Migration Decision in Reconfigurable Distributed Virtual Machines

2005 ◽  
pp. 361-380
Keyword(s):  
Virtual Machines ◽  
Migration Decision

Optimum Utilization of Resources Through Restricted Virtual Machine Migration and Efficient VM Placement in Cloud Data Center

2018 ◽  
Vol 9 (4) ◽  
pp. 1-19
Author(s):  
Subhadra Bose Shaw ◽  
Anil Kumar Singh ◽  
Shailesh Tripathi
Keyword(s):  
Energy Consumption ◽  
Virtual Machines ◽  
Energy Performance ◽  
Migration Decision ◽  
Performance Gain ◽  
Virtual Machine Migration ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Network Bandwidth ◽  
Optimum Utilization

In infrastructure-as-a-service (IAAS) cloud platforms, it is a real challenge to provide high performance gain by the optimum utilization of resources while maintaining minimum consumption of energy. The existing research works show that reduction in energy consumption causes violation of service level agreement (SLA). In this article, the concept of probability has been used to take the migration decision of virtual machines (VM) from over-utilized as well as under-utilized nodes. A novel method has also been proposed for selecting the destination server where a migrated VM will be placed. This method is based on the current utilization of CPU, memory and network bandwidth. The proposed scheme maintains a balance between energy consumption and performance gain. Results obtained through trace driven simulation demonstrate that the probability-based migration scheme achieves energy-performance trade-off whereas the VM placement method shows a very high gain in performance.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

What Determines The Migration Intentions Of University Students?

2014 ◽  
Vol 1 (1) ◽  
pp. 193-198
Author(s):  
Heiko Haase ◽  
Arndt Lautenschläger
Keyword(s):  
University Students ◽  
Career Choice ◽  
Cross Sectional Study ◽  
Demographic Characteristics ◽  
Migration Decision ◽  
Considerable Proportion ◽  
Sectional Study ◽  
Intention To Stay ◽  
Cross Sectional ◽  
The University

AbstractThe paper aims at exploring determinants of the university students' intentions to stay within their university region. At this, we presume that students' career choice motivations are related to their professional intentions, which again, along with demographic characteristics, affect their migration decision. Our analysis is based on a cross-sectional study of 2,353 students from three different higher education institutions, two of them located in Germany and one in Namibia. Results indicate that in Germany migration matters because a considerable proportion of students intend to leave the university region after graduation. At this, we found that the students' geographical provenance exerts the most significant effect on the intention to stay. Moreover, certain professional intentions were directly and some career choice motivations were indirectly linked with the intention to remain at the university location. We present several conclusions and implications.

Allocation of Virtual Machines to the Cloud Network Using the Barking-Deer Approach

2020 ◽  
Author(s):  
Himadri Biswas ◽  
Sudipta Sahana ◽  
Priyajit Sen ◽  
Debabrata Sarddar
Keyword(s):  
Virtual Machines ◽  
Cloud Network
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