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Organizations are increasingly adopting AI-driven predictive analytics to optimize cloud resource management. This shift allows businesses to move beyond outdated resource allocation methods, enhancing efficiency and reducing costs. Siddharth Kumar Choudhary's research outlines a strategic framework that leverages AI and machine learning to improve cloud infrastructure performance.
Traditional cloud management methods have proven inadequate, with significant underutilization of resources. AI models, particularly those using Long Short-Term Memory networks, can predict workload demands with high accuracy, leading to better resource allocation. The integration of real-time monitoring and intelligent scheduling further enhances operational efficiency and sustainability.
• AI-driven analytics significantly enhance cloud resource management efficiency.
• Real-time monitoring reduces response time and improves system reliability.
Predictive analytics uses historical data to forecast future workload demands, optimizing resource allocation.
Machine learning algorithms analyze data patterns to improve decision-making in resource management.
LSTM networks are used for accurate forecasting of cloud resource demands, achieving up to 92% accuracy.
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