A Relationship-Based VM Placement Framework of Cloud Environment

To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.

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Energy efficient strategy for task allocation and VM placement in cloud environment

2017 Innovations in Power and Advanced Computing Technologies (i-PACT) ◽

10.1109/ipact.2017.8244950 ◽

2017 ◽

Cited By ~ 3

Author(s):

P Divya Bharathi ◽

P Prakash ◽

M Vamsee Krishna Kiran

Keyword(s):

Task Allocation ◽

Energy Efficient ◽

Cloud Environment ◽

Vm Placement ◽

Efficient Strategy

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Best Fit Sharing and Power Aware (BFSPA) Algorithm for VM placement in cloud environment

2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall) ◽

10.1109/icaccaf.2017.8344674 ◽

2017 ◽

Cited By ~ 1

Author(s):

Garima Batra ◽

Harshita Singh ◽

Ishu Gupta ◽

Ashutosh Kumar Singh

Keyword(s):

Cloud Environment ◽

Vm Placement ◽

Best Fit

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Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment

International Journal of Business Data Communications and Networking ◽

10.4018/ijbdcn.2017010104 ◽

2017 ◽

Vol 13 (1) ◽

pp. 45-57 ◽

Cited By ~ 1

Author(s):

Oshin Sharma ◽

Hemraj Saini

Keyword(s):

Genetic Algorithm ◽

Bin Packing ◽

Virtual Machines ◽

Cloud Environment ◽

Vm Placement ◽

Consolidation Process ◽

Physical Machine ◽

Overall Performance ◽

Vm Consolidation ◽

New Algorithms

In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, the idea of placing the virtual machines on some other hosts leads to the proposal of many new algorithms of VM placement. However, the reduced set of physical machines needs the lesser amount of power consumption therefore; in current work the authors have presented a decision making VM placement system based on genetic algorithm and compared it with three predefined VM placement techniques based on classical bin packing. This analysis contributes to better understand the effects of the placement strategies over the overall performance of cloud environment and how the use of genetic algorithm delivers the better results for VM placement than classical bin packing algorithms.

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Review of Fault Tolerance Frameworks in the Cloud

International Journal of Information System Modeling and Design ◽

10.4018/ijismd.2020070105 ◽

2020 ◽

Vol 11 (3) ◽

pp. 79-99

Author(s):

Ajay Rawat ◽

Rama Sushil ◽

Amit Agarwal

Keyword(s):

Fault Tolerance ◽

Cloud Services ◽

Research Review ◽

Cloud Environment ◽

Critical Research ◽

Vm Placement ◽

Critical Metrics ◽

Research Survey ◽

Detection Strategy ◽

Inherent Vulnerability

Fault tolerance is the most imperious issue in the cloud to provide reliable services. Inherent vulnerability to failure hampers the performance and reliability of cloud services. Hence, to achieve reliability, fault tolerance becomes a mandatory feature which is hard to implement due to the dynamic infrastructure and complex interdependencies. Numerous fault tolerance techniques have been developed in the literature to address the challenges of cloud reliability. A recent research survey presented in this paper attempts to integrate the different fault tolerance architecture. This study presents a critical research review on various existing fault tolerance techniques to improve services reliability, availability, and applications execution in the cloud. A comparative analysis, based on different critical metrics like failure prediction, detection strategy, failure history, VM placement, and limitations, of the reviewed framework systems is also included in the paper. This review intends to facilitate the development of the new fault tolerance technique for the cloud environment.

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Energy and SLA Efficient Virtual Machine Placement in Cloud Environment Using Non-Dominated Sorting Genetic Algorithm

International Journal of Information Security and Privacy ◽

10.4018/ijisp.2019010101 ◽

2019 ◽

Vol 13 (1) ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Oshin Sharma ◽

Hemraj Saini

Keyword(s):

Genetic Algorithm ◽

Energy Consumption ◽

Data Centers ◽

Virtual Machines ◽

Cloud Environment ◽

Vm Placement ◽

Energy Consumption Reduction ◽

Sla Violation ◽

Consumption Reduction ◽

Placement Algorithm

To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.

Download Full-text