Towards a comprehensive performance model of virtual machine live migration

Nathan, Senthil; Bellur, Umesh; Kulkarni, Purushottam

doi:10.1145/2806777.2806838

Cited by 55 publications

(32 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Total migration time, downtime, and forecasting dirty pages are performance parameters of our live migration system. Experiments 1 and 2 are shown using ARIMA model and SVR model, respectively, using real data set given in [27].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Stop-and-copy condition shown in Figure 1 is described below [27]: (i) A maximum limit of iterations is 29.…”

Section: Proposed Framework the Migration Of Virtual Machinementioning

confidence: 99%

“…ARIMA model is performed with time series having stationary data and it is also used to forecast the training data. Real data set [27] is used to evaluate performance parameters using ARIMA model. [8] is defined as ( ), where ( ) is a random variable over a set of data points and represents the time elapsed having values 0, 1, 2, .…”

Section: Proposed Models Based On Statistical and Regression Techniquesmentioning

confidence: 99%

“…In this section, SVR model is applied for live migration of virtual machines. The model is used to evaluate accuracy on real data set [27]. ARIMA model has been designed based on universal approximation so it may have been lacking to generate higher accuracy on testing data set.…”

Section: Svr Modelmentioning

confidence: 99%

See 3 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: Experiments and Resultsmentioning

confidence: 99%

“…Stop-and-copy condition shown in Figure 1 is described below [27]: (i) A maximum limit of iterations is 29.…”

Section: Proposed Framework the Migration Of Virtual Machinementioning

confidence: 99%

Section: Proposed Models Based On Statistical and Regression Techniquesmentioning

confidence: 99%

Section: Svr Modelmentioning

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

show abstract

“…Depending on different migration mechanisms their performance differs in metrics as optimization ways change [4,8].…”

Section: Performance Parametersmentioning

confidence: 99%

A Distributed API for Live VM Migration in Cloudlets

Singhai¹,

Damania²,

Holmukhe³

et al. 2018

IJCA

View full text Add to dashboard Cite

Cloud computing is an effective technology in handling computation with dynamically scalable resources. With the growth of multimedia applications, mobile applications have become resource-intensive. To provide better connectivity with the cloud, cloudlets have been introduced, which in turns provide low latency and high bandwidth. In this paper, we discuss the live migration of Virtual machines in Cloudlets using VirtualBox as hypervisor. We have proposed and implemented a distributed API Viper particularly for cloudlets, which provides users, interfaces for operating with Virtual Machines. We also test this API in different scenarios, changing RAM, CPU and CPU Stress.

show abstract

Opportunistic live migration of virtual machines

Gilesh¹,

Jain

Kumar

et al. 2019

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Live migrations of virtual machines are required in cloud data centers in various contexts such as evacuating a host machine for maintenance, balancing the workloads on host machines, optimizing physical resource utilization, and meeting the custom demands of the user applications.Virtual machine migrations can, however, be costly in terms of both resources consumed for the migration as well as in terms of service level agreement (SLA) violations during the migration window. The cost is determined by many factors and they, in turn, are impacted by WHEN the migration happens during the lifetime of the virtual machine. Empirical studies show that if we have a window in which the migration is to be done (proactive rather than reactive where no such window exists), we can do it by carefully choosing the starting point within the window so that the SLA violation is minimum. In this paper, we propose a model to migrate virtual machines opportunistically to minimize the SLA violations. The idea is to find the time instants, called opportunities, at which, if the migration starts, the number and extent of SLA violations are minimal. Our experiments show that the migrations time savings with opportunistic migrations are up to 25%. KEYWORDS cloud computing, opportunistic live migration, virtual machines INTRODUCTIONLive virtual machine (VM) migrations are performed in cloud data centers (CDC) for various reasons such as evacuating a host machine for maintenance, balancing the workloads on host machines, optimizing physical resource utilization, and meeting the custom demands of the user applications. 1 One of the most significant advantages of live migration is the fact that it facilitates proactive resource management and maintenance. There are two approaches for live VM migration, pre-copy and post-copy. Pre-copy is the most commonly used mode of migration and has two phases. In the iterative copy phase, memory pages are iteratively copied from the source machine to the destination, all without stopping the execution of the VM being migrated. In the stop-and-copy phase, which follows the execution of VM, is suspended at the source to transfer all pages dirtied during the final iteration of the iterative copy phase and also the hardware state associated with the VM. 2 The duration of the stop-and-copy phase is the downtime of the VM being migrated.The VM migrations may be for either proactive or reactive resource management in a data center. In reactive resource management, VMs are migrated when the need arises, whereas in proactive resource management, migrations are carried out well before the actual need arises. 3Proactive resource management using existing predictive analytics tools is shown to be suitable for multi-tenant data centers to address cases such as timely management of load spikes. 4 Furthermore, live VM migration can be adaptive or non-adaptive. In case of adaptive migration, the rate of transfer of pages is adjusted based on the page dirtying rate, whereas in non-adaptive technique, pages are migrated at...

show abstract

Towards a comprehensive performance model of virtual machine live migration

Cited by 55 publications

References 28 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

A Distributed API for Live VM Migration in Cloudlets

Opportunistic live migration of virtual machines

Contact Info

Product

Resources

About