Enhancing Security and Performance in Live VM Migration: A Machine Learning‐Driven Framework With Selective Encryption for Enhanced Security and Performance in Cloud Computing Environments DOI Creative Commons

Raseena M. Haris,

Mahmoud Barhamgi, Ahmed Badawy

et al.

Expert Systems, Journal Year: 2025, Volume and Issue: 42(2)

Published: Jan. 9, 2025

ABSTRACT Live virtual machine (LVM) migration is pivotal in cloud computing for its ability to seamlessly transfer machines (VMs) between physical hosts, optimise resource utilisation, and enable uninterrupted service. However, concerns persist regarding safeguarding sensitive data during migration, particularly critical sectors like healthcare, banking military operations. Existing methods often compromise performance security, prompting the need a balanced solution. To address this, we propose novel framework merging learning with selective encryption fortify pre‐copy live process. Our approach intelligently predicts optimal times while selectively encrypting data, ensuring confidentiality integrity without compromising performance. Rigorous experiments demonstrate effectiveness, showcasing an average 51.82% reduction downtime 72.73% decrease total time across diverse workloads. This integration of not only bolsters security but also optimises metrics, presenting robust solution service delivery domains.

Language: Английский

Enhancing Security and Performance in Live VM Migration: A Machine Learning‐Driven Framework With Selective Encryption for Enhanced Security and Performance in Cloud Computing Environments DOI Creative Commons

Raseena M. Haris,

Mahmoud Barhamgi, Ahmed Badawy

et al.

Expert Systems, Journal Year: 2025, Volume and Issue: 42(2)

Published: Jan. 9, 2025

ABSTRACT Live virtual machine (LVM) migration is pivotal in cloud computing for its ability to seamlessly transfer machines (VMs) between physical hosts, optimise resource utilisation, and enable uninterrupted service. However, concerns persist regarding safeguarding sensitive data during migration, particularly critical sectors like healthcare, banking military operations. Existing methods often compromise performance security, prompting the need a balanced solution. To address this, we propose novel framework merging learning with selective encryption fortify pre‐copy live process. Our approach intelligently predicts optimal times while selectively encrypting data, ensuring confidentiality integrity without compromising performance. Rigorous experiments demonstrate effectiveness, showcasing an average 51.82% reduction downtime 72.73% decrease total time across diverse workloads. This integration of not only bolsters security but also optimises metrics, presenting robust solution service delivery domains.

Language: Английский

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