An adaptive virtual machine management system for cloud computing

2015 ◽  
pp. 77-82
Author(s):  
B Li ◽  
Y Shang
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Management System

Model Knowledge Management System Konseling Mahasiswa Berbasis Cloud Computing

CCIT Journal ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 138-148
Author(s):  
H. Abdul Hamid Arribathi ◽  
Dedeh Supriyanti ◽  
Lusyani Sunarya
Keyword(s):  
Information Technology ◽  
Cloud Computing ◽  
Knowledge Management ◽  
Management System ◽  
Positive Impact ◽  
Professional Staff ◽  
Knowledge Management System ◽  
Literature Study ◽  
Student Counseling ◽  
The World

Information technology is increasingly developing and has a positive impact on the world of education. The implementation of student counseling with a Knowledge Management System is one of the contributions of information technology in the world of education. Many benefits provided by the Student Counseling Knowledge Management System, in managing the knowledge needed by the counselor section to document student consultations. The development and implementation of a KMS counseling system costs more to employ professional staff to maintain and improve; KMS student counseling application; For this reason, it is necessary to design a Cloud Computing-based Student Counseling Knowledge Management System. The research method carried out in the first stage is to collect data and information about Knowledge Management and Student Counseling, and how to use it to create a Knowledge Management System Application, Student Counseling Based on cloud computing. Furthermore, conducting a literature study and literature review, system design is in the form of data architecture compounding, process design, network design, and user interface design. The design results of this system can facilitate educational institutions in conducting online cloud computing-based student counseling

A Hybrid Approach for Virtual Machine Migration in Cloud Computing Environment

2017 ◽  
Vol 7 (9) ◽  
pp. 30 ◽  
Author(s):  
Ramandeep Kaur
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Heuristic Algorithms ◽  
Hybrid Approach ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Bee Colony ◽  
Effective Manner ◽  
Effective Performance

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.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Global host allocation policy for virtual machine in cloud computing

2018 ◽  
Vol 10 (3) ◽  
pp. 279-287 ◽  
Author(s):  
Mohit Kumar ◽  
Arun Kumar Yadav ◽  
Pallavi Khatri ◽  
Ram Shringar Raw
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Allocation Policy

scholarly journals An Efficient Virtual Machine Consolidation Scheme for Multimedia Cloud Computing

Sensors ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 246 ◽  
Author(s):  
Guangjie Han ◽  
Wenhui Que ◽  
Gangyong Jia ◽  
Lei Shu
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Multimedia Cloud ◽  
Virtual Machine Consolidation

Layered virtual machine migration algorithm for network resource balancing in cloud computing

2017 ◽  
Vol 12 (1) ◽  
pp. 75-85 ◽  
Author(s):  
Xiong Fu ◽  
Juzhou Chen ◽  
Song Deng ◽  
Junchang Wang ◽  
Lin Zhang
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machine Migration ◽  
Network Resource
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