AI-Generated Predictive Cloud Optimization: Preemptively Detecting and Preventing System Failures for Enhanced Cloud Reliability

Authors: Subhasis Kundu

DOI: https://doi.org/10.5281/zenodo.15084283

Short DOI: https://doi.org/g89sbb

Country: United States

Full-text Research PDF File:   View   |   Download

Abstract: This study examines the application of AI-driven predictive cloud optimization to enhance cloud reliability by forecasting and preventing system failures. An innovative method is proposed, employing machine learning algorithms to analyze extensive cloud infrastructure data, identify potential issues, and implement proactive measures. This approach integrates real-time monitoring, predictive analytics, and automated solutions to minimize downtime and improve resource management. A case study is presented, demonstrating the method's success in a large-scale cloud environment, with significant improvements in system reliability and performance. The findings indicate a substantial reduction in unexpected outages and a notable increase in the overall efficiency of cloud infrastructure. This research contributes to the field of cloud computing by offering a robust framework for AI-based predictive maintenance and optimization.

Keywords: Artificial Intelligence, Cloud Computing, Predictive Analytics, System Reliability, Machine Learning, Infrastructure Optimization, Proactive Maintenance, Cloud Optimization, Anomaly Detection, Automated Remediation.

Paper Id: 232297

Published On: 2023-11-24

Published In: Volume 11, Issue 6, November-December 2023

Share this