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

Patel, Minal; Chaudhary, Sanjay; Garg, Sanjay

doi:10.1155/2016/3061674

Cited by 22 publications

(10 citation statements)

References 22 publications

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“…The estimator used for calculating prediction error was average prediction and maximum prediction error. Minal Patel et al, [15] proposed the Support Vector Regression (SVR) and Autoregressive Integrated Moving Average (ARIMA) method to predict the dirty pages of VM during live migration and determine the migration time of VM depend on time series analysis. The ARIMA model is applied to reduce the dirty pages, network traffic, and memory size based on past statistical data.…”

Section: Related Workmentioning

confidence: 99%

Power consumption prediction in cloud data center using machine learning

Deepika

Prakash

2020

IJECE

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The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.

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Section: Related Workmentioning

confidence: 99%

Power consumption prediction in cloud data center using machine learning

Deepika

Prakash

2020

IJECE

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“…Historical data of PMs and VMs were evaluated and classified according to the overall resource utilization [29]. In some research support vector regression (SVR) was also used as a learning approach and SVM classify data and make predictions effectively on overlap regions of two classes, it is widely used for forecasting and classification [23]. Niehorster et al, presented an approach for the provisioning of VMs using SVMs.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

A survey on application of machine learning to manage the virtual machines in cloud computing

Barthwal

Rauthan

Varma

2020

IRASE

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Virtual machine (VM) management is a fundamental challenge in the cloud datacenter, as it requires not only scheduling and placement, but also optimization of the method to maintain the energy cost and service quality. This paper reviews the different areas of literature that deal with the resource utilization prediction, VM migration, VM placement and the selection of physical machines (PMs) for hosting the VMs. The main features of VM management policies were also examined using a comparative analysis of the current policies. Many research works include Machine Learning (ML) for detecting the PM overloading, the selection of VMs from over-utilized PM and VM placement as the main activities. This article aims to identify and classify research done in the area of scheduling and placement of VMs using the ML with resource utilization history. Energy efficiency, VM migration counts and Service quality were the key performance parameters that were used to assess the performance of the cloud datacenter.

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“…Stage and Setzer propose a control system model for workload‐based VM allocation and resource aware migration scheduling. For predicting the aforementioned parameters determining the duration of a live VM migration, Patel et al propose a machine learning approach for the prediction of workload to create an efficient live migration schedule. Zhu et al develop approaches for task allocation and message transmission to ensure faults tolerance during the workflow execution.…”

Section: Related Workmentioning

confidence: 99%

“…Reduction of the impact of migration on the application throughput in various contexts such as private clouds, public cloud data centers, and mobile systems have already been studied. The literature has work to predict one or more of the parameters determining the duration of a live VM migration, based on the application behavior or workload characteristics.…”

Section: Introductionmentioning

confidence: 99%

Opportunistic live migration of virtual machines

Gilesh¹,

Jain

Kumar

et al. 2019

Concurrency and Computation

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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...

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

Cited by 22 publications

References 22 publications

Power consumption prediction in cloud data center using machine learning

Power consumption prediction in cloud data center using machine learning

A survey on application of machine learning to manage the virtual machines in cloud computing

Opportunistic live migration of virtual machines

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