International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Artificial Intelligence and Machine Learning in Cloud Systems: A Comprehensive Framework for Real-Time Optimization

Author(s) Varshini Choudary Nuvvula
Country United States
Abstract Abstract This article comprehensively analyzes artificial intelligence and machine learning integration in real-time cloud-based system optimization. The current research and emerging technologies examine how AI-driven algorithms enhance dynamic resource allocation, workload management, and automated decision-making processes in cloud computing environments. The article investigates the implementation of predictive analytics for workload forecasting, machine learning-based anomaly detection, and reinforcement learning approaches for autonomous system optimization. The findings demonstrate significant improvements in resource utilization efficiency, load balancing effectiveness, and system response time compared to traditional rule-based methods. The article also reveals that AI-powered auto-scaling mechanisms substantially enhance cloud system adaptability to varying workload patterns while minimizing operational costs. Moreover, it identifies key challenges in implementing these technologies, including integration complexity and performance overhead considerations, and proposes practical solutions for enterprise adoption. This article contributes to the growing knowledge in cloud computing optimization and provides valuable insights for researchers and practitioners in cloud infrastructure management.
Keywords Abstract This article comprehensively analyzes artificial intelligence and machine learning integration in real-time cloud-based system optimization. The current research and emerging technologies examine how AI-driven algorithms enhance dynamic resource allocation, workload management, and automated decision-making processes in cloud computing environments. The article investigates the implementation of predictive analytics for workload forecasting, machine learning-based anomaly detection, and reinforcement learning approaches for autonomous system optimization. The findings demonstrate significant improvements in resource utilization efficiency, load balancing effectiveness, and system response time compared to traditional rule-based methods. The article also reveals that AI-powered auto-scaling mechanisms substantially enhance cloud system adaptability to varying workload patterns while minimizing operational costs. Moreover, it identifies key challenges in implementing these technologies, including integration complexity and performance overhead considerations, and proposes practical solutions for enterprise adoption. This article contributes to the growing knowledge in cloud computing optimization and provides valuable insights for researchers and practitioners in cloud infrastructure management.
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-13
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.32566
Short DOI https://doi.org/g8wkph
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