Open Access

ARTICLE

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

K. Karthikeyan^1,*, Liyakathunisa², Eman Aljohani², Thavavel Vaiyapuri³

1 Department of Computer Applications, Anna University, Regional Campus, Madurai, Madurai, India
2 Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia
3 College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia

* Corresponding Author: K. Karthikeyan. Email:

Computer Systems Science and Engineering 2023, 45(2), 1641-1654. https://doi.org/10.32604/csse.2023.031582

Received 21 April 2022; Accepted 08 June 2022; Issue published 03 November 2022

Abstract

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

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Keywords

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