Analytical Approach to Estimating Total Migration Time of Virtual Machines With Various Applications

2020 ◽  
Vol 11 (2) ◽  
pp. 58-75
Author(s):  
Andrew Toutov ◽  
Anatoly Vorozhtsov ◽  
Natalia Toutova
Keyword(s):  
Analytical Approach ◽  
Virtual Machines ◽  
Migration Time ◽  
Characteristic Functions ◽  
Live Migration ◽  
File Storage ◽  
Laguerre Series ◽  
Cloud Applications ◽  
Total Migration

Cloud applications and services such as social networks, file sharing services, and file storage have become increasingly popular among users in recent years. This leads to the enlargement of data centers, and an increase in the number of servers and virtual machines. In such systems, live migration is used to move virtual machines from one server to another, which affects the quality of service. Therefore, the problem of finding the total migration time is relevant. This article proposes analytical approach to obtaining analytical expression of the probability density of the total migration time based on the use of the apparatus of characteristic functions. The obtained expression is used to calculate characteristics of migration, taking into account the applications contributing the most randomness to the total migration time. To simplify the calculation of migration characteristics, the use of the Laguerre series can be recommended as giving more reliable results compared to Gram-Charlier series.

ABDM : Agent Based Live Migration of Virtual Machines in Cloud Computing For Multimedia Data

2018 ◽  
pp. 9-17
Author(s):  
K. Syed Ibrahim ◽  
Dr. A. R. Mohamed Shanavas
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Exit Time ◽  
Multimedia Data ◽  
Migration Time ◽  
Source Node ◽  
Destination Node ◽  
Live Migration ◽  
Agent Based ◽  
Total Migration

Migration time is one of the metric to measure the performance of the algorithm for live migration. In this paper we have introduced a new parameter for live migration of virtual machines (VM) called the ‘Exit Time’ which is defined as the time to eject the state of one or more VMs from the source node. Exit Time defines how rapidly the VM can be taken out from the source node and its resources are freed for reallocating other tasks. We present an Agent Based Live Migration which disconnects the source node from the destination node during migration to reduce the exit time if the destination is slow. The source distributes the memory of VMs to multiple intermediate nodes organized by a middleware. Simultaneously, the destination collects and merges the VMs’ memory from the intermediate nodes. Thus exit from the source node is no longer resisted by the receiving speed of the destination. We support simultaneous live exit of multiple VMs and our ABDM implementation in the CloudSim platform reduces the exit time by a considerable amount against the traditional pre-copy and post-copy migration at the same time keeping the total migration time when the destination node is sluggish than the source

scholarly journals FEATURE SELECTION FOR PREDICTING LIVE MIGRATION CHARACTERISTICS OF VIRTUAL MACHINES

T-Comm ◽  
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.

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

2014 ◽  
Vol 668-669 ◽  
pp. 1363-1367 ◽  
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.

A Virtual Machine Placement Algorithm with Energy-Efficiency in Cloud Computing

2017 ◽  
Vol 8 (2) ◽  
pp. 20-36
Author(s):  
Yu Cai
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Virtual Machines ◽  
Live Migration ◽  
Vm Placement ◽  
Server Cluster ◽  
Simulated Environment ◽  
Resource Requirements ◽  
Placement Algorithm

Energy efficient virtual machines (VM) management and distribution on cloud platforms is an important research subject. Mapping VMs into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It should also take into accounts of VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. In this article, the authors propose an energy efficient statistical live VM placement scheme in a heterogeneous server cluster. Their scheme supports VM requests scheduling and live migration to minimize the number of active servers in order to save the overall energy in a virtualized server cluster. Specifically, the proposed VM placement scheme incorporates all VM operation overheads in the dynamic migration process. In addition, it considers other important factors in relation to energy consumption and is ready to be extended with more considerations on user demands. The authors conducted extensive evaluations based on HPC jobs in a simulated environment. The results prove the effectiveness of the proposed scheme.

