<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Enhancing the fault tolerance of cloud systems and accurately forecasting cloud performance are pivotal concerns in cloud computing research. This research addresses critical concerns in cloud computing by enhancing fault tolerance and forecasting cloud performance using machine learning models. Leveraging the Google trace dataset with 10000 cloud environment records encompassing diverse metrics, we systematically have employed machine learning algorithms, including linear regression, decision trees, and gradient boosting, to construct predictive models. These models have outperformed baseline methods, with C5.0 and XGBoost showing exceptional accuracy, precision, and reliability in forecasting cloud behavior. Feature importance analysis has identified the ten most influential factors affecting cloud system performance. This work significantly advances cloud optimization and reliability, enabling proactive monitoring, early performance issue detection, and improved fault tolerance. Future research can further refine these predictive models, enhancing cloud resource management and ultimately improving service delivery in cloud computing.</p>
</abstract>

Enhancing the fault tolerance of cloud systems and accurately forecasting cloud performance are pivotal concerns in cloud computing research. This research addresses critical concerns in cloud computing by enhancing fault tolerance and forecasting cloud performance using machine learning models. Leveraging the Google trace dataset with 10000 cloud environment records encompassing diverse metrics, we systematically have employed machine learning algorithms, including linear regression, decision trees, and gradient boosting, to construct predictive models. These models have outperformed baseline methods, with C5.0 and XGBoost showing exceptional accuracy, precision, and reliability in forecasting cloud behavior. Feature importance analysis has identified the ten most influential factors affecting cloud system performance. This work significantly advances cloud optimization and reliability, enabling proactive monitoring, early performance issue detection, and improved fault tolerance. Future research can further refine these predictive models, enhancing cloud resource management and ultimately improving service delivery in cloud computing.

Fault Tolerance of Cloud Infrastructure with Machine Learning

Department of Computer Science and Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Fault Tolerance of Cloud Infrastructure with Machine Learning"}

Enhancing the fault tolerance of cloud systems and accurately forecasting cloud performance are pivotal concerns in cloud computing research. This research...

Fault Tolerance of Cloud Infrastructure with Machine Learning

Online veröffentlicht: 30. Nov. 2023

Seitenbereich: 26 - 50

Eingereicht: 13. Juli 2023

Akzeptiert: 07. Nov. 2023

DOI: https://doi.org/10.2478/cait-2023-0034

Schlüsselwörter
Cloud computing, Fault tolerance, Machine learning, Reliability of cloud

© 2023 Chetankumar Kalaskar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fault Tolerance of Cloud Infrastructure with Machine Learning

Online veröffentlicht: 30. Nov. 2023

Seitenbereich: 26 - 50

Eingereicht: 13. Juli 2023

Akzeptiert: 07. Nov. 2023

DOI: https://doi.org/10.2478/cait-2023-0034

SchlüsselwörterCloud computing, Fault tolerance, Machine learning, Reliability of cloud

© 2023 Chetankumar Kalaskar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Cloud computing, Fault tolerance, Machine learning, Reliability of cloud