Optimizing Cloud Resources with AI-Driven Machine Learning Algorithms

Authors

  • Anand Polamarasetti M.C.A (Master of Computer Applications) Andhra University, Visakhapatnam, AP, INDIA exploretechnologi@gmail.com Author

Keywords:

Cloud Resource Optimization, AI-Driven Machine Learning, Reinforcement Learning, Predictive Modeling, Resource Allocation, Cloud Computing Efficiency, Long ShortTerm Memory (LSTM).

Abstract

Efficient management of cloud resources is critical for achieving optimal performance and costeffectiveness in cloud computing environments. This paper explores the integration of AI-driven machine learning algorithms to optimize cloud resource allocation, scaling, and utilization. We propose a framework that leverages advanced machine learning techniques, including reinforcement learning and neural network-based predictive models, to enhance resource management in cloud infrastructures. The proposed framework incorporates real-time data analytics to dynamically adjust resource allocations based on workload patterns and performance metrics. Through extensive experimentation with various machine learning models, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), we demonstrate significant improvements in resource utilization efficiency and cost savings. The results indicate that the AI-driven approach outperforms traditional static resource allocation methods, achieving up to 30% reduction in resource wastage and up to 25% reduction in operational costs. The study provides insights into the application of AI in optimizing cloud resources, offering a novel solution to enhance scalability and performance in modern cloud environments.

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Published

2018-06-02

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