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The Journal of Supercomputing

Erschienen in:

16.01.2023

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

verfasst von: Ali Belgacem, Saïd Mahmoudi, Mohamed Amine Ferrag

Erschienen in: The Journal of Supercomputing | Ausgabe 9/2023

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Abstract

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. Virtual machines (VMs) migration technique provided by virtualization technology impacts the performance of the cloud. It is a significant concern in this environment. When allocating resources, the distribution of VMs is unbalanced, and their movement from one server to another can increase energy consumption and network overhead, necessitating an improvement in VM migrations. This paper addresses the VMs migration issue by applying a machine learning model to reduce the VMs migration number and energy consumption. The proposed algorithm (named VMLM) is based on improving VM’s migration process and selection. It has been benchmarked with JVCMMD and EVSP solutions. The simulation results demonstrate the efficiency of our proposal, which includes two phases the machine learning preparing stage and the VMs migration stage.

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Titel: A machine learning model for improving virtual machine migration in cloud computing
verfasst von: Ali Belgacem
Saïd Mahmoudi
Mohamed Amine Ferrag
Publikationsdatum: 16.01.2023
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 9/2023
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-022-05031-z

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