Route Aware Virtual Machine Migration in Cloud Datacenter

Author(s):  
Getzi Jeba Leelipushpam Paulraj ◽  
Sharmila An John Francis ◽  
J. Dinesh Peter ◽  
Immanuel Johnraja Jebadurai

The workload in cloud computing surroundings changes progressively delivering unwanted circumstances, for example, load unbalancing and minor usage. Virtual machine migration is an impressive plan in such circumstances inorder to improve system performance. With a specific end goal to give productive energy virtual machine migration is essential that migrates a running virtual machine without disconnecting the client or application. In any case, an algorithm in view of a single objective is generally familiar with to coordinate the migration process. Unexpectedly, there stay alive unconsidered variables affecting the migration process, for example, burden capacity, power utilization and resource wastage. We offer a multi-objective algorithm for obtaining VM migration by evaluating the multi objectives that are responsible for migration overhead. In this manner, we suggest a narrative relocation approach united by a Multi objective Dolphin Echolocation Optimization Algorithm (MO-DEOA) to assess several objectives. The aim is to efficiently obtain improved migration that concurrently diminishes power consumption by guaranteeing the performance of the system.


Author(s):  
Suresh Chandra Moharana ◽  
Bishwabara Panda ◽  
Manoj Kumar Mishra ◽  
Bhabani Shankar Prasad Mishra ◽  
Amulya Ratna Swain ◽  
...  

Virtualization is a core and requisite technology in Cloud Computing that provisions scalable virtual resources for execution of varied applications. It enables the cloud datacenter resources to be multiplexed within numerous virtual computing environments recognized as virtual machines. These virtual machines consolidates varied applications with diversified resource requirements. It prompts to increase in load imbalance level leading to reduced performance and SLA violations. In order to achieve load balancing across virtual machines varied approaches are presented in literature and virtual machine migration based load balancing is a popular move in this direction. In this work, recent literature on different migration based load balancing schemes are reviewed. The objective of the work is highlight the features, advantages and shortcomings of the considered literature. Alongside that, the effort is conferred to provide an analytical view over different perspectives which will motivate the research in this area.


Author(s):  
Ramandeep Kaur

A lot of research has been done in the field of cloud computing in computing domain.  For its effective performance, variety of algorithms has been proposed. The role of virtualization is significant and its performance is dependent on VM Migration and allocation. More of the energy is absorbed in cloud; therefore, the utilization of numerous algorithms is required for saving energy and efficiency enhancement in the proposed work. In the proposed work, green algorithm has been considered with meta heuristic algorithms, ABC (Artificial Bee colony .Every server has to perform different or same functions. A cloud computing infrastructure can be modelled as Primary Machineas a set of physical Servers/host PM1, PM2, PM3… PMn. The resources of cloud infrastructure can be used by the virtualization technology, which allows one to create several VMs on a physical server or host and therefore, lessens the hardware amount and enhances the resource utilization. The computing resource/node in cloud is used through the virtual machine. To address this problem, data centre resources have to be managed in resource -effective manner for driving Green Cloud computing that has been proposed in this work using Virtual machine concept with ABC and Neural Network optimization algorithm. The simulations have been carried out in CLOUDSIM environment and the parameters like SLA violations, Energy consumption and VM migrations along with their comparison with existing techniques will be performed.


Author(s):  
Liu-Mei Zhang ◽  
Jian-Feng Ma ◽  
Di Lu ◽  
Yi-Chuan Wang

2018 ◽  
Vol 9 (4) ◽  
pp. 309-317
Author(s):  
Damodar Tiwari ◽  
Shailendra Singh ◽  
Sanjeev Sharma

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