An overview of virtual machine live migration techniques

In a cloud computing the live migration of virtual machines shows a process of moving a running virtual machine from source physical machine to the destination, considering the CPU, memory, network, and storage states. Various performance metrics are tackled such as, downtime, total migration time, performance degradation, and amount of migrated data, which are affected when a virtual machine is migrated. This paper presents an overview and understanding of virtual machine live migration techniques, of the different works in literature that consider this issue, which might impact the work of professionals and researchers to further explore the challenges and provide optimal solutions.

Download Full-text

Live Migration for Virtual Machine Based on Kalman Prediction of Dirty Pages

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.668-669.1363 ◽

2014 ◽

Vol 668-669 ◽

pp. 1363-1367 ◽

Cited By ~ 1

Author(s):

Zhi Hong Sun ◽

Xian Lang Hu

Keyword(s):

Virtual Machine ◽

Migration Time ◽

Live Migration ◽

Virtual Machine Migration ◽

Memory State ◽

Dynamic Memory ◽

Virtual Machine Live Migration ◽

Migration Method ◽

Kalman Prediction ◽

Total Migration

The live migration of virtual machine (VM) is an important technology of cloud computing. Down-time, total migration time and network traffic data are the key measures of performance. Through the analysis of dynamic memory state of a virtual machine migration process, we propose a dirty pages algorithm prediction based on pre-copy to avoid dirty pages re transmission. Experimental results show that, compared with the Xen virtual machine live migration method adopted, our method can at least reduce 15.1% of the total amount of data and 12.2% of the total migration time.

Download Full-text

Performance Metrics of Virtual Machine Live Migration

2015 IEEE 8th International Conference on Cloud Computing ◽

10.1109/cloud.2015.90 ◽

2015 ◽

Cited By ~ 3

Author(s):

Michael Galloway ◽

Gabriel Loewen ◽

Susan Vrbsky

Keyword(s):

Virtual Machine ◽

Performance Metrics ◽

Live Migration ◽

Virtual Machine Live Migration

Download Full-text

FEATURE SELECTION FOR PREDICTING LIVE MIGRATION CHARACTERISTICS OF VIRTUAL MACHINES

T-Comm ◽

10.36724/2072-8735-2021-15-7-62-70 ◽

2021 ◽

Vol 15 (7) ◽

pp. 62-70

Author(s):

Denis E. Kirov ◽

◽

Natalia V. Toutova ◽

Anatoly S. Vorozhtsov ◽

Iliya A. Andreev ◽

...

Keyword(s):

Support Vector Machine ◽

Linear Regression ◽

Virtual Machine ◽

Performance Metrics ◽

Virtual Machines ◽

Cloud Services ◽

Migration Time ◽

Support Vector ◽

Total Migration

Virtual machine migration is widely used in cloud data centers to scale and maintain the stability of cloud services. However, the performance metrics of virtual machine (VM) applications during migration that are set in the Service Level Agreements may deteriorate. Before starting a migration, it is necessary to evaluate the migration characteristics that affect the quality of service. These characteristics are the total migration time and virtual machine downtime, which are random variables that depend on a variety of factors. The prediction is based on the VM monitoring data. In this paper, we select the most suitable factors for forecasting five types of migrations: precopy migration, postcopy migration, and modification of precopy migration such as CPU throttling, data compression, and delta compression of modified memory pages. To do this, we analyzed a dataset that includes data on five types of migrations, approximately 8000 records of each type. Using correlation analysis, the factors that mostly affect the total migration time and the VM downtime are chosen. These characteristics are predicted using machine learning methods such as linear regression and the support vector machine. It is shown that the number of factors can be reduced almost twice with the same quality of the forecast. In general, linear regression provides relatively high accuracy in predicting the total migration time and the duration of virtual machine downtime. At the same time, the observed nonlinearity in the correlations shows that it is advisable to use the support vector machine to improve the quality of the forecast.

Download Full-text