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Allocation and Migration of Virtual Machines Using Machine Learning

Suruchi Talwani1, Khaled Alhazmi2,*, Jimmy Singla1, Hasan J. Alyamani3, Ali Kashif Bashir4

1 School of Computer Science & Engg., Lovely Professional University, Punjab, India
2 National Center for Robotics and Internet of Things Technology, Communication and Information Technology Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 11442, Saudi Arabia
3 Department of Information Systems, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Rabigh, 21911, Saudi Arabia
4 Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, M156BH, UK

* Corresponding Author: Khaled Alhazmi. Email: email

(This article belongs to this Special Issue: Pervasive Computing and Communication: Challenges, Technologies & Opportunities)

Computers, Materials & Continua 2022, 70(2), 3349-3364.


Cloud computing promises the advent of a new era of service boosted by means of virtualization technology. The process of virtualization means creation of virtual infrastructure, devices, servers and computing resources needed to deploy an application smoothly. This extensively practiced technology involves selecting an efficient Virtual Machine (VM) to complete the task by transferring applications from Physical Machines (PM) to VM or from VM to VM. The whole process is very challenging not only in terms of computation but also in terms of energy and memory. This research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data centres. Machine Learning (ML) based Artificial Bee Colony (ABC) is used to rank the VM with respect to the load while considering the energy efficiency as a crucial parameter. The most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy, applications are migrated from one VM to another. The simulation analysis is performed in Matlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.


Cite This Article

S. Talwani, K. Alhazmi, J. Singla, H. J. Alyamani and A. Kashif Bashir, "Allocation and migration of virtual machines using machine learning," Computers, Materials & Continua, vol. 70, no.2, pp. 3349–3364, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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