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Designing Intelligent Cloud Infrastructure Powered by Artificial Intelligence–Based Distributed System Optimization

Abstract

The rapid expansion of cloud computing has created complex infrastructure environments that require advanced mechanisms for resource management, scalability, reliability, and operational efficiency. Traditional distributed system optimization techniques often struggle to address the dynamic and unpredictable nature of modern cloud workloads. Artificial intelligence (AI)-based optimization provides an emerging approach for designing intelligent cloud infrastructures capable of autonomous decision-making, adaptive resource allocation, and continuous performance improvement. This research explores the integration of artificial intelligence techniques, including machine learning, deep learning, reinforcement learning, and predictive analytics, into distributed cloud system optimization frameworks. The study investigates how AI-driven approaches enhance workload scheduling, energy efficiency, fault prediction, network management, and security optimization across large-scale cloud environments. A comