text Add to dashboard Cite

show abstract

“…When this write interrupt is higher than threshold than it is called high dirty page and high dirty page will be sent at last iteration for avoiding repeated sending of dirty pages. Cui et al [9] proposed matrix bitmap algorithm for efficient VM live migration. They improve the simple pre copy algorithm using multiple sampling during round.…”

Section: Related Workmentioning

confidence: 99%

“…do not focus on data Frequently updated pages of memory page bitmap [9] Frequently updated pages put together as bitmap and transfer those pages in last iteration, Total migration time and total transferred data gets reduced. Not suitable In WAN Time-series prediction technique [2] it recognized repeatedly updated high dirty pages in the past and transmit them in the last round of iteration fewer number of Iterations, a lesser amount of down time and migration time and smaller quantity pages transferred.…”

Section: Proposed Approachmentioning

confidence: 99%

Efficient Virtual Machine Migration in Cloud Computing

Desai¹,

Patel²

2015

2015 Fifth International Conference on Communication Systems and Network Technologies

View full text Add to dashboard Cite

Cloud computing allows users to migrate their calculations to distributed environment which will utilize more number of resources to complete execution quickly. Virtualization means multiple virtual machines (VM) on single physical machine.During process of migration, VM move one physical machine to another. In offline migration, process is stopped till the virtual machine can continue on target machine, while in live migration process can execute without interrupted. Live migration is a migration during which the VM seems to be responsive all the time from clients' perspective. In Data centre, live migration of virtual machine performs important role. Live migration has been extensively used in load balancing, energy reduction and dynamic resizing to increase availability and hardware maintenance. Which cause transferring huge number of unnecessary memory pages resulting into increase in the total migration time and downtime. Proposed approach of modifying optimized pre-copy will reduce unnecessary transfer of pages and its combination with Characteristic Based Compression (CBC) algorithm will handle two factors viz. (i) total migration time and (ii) total downtime and make migration process more effective.

show abstract

Research Challenges in Pre‐Copy Virtual Machine Migration in Cloud Environment

Nirmala¹,

Vengatesh²

2022

The Internet of Medical Things (IoMT)

View full text Add to dashboard Cite

Live memory migration with matrix bitmap algorithm

Cited by 16 publications

References 8 publications

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

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

Efficient Virtual Machine Migration in Cloud Computing

Research Challenges in Pre‐Copy Virtual Machine Migration in Cloud Environment

Contact Info

Product

Resources

About