scholarly journals TSMC: A Novel Approach for Live Virtual Machine Migration

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jiaxing Song ◽  
Weidong Liu ◽  
Feiran Yin ◽  
Chao Gao
Keyword(s):  
Virtual Machine ◽  
Power Saving ◽  
Live Migration ◽  
Virtual Machine Migration ◽  
Page Fault ◽  
Novel Approach ◽  
Virtualization Technology ◽  
Management Method ◽  
Virtual Resources ◽  
Infrastructure Services

Cloud computing attracted more and more attention in recent years, and virtualization technology is the key point for deploying infrastructure services in cloud environment. It allows application isolation and facilitates server consolidation, load balancing, fault management, and power saving. Live virtual machine migration can effectively relocate virtual resources and it has become an important management method in clusters and data centers. Existing precopy live migration approach has to iteratively copy redundant memory pages; another postcopy live migration approach would lead to a lot of page faults and application degradation. In this paper, we present a novel approach called TSMC (three-stage memory copy) for live virtual machine migration. In TSMC, memory pages only need to be transmitted twice at most and page fault just occurred in small part of dirty pages. We implement it in Xen and compare it with Xen’s original precopy approach. The experimental results under various memory workloads show that TSMC approach can significantly reduce the cumulative migration time and total pages transferred and achieve better network IO performance in the same time.

Towards A Virtual Machine Migration Algorithm Based On Multi-Objective Optimization

2017 ◽  
Vol 8 (3) ◽  
pp. 79-89 ◽  
Author(s):  
Xiang Chen ◽  
Jun-rong Tang ◽  
Yong Zhang
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Resource Competition ◽  
Service Level Agreement ◽  
Service Level ◽  
Multi Objective Optimization ◽  
Virtual Machine Migration ◽  
Multi Objective ◽  
Time Migration ◽  
Management Method

In the cloud computing, the virtual machine (VM) dynamical management method needs to consider VM resource re-configuration caused by system computation resource status changing and load fluctuation. Based on migration objectives as QoS (Quality of Service), resource competition and energy consumption, the VM migration time, migration objective node selection and VM placement strategies are designed in this work. The Multi-Criteria Decision-Making (MCDM) method is also introduced for migration destination host selection. Experiment results show that the multi-objective optimization management method with TOPSIS can achieve lower service-level agreement (SLA) violation rate, less energy consumption and better balance among different objectives.

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.

scholarly journals A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Jia Zhao ◽  
Yan Ding ◽  
Gaochao Xu ◽  
Liang Hu ◽  
Yushuang Dong ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Power Saving ◽  
Genetic Operators ◽  
Virtual Machine Migration ◽  
Cloud Data Center ◽  
Vm Migration ◽  
Location Selection ◽  
Cloud Data

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

scholarly journals A Specific Risk Evaluation System for Live Virtual Machine Migration Based on the Uncertain Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hang Zhou ◽  
Xinying Zhu ◽  
Jian Wang
Keyword(s):  
Virtual Machine ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Integration Method ◽  
Live Migration ◽  
Virtual Machine Migration ◽  
And Performance ◽  
Uncertain Theory ◽  
Risk Evaluation System ◽  
Theory Series

Benefiting from the convenience of virtualization, virtual machine migration is generally utilized to fulfil optimization objectives in cloud/edge computing. However, live migration has certain risks and unapt decision may lead to side effects and performance degradation. Leveraging modified deep Q network, this paper provided an advanced risk evaluation system. Thorough formulation was given in this paper and a specific integration method was innovated based on uncertain theory. Series experiments were carried on computing cluster with OpenStack. The experimental results showed deep Q network for risk system was reliable while the uncertain approach was a proper way to deal with the risk integration.

scholarly journals A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Gaochao Xu ◽  
Yan Ding ◽  
Jia Zhao ◽  
Liang Hu ◽  
Xiaodong Fu
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Artificial Bee Colony ◽  
Power Saving ◽  
Bayes Theorem ◽  
Virtual Machine Migration ◽  
Cloud Data Center ◽  
Vm Migration ◽  
Cloud Data ◽  
Bee Colony

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration’s ability and local exploitation’s ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

A Practical Approach on Virtual Machine Live Migration

2015 ◽  
pp. 464-483
Author(s):  
Prashanta Kumar Das
Keyword(s):  
Virtual Machine ◽  
Operating Systems ◽  
Live Migration ◽  
Virtual Machine Migration ◽  
The Third ◽  
Physical Machine ◽  
Virtual Machine Live Migration ◽  
Key Features ◽  
Server Maintenance ◽  
Resource Consolidation

