A machine learning model for improving virtual machine migration in cloud computing - paper overview

Aug 5, 2026

Cloud computing enables access to virtualized hardware and application resources over the Internet, and virtualization makes multiple virtual machines (VMs) available as a service. VM migration strongly influences cloud performance, yet unbalanced VM placement and server-to-server movement can raise energy consumption and network overhead. The work proposes a machine learning approach to reduce both the number of VM migrations and energy usage. The algorithm, VMLM, improves VM migration process and selection, using a two-phase pipeline for ML preparation and migration execution, benchmarked against JVCMMD and EVSP.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Cloud computing and virtualization

Virtual machine migration and its impact

VM selection and migration mechanics

Machine learning overview

Data processing before model building

A machine learning model for improving virtual machine migration in cloud computing Ali Belgacem1 · Saïd Mahmoudi2 · Mohamed Amine Ferrag3 Accepted: 29 December 2022 / Published online: 16 January 2023 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023 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. Keywords Cloud computing · Virtualization · VM migration · Machine learning · Energy consumption * Ali Belgacem[a.belgacem@univ-boumerdes.dz](a.belgacem@univ-boumerdes.dz) Saïd Mahmoudi [Said.MAHMOUDI@umons.ac.be](Said.MAHMOUDI@umons.ac.be) Mohamed Amine Ferrag [mohamed.ferrag@tii.ae](mohamed.ferrag@tii.ae) 1 LIMOSE laboratory, Faculty of Sciences, M’hamed Bougara University, Boumerdes, Algeria 2 Mons University, Mons, Belgium 3 Technology Innovation Institute, Abu Dhabi, United Arab Emirates 1 Introduction One of the essential contemporary advances in information technology is the emergence of the cloud computing paradigm. A cloud is a collection of hardware and software connected over a network. It has been widely adopted by people, businesses, and large companies. The main idea behind cloud computing is scalability, and virtualization is the critical technology that makes this possible [1] . In its broadest sense, virtualization creates a virtual platform of server operating systems and storage devices within a single physical computer. This allows multiple virtual machines to be provisioned simultaneously. So, virtual machines can share one physical machine. In other words, 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] . Virtualization technology has changed how data centers are configured and operated by providing new mechanisms for better sharing and controlling datacenter resources. Specifically, virtual machine migration is an effective management strategy that affects data center performance. It gives the ability to adjust the state of virtual machines according to the required performance while allocating resources, enhance resource usage, adapt to internal failures, reduce power consumption, and improve task scheduling [3] . For example, when a VM crashes due to a bug, the whole system does not crash, allowing the developer to debug the problem. Hence, it helps troubleshoot the issues. In addition, this technique can be done when a physical machine needs to be updated or shut down for maintenance [4] . The VMs migration technique transfers a VM from one physical host to another while the VM is still running. It is specially applied in current data enters to make computing dynamic, flexible, platform-independent, and efficient use and sharing of resources. Also, VM migration within/across data centers happens for power management, load balancing, availability, and redu

FAQs

Why is improving virtual machine migration important in cloud computing?

VM migration affects data center performance. Unbalanced VM distribution and frequent migrations can increase energy consumption and network overhead, so improvements are needed to reduce those costs.

What problem does the proposed VMLM approach address?

VMLM targets the VM migration issue by reducing the migration number and the resulting energy consumption, by enhancing both the migration process and the VM selection strategy.

How is the machine learning solution structured in the proposed method?

The approach uses two phases: an ML preparing stage followed by a VM migration stage, where the trained model supports selecting and executing migration decisions.

Aug 5, 2026

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