Live Migration of Multiple Virtual Machines with Resource Reservation in Cloud Computing Environments

2011 ◽  
Author(s):  
Kejiang Ye ◽  
Xiaohong Jiang ◽  
Dawei Huang ◽  
Jianhai Chen ◽  
Bei Wang
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Resource Reservation ◽  
Live Migration ◽  
Computing Environments

scholarly journals A Threshold-based Tournament Resource Allocation in Cloud Computing Environment

2021 ◽  
pp. 1-13
Author(s):  
I. P. Oladoja ◽  
O. S. Adewale ◽  
S. A. Oluwadare ◽  
E. O. Oyekanmi
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Selection Probability ◽  
Cloud Computing Environment ◽  
High Resource ◽  
Performance Requirements ◽  
Tournament Selection ◽  
Computing Environments ◽  
Cloud Users

Cloud computing environments provide an apparition of infinite computing resources to cloud users so that they can increase or decrease resource consumption rate according to their demands. In the Cloud, computing resources need to be allocated and scheduled in a way that providers can achieve high resource utilization and users can meet their applications’ performance requirements with minimum expenditure. Due to these different intentions, there is the need to develop a scheduling algorithm to outperform appropriate allocation of tasks on resources. The paper focuses on the resource optimization using a threshold-based tournament selection probability for virtual machines used in the execution of tasks. The proposed approach was designed to create metatask and the proposed algorithm used was Median-Based improved Max-Min algorithm. The experimental results showed that the algorithm had better performance in terms of makespan, utilization of resources and throughput. The load balance of tasks was also fairly distributed on the two datacenters.

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 Energy-aware cost prediction and pricing of virtual machines in cloud computing environments

2019 ◽  
Vol 93 ◽  
pp. 442-459 ◽  
Author(s):  
Mohammad Aldossary ◽  
Karim Djemame ◽  
Ibrahim Alzamil ◽  
Alexandros Kostopoulos ◽  
Antonis Dimakis ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cost Prediction ◽  
Energy Aware ◽  
Computing Environments

scholarly journals Optimized Mechanism for Resource Sharing in Cloud

2019 ◽  
Vol 9 (2) ◽  
pp. 4889-4893
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Virtual Machines ◽  
Cloud Infrastructure ◽  
Cloud Computing Environment ◽  
Computing Paradigm ◽  
Suggested Technique ◽  
Computing Environments ◽  
Poor Management ◽  
Stable Stage

The cloud computing paradigm has settled to a stable stage. Due to its enormous advantages, services based on cloud computing are getting more and more attraction and adoption by diversified sectors of society. Because of its pay per use model, people prefer to execute various data crunching operations on high end virtual machines. Optimized resource management however becomes critical in such scenarios. Poor management of cloud resources may affect not only customer satisfaction but also wastage of available cloud infrastructure. An optimized resource sharing mechanism for collaborated cloud computing environments is suggested here. The suggested resource sharing technique solves starvation issue in inter cloud load balancing context. In case of occurrence of starvation problem, the suggested technique resolves the issue by switching under loaded and overloaded virtual machines between intra cloud and inter cloud computing environment.

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