VMware® Certified Professional Data Center Virtualization on vSphere 6.7 Exam 2V0‐21.19

Hall, Jon Maddog; Andrews, Joshua

doi:10.1002/9781119539698

Cited by 2 publications

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“…Therefore, sharing a single physical resource instance or application with different users. More precisely, the virtualization tool simulates hardware resources to create a fully functional virtual machine capable of installing an operating system and associated applications just as one would on a physical machine (PM) [2].…”

Section: Introductionmentioning

confidence: 99%

A machine learning model for improving virtual machine migration in cloud computing

2023

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These resources are virtualized using virtualization software to make them available to users as a service. In this environment, the migration of virtual machines (VMs) is a significant concern these days. This technique provided by virtualization technology impacts the performance of the cloud. When allocating resources, the distribution of VMs is unbalanced, and their migration from one server to another can increase energy consumption and network overhead, necessitating an improvement in VM migrations. This paper presents a machine learning model for migrating virtual machines. The goal is to improve the selection of virtual machines and migration processes by reducing energy consumption and the number of VMs migrations. Numerical results demonstrate that the proposed solution can significantly enhance the goals addressed.

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Section: Introductionmentioning

confidence: 99%

A machine learning model for improving virtual machine migration in cloud computing

2023

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A machine learning model for improving virtual machine migration in cloud computing

Belgacem,

Mahmoudi,

Ferrag

2022

Preprint

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Cloud Computing is a paradigm allowing access to physical and application resources online via the Internet. These resources are virtualized using virtualization software to make them available to users as a service. In this environment, the migration of virtual machines (VMs) is a significant concern these days. This technique provided by virtualization technology impacts the performance of the cloud. When allocating resources, the distribution of VMs is unbalanced, and their migration from one server to another can increase energy consumption and network overhead, necessitating an improvement in VM migrations. This paper presents a machine learning model for migrating virtual machines. The goal is to improve the selection of virtual machines and migration processes by reducing energy consumption and the number of VMs migrations. Numerical results demonstrate that the proposed solution can significantly enhance the goals addressed.

show abstract

VMware® Certified Professional Data Center Virtualization on vSphere 6.7 Exam 2V0‐21.19

Cited by 2 publications

References 0 publications

A machine learning model for improving virtual machine migration in cloud computing

A machine learning model for improving virtual machine migration in cloud computing

A machine learning model for improving virtual machine migration in cloud computing

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