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Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Tahir Alyas1, Iqra Javed1, Abdallah Namoun2, Ali Tufail2, Sami Alshmrany2, Nadia Tabassum3,*
1 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
2 Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, 42351, Saudi Arabia
3 Department of Computer Science, Virtual University of Pakistan, Lahore, 54000, Pakistan
* Corresponding Author: Nadia Tabassum. Email:

Computers, Materials & Continua 2022, 71(2), 3019-3033.

Received 27 April 2021; Accepted 17 August 2021; Issue published 07 December 2021


Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in cross-data-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.


Cloud computing; IaaS; data centre; storage; performance analysis; live migration

Cite This Article

T. Alyas, I. Javed, A. Namoun, A. Tufail, S. Alshmrany et al., "Live migration of virtual machines using a mamdani fuzzy inference system," Computers, Materials & Continua, vol. 71, no.2, pp. 3019–3033, 2022.

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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