Virtualization technology enables organizations to take the benefit of different services, operating systems, and software without increasing their IT infrastructure liabilities. Live migration of virtual machine is the key features of the virtualization. It allows the administrator to move the virtual machine from one physical machine to another physical machine without any interruption. This technique is widely used for load balancing, server maintenance, and resource consolidation. The virtual machine migration problem consists of four distinct steps. The first step is to select the host from where VM migrated. After selecting, the host next step is to select the VM, which is migrated. The third step is to select the host where the migrated VM will be placed, and the last step is to decide the method, which is used to transfer the VM. This chapter covers all the basic information related to VM migration.

scholarly journals Live Virtual Machine migration: A Performance Test Xen and KVM

2020 ◽  
Vol 8 (5) ◽  
pp. 4643-4647
Keyword(s):  
Virtual Machine ◽  
Performance Test ◽  
Virtual Machines ◽  
Failure Load ◽  
Cloud Environment ◽  
Virtual Machine Migration ◽  
Virtualization Technology ◽  
Balancing Energy ◽  
Cluster Environment ◽  
Better Than

Virtualization technology has many important features such as live virtual machine migration. In live virtual machine migration, a power on virtual machine is moved from one physical host to another. It has various benefits such as server consolidation, proactive failure, load balancing, energy saving and resource scheduling. Live virtual machine migration is very useful tool in cluster environment, administrators of data centers and in cloud environment. Live virtual machine migration is supported by hypervisors such as Xen, KVM, VMware etc. In this paper we discuss live virtual machine migration Pre-Copy approach which is a default approach in many hypervisors. We compare the performance of virtual machines which are made using Xen and KVM. We also compare performance when virtual machines are migrate using Xen and KVM in cloudreport simulator. In result we find that KVM performs better than Xen.

Formal Analysis of Virtual Machine Migration and Identification of Faults

2018 ◽  
Vol 8 (1) ◽  
pp. 16-28
Author(s):  
Santosh Kumar Majhi ◽  
Sunil Kumar Dhal
Keyword(s):  
Virtual Machines ◽  
Service Providers ◽  
Formal Analysis ◽  
Live Migration ◽  
Migration Process ◽  
Virtual Machine Migration ◽  
Vm Migration ◽  
System Maintenance ◽  
State Changes ◽  
Infrastructure Services

Infrastructure as a service (IaaS) cloud supports flexible and agile execution of applications by creating virtualized execution environment namely, virtual machines (VMs) with on-demand infrastructural resources. In such environment, VM migration is used as a tool to facilitate system maintenance, load balancing and fault tolerance. The use of VM migration is to establish the portfolio of using dynamic and scalable infrastructure services offered by the service providers. In this paper, we study the VM migration process and investigate the potential faults which can occur during migration. Also, the state changes of a VM throughout its lifetime has been systematically analyzed and modeled as concurrent state machines. The potential faults are presented considering the live migration process of VM and accordingly VM state changes. In addition, a methodology for identifying the migration faults has been presented.

scholarly journals Global Detection of Live Virtual Machine Migration Based on Cellular Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Kang Xie ◽  
Yixian Yang ◽  
Ling Zhang ◽  
Maohua Jing ◽  
Yang Xin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Virtual Machine ◽  
Large Scale ◽  
Detection Algorithm ◽  
Cellular Neural Networks ◽  
Virtual Machine Migration ◽  
Vm Migration ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Virtual Resources

In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better.

scholarly journals An Automatic Decision-Making Mechanism for Virtual Machine Live Migration in Private Clouds

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Ming-Tsung Kao ◽  
Yu-Hsin Cheng ◽  
Shang-Juh Kao
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Operating Cost ◽  
Live Migration ◽  
Cloud Computing Environment ◽  
Virtualization Technology ◽  
Virtual Machine Live Migration ◽  
Automatic Mechanism ◽  
Two Machines ◽  
Heavy System

Due to the increasing number of computer hosts deployed in an enterprise, automatic management of electronic applications is inevitable. To provide diverse services, there will be increases in procurement, maintenance, and electricity costs. Virtualization technology is getting popular in cloud computing environment, which enables the efficient use of computing resources and reduces the operating cost. In this paper, we present an automatic mechanism to consolidate virtual servers and shut down the idle physical machines during the off-peak hours, while activating more machines at peak times. Through the monitoring of system resources, heavy system loads can be evenly distributed over physical machines to achieve load balancing. By integrating the feature of load balancing with virtual machine live migration, we successfully develop an automatic private cloud management system. Experimental results demonstrate that, during the off-peak hours, we can save power consumption of about 69 W by consolidating the idle virtual servers. And the load balancing implementation has shown that two machines with 80% and 40% CPU loads can be uniformly balanced to 60% each. And, through the use of preallocated virtual machine images, the proposed mechanism can be easily applied to a large amount of physical machines.

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