Dynamic weighted virtual machine live migration mechanism to manages load balancing in cloud computing

Author(s):  
Pradeep Kumar Tiwari ◽  
Sandeep Joshi
2018 ◽  
Vol 8 (1) ◽  
pp. 2459-2463 ◽  
Author(s):  
M. K. Hassan ◽  
A. Babiker ◽  
M. Baker ◽  
M. Hamad

Application of cloud computing is rising substantially due to its capability to deliver scalable computational power. System attempts to allocate a maximum number of resources in a manner that ensures that all the service level agreements (SLAs) are maintained. Virtualization is considered as a core technology of cloud computing. Virtual machine (VM) instances allow cloud providers to utilize datacenter resources more efficiently. Moreover, by using dynamic VM consolidation using live migration, VMs can be placed according to their current resource requirements on the minimal number of physical nodes and consequently maintaining SLAs. Accordingly, non optimized and inefficient VMs consolidation may lead to performance degradation. Therefore, to ensure acceptable quality of service (QoS) and SLA, a machine learning technique with modified kernel for VMs live migrations based on adaptive prediction of utilization thresholds is presented. The efficiency of the proposed technique is validated with different workload patterns from Planet Lab servers. 


2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Ming-Tsung Kao ◽  
Yu-Hsin Cheng ◽  
Shang-Juh Kao

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.


2018 ◽  
Vol 7 (4.16) ◽  
pp. 28-31
Author(s):  
Aula Abdel Latief Dewan ◽  
Rabah Abood Ahmed

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.  


2019 ◽  
Vol 38 (2) ◽  
pp. 291-320
Author(s):  
Petrônio Bezerra ◽  
Marcela Santos ◽  
Edlane Alves ◽  
Anderson Costa ◽  
Fellype Albuquerque ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document