Cloud Computing-Oriented Virtual Machine Live Migration Mechanism

2014 ◽  
Vol 513-517 ◽  
pp. 1731-1734
Author(s):  
Yi Qiu Fang ◽  
Zhi Chao Song ◽  
Jun Wei Ge
Keyword(s):  
Data Transmission ◽  
Transmission Probability ◽  
Migration Time ◽  
Live Migration ◽  
Prediction Probability ◽  
Migration Mechanism ◽  
Probability Prediction ◽  
Locality Principle ◽  
Virtual Machine Live Migration ◽  
Total Migration

In the process of memory pre-copy, aiming at the characteristic which the dirty pages may be re-transmitted, dirty pages based on the probability prediction pre-copy mechanism is proposed, which aims to reduce the data transmission and total migration time. The mechanism uses temporal locality principle, on the pages before transmission, probability prediction for memory page, give priority to transmission of dirty pages prediction probability low memory pages, avoiding high dirty pages resend. Simulation results show that: the mechanism can reduce the amount of data transmission, reducing downtime and total migration time.

scholarly journals An overview of virtual machine live migration techniques

2019 ◽  
Vol 9 (5) ◽  
pp. 4433
Author(s):  
Artan Mazrekaj ◽  
Shkelzen Nuza ◽  
Mimoza Zatriqi ◽  
Vlera Alimehaj
Keyword(s):  
Virtual Machine ◽  
Performance Metrics ◽  
Virtual Machines ◽  
Optimal Solutions ◽  
Live Migration ◽  
Time Performance ◽  
Virtual Machine Live Migration ◽  
Memory Network ◽  
And Storage ◽  
Total Migration

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.

scholarly journals IAAS Reactive Auto Scaling Performance Challenges

2019 ◽  
Vol 8 (10) ◽  
pp. 538-541
Keyword(s):  
Quality Of Service ◽  
Virtual Machines ◽  
Performance Estimation ◽  
After Effects ◽  
Cloud Application ◽  
Performance Challenges ◽  
Auto Scaling ◽  
Cloud Applications

The principle highlight of a cloud application is its versatility. Significant IaaS cloud administrations suppliers (CSP) utilize auto scaling on the dimension of virtual machines (VM). Other virtualization arrangements (for example compartments, units) can likewise scale. An application scales in light of progress in watched measurements, for example in CPU use. Every so often, cloud applications display the powerlessness to meet the Quality of Service (QoS) necessities during the scaling brought about by the reactivity of auto scaling arrangements. This paper gives the after effects of the auto scaling execution assessment for two-layered virtualization (VMs and units) directed in the open billows of AWS, Microsoft and Google utilizing the methodology and the Auto scaling Performance Estimation Tool created by the creators

scholarly journals Enhancing Virtual Machine Live Migration Time Using Vcpu Limits

2018 ◽  
Vol 7 (4.16) ◽  
pp. 28-31
Author(s):  
Aula Abdel Latief Dewan ◽  
Rabah Abood Ahmed
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Virtual Machine ◽  
Data Centers ◽  
Migration Time ◽  
Live Migration ◽  
Important Method ◽  
Virtual Machine Live Migration ◽  
Computing Platforms

In the Cloud Computing, the live migration of a virtual machine or VM from one physical machine to another is a vital process applied on the service provider side. Live migration helps administrators manage data centers resources optimally. Due to the intensive daily use, it is necessary to improve the performance of VM migration-this is reflected in improving the quality of service provided to the customer while minimizing the costs incurred. Pre-copy is an important method of live migration that has been adopted in many cloud computing platforms. One main drawback of this method is the degradation of its performance when the migrating VM runs one or more of the processes that write on the memory pages faster than the speed of transferring those pages. This makes migration time-consuming. In this paper, we propose an approach to address this issue by reducing the generation rate of the modified pages while maintaining the service provided to the customer. This approach was applied to the real Xen platform and the results showed an improvement in the time of live migration of the virtual machine that runs an intensive write process up to 40% compared to the migration time of the original Xen platform.  

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