CPU Performance in Data Migrating from Virtual Machine to Physical Machine in Cloud Computing

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.

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Virtual Machine Migration Based on Trust Measurement of Computer Node

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.678 ◽

2014 ◽

Vol 536-537 ◽

pp. 678-682

Author(s):

Zhi Hong Liang ◽

Zhi Qiang Liang ◽

Yi Ming Tan ◽

Xue Cheng Lv

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Virtual Machine ◽

Virtual Machine Migration ◽

Performance Constraints ◽

Computing Platform ◽

Physical Machine ◽

Cloud Computing Platform ◽

Trust Measurement ◽

Computer Node

Currently, the study of virtual machine migration in cloud computing platform which usually did not consider the trustworthiness of target physical machine. For this, the paper proposes a trusted virtual machine migration with performance constraints algorithm (TVM2PC). The trustworthiness of target physical machine includes direct trustworthiness and indirect trustworthiness. By this method, a virtual machine will be migrated to a trusted physical machine. A large of experiment shows that the proposed method can give a better result than the existing method in load balancing and trustworthiness.

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Securing SAAS service under cloud computing based multi-tenancy systems

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v13.i1.pp65-71 ◽

2019 ◽

Vol 13 (1) ◽

pp. 65

Author(s):

Trinathbasu Miriyala ◽

JKR Sastry

Keyword(s):

Cloud Computing ◽

Virtual Machine ◽

Efficient Method ◽

Service Provider ◽

Virtual Machines ◽

Cloud Service ◽

Cloud Service Provider ◽

Physical Machine ◽

Multiple Users ◽

Business Requirements

<p><span lang="EN-US">Cloud computing technologies are being used by many who need computing resources such as software, platform and infrastructure as per their business requirements in terms of provisioning and pay for the usage as per actual consumption of the services based on the SLA signed by the user and cloud service provider. Software running on a physical machine is being provided as services to the end users. For the reasons of cost economies access to software that uses a database is being provided to multiple users. The access to the software is provided either directly or through a virtual machine. The software being provided as service uses the same database for many of the users who have requisitioned for the same. As a result, there could be encroachments by the users into the data of others. There is a need to secure the data belonging to several users while all of them access the data using the same application. In this paper an efficient method is presented for securing the data processed by software which is offered as a service to multiple users either directly or through virtual machines. </span></p>

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Strategi Efisiensi Energi dan Penyeimbangan Beban Kerja Layanan Cloud Computing Melalui Konsolidasi Mesin Virtual Dinamis

Applied Technology and Computing Science Journal ◽

10.33086/atcsj.v3i1.1680 ◽

2020 ◽

Vol 3 (1) ◽

pp. 1-12

Author(s):

Abdullah Fadil

Keyword(s):

Cloud Computing ◽

Virtual Machine ◽

Data Center ◽

Vm Migration ◽

Physical Machine

Arsitektur data center di dalam cloud computing merupakan lingkungan yang heterogen dan terdistribusi, tersusun atas gugusan jaringan physical machine (PM) atau server dengan berbagai kapasitas sumber daya komputasi yang berbeda-beda di dalam PMnya. Kondisi permintaan (demand) dan ketersediaan (supply) pada layanan cloud yang fluktuatif tersebut membuat data center cloud harus dibuat elastis. Virtual Machine (VM) merupakan representasi dari ketersediaan sumber daya komputasi dinamis yang dapat dialokasikan dan direlokasikan sesuai dengan permintaan. VM yang berada di dalam data center cloud dapat dipindahkan dari satu PM ke PM lainnya menggunakan migrasi VM secara langsung (live VM migration) ataupun tidak langsung (off-line VM migration). lingkungan cloud computing yang dinamis dan terdistribusi mengharuskan strategi pengambilan keputusan di dalam konsolidasi VM harus dibuat sedinamis mungkin atau bahkan adaptif dengan mempertimbangkan heterogenitas sumber daya virtual sesuai dengan layanan cloud computing yang disajikan. Sehingga, dalam penelitian ini diusulkan efisiensi energi sekaligus menjaga kinerja layanan cloud computing melalui penyeimbangan beban kerja dengan teknik migrasi VM yang terdapat pada prosedur konsolidasi VM dinamis. Strategi pengambilan keputusan pada prosedur konsolidasi virtual machine dinamis yang diusulkan, dapat meningkatkan kinerja layanan cloud computing sekaligus beban kerja physical machine menjadi seimbang karena keputusan pemilihan VM dan penempatan VM pada physical machine dipilih secara optimal melalui MADM. Konsumsi energi dari physical machine juga dapat di hemat dengan mematikannya karena statusnya idle.

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Virtual Machine Placement in Cloud Computing: Challenges, Research Gaps, and Future

10.54216/ijwac.030202 ◽

2021 ◽

pp. 64-71

Author(s):

Puneet Kaushal ◽

◽

Subash Chander ◽

Vijay Kumar Sinha ◽

◽

...

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Resource Utilization ◽

Virtual Machine ◽

Optimization Methods ◽

Virtual Machine Placement ◽

Cloud Environment ◽

Research Gaps ◽

Physical Machine ◽

Main Motive

Cloud computing provides various types of services to users. The goal of virtual machine placement (VMP) is to map the best physical machine to a virtual machine. With the help of Virtual Machine Placement, we can reduce cost, maximize resource utilization, reduced energy consumption of data centers in cloud environments. The focus of Virtual Machine Placement is to saving of power, quality of service. In this paper, we have reviewed various placements techniques used in cloud computing. At last, we have also studied various challenges for virtual machine placement in cloud computing. The main motive of various types of Virtual Machine Placement algorithms have to reduced energy consumption and minimize cost by maximizing utilization of various resources in the cloud platform. For further study, the researcher should focus on these challenges for the best virtual machine placement in a cloud environment. In this paper, we critically examine the techniques, challenges, and research gaps in virtual placements in cotext with Cloud Computing. Cloud computing, placement of virtual machines becomes major problems. For finding the solution to the problem we can use the various virtual machine placement algorithms. The main motive is to reduce consumption of energy, maximum resource utilization, minimizing cost factors used for virtual to the physical machine mapping in the cloud environment. For selecting the best algorithm various optimization methods are used. With these different optimization methods, we can analyze different algorithms. There is a great scope of improvement in existing systems of virtual placements to make them more energy-efficient, more reliable, and fault-tolerant. Redundancy in cloud downloading can be made more intelligent and minimized for duplicate data while downloading and uploading.

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A Hybrid Approach for Virtual Machine Migration in Cloud Computing Environment

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i9.407 ◽

2017 ◽

Vol 7 (9) ◽

pp. 30 ◽

Cited By ~ 1

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.

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