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

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.

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.

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 Self - Adaptive Load Balancing Using Live Migration of Virtual Machines in Cloud Environment

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 735-745
Author(s):  
V. Lavanya ◽  
M. Saravanan ◽  
E.P. Sudhakar
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Live Migration ◽  
Management Activity ◽  
Cloud Computing Environment ◽  
Efficient Resource ◽  
Server Overload ◽  
Self Adaptive

In this paper, a self-adaptive load balancing technique is proposed using live migration of heterogeneous virtual machines (VM) in a Hyper-V based cloud environment. A cloud supported plugin as a management activity within the infrastructure as a service strategy. It is proposed to assist the load balancing process in such a way so that all hypervisors are almost equally loaded once the overload status gets triggered. In the cloud computing environment, load balancing plays a major role if the large number of events triggered has a high impact on the performance of the system. The efficiency of cloud computing is based on the efficient load balancing having a self-adjustable technique using live migration of VMs across clusters of nodes. The proposed load balancing model is efficient in performance improvement by efficient resource utilization and also it helps to avoid the situation occurrence of server hanging by the cause of server overload within the infrastructure of multiple Microsoft Hyper-V hypervisors environment.

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 Virtual machines pre-copy live migration cost modeling and prediction: a survey

2021 ◽  
Author(s):  
Mohamed Esam Elsaid ◽  
Hazem M. Abbas ◽  
Christoph Meinel
Keyword(s):  
Cloud Computing ◽  
Cost Analysis ◽  
Virtual Machines ◽  
Essential Feature ◽  
Power Saving ◽  
Live Migration ◽  
Survey Paper ◽  
Migration Cost ◽  
Prediction Techniques ◽  
Migration Costs

AbstractLive migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration cost. Several papers analyze and model live migration costs for different hypervisors, different kinds of workloads and different models of analysis. In addition, there are also many other papers that provide prediction techniques for live migration costs. It is a challenge for the reader to organize, classify, and compare live migration overhead research papers due to the broad focus of the papers in this domain. In this survey paper, we classify, analyze, and compare different papers that cover pre-copy live migration cost analysis and prediction from different angels to show the contributions and the drawbacks of each study. Papers classification helps the readers to get different studies details about a specific live migration cost parameter. The classification of the paper considers the papers’ research focus, methodology, the hypervisors, and the cost parameters. Papers analysis helps the readers to know which model can be used for which hypervisor and to know the techniques used for live migration cost analysis and prediction. Papers comparison shows the contributions, drawbacks, and the modeling differences by each paper in a table format that simplifies the comparison. Virtualized Data-center and cloud computing clusters admins can also make use of this paper to know which live migration cost prediction model can fit for their environments.

Sign in / Sign up

Export Citation Format

Share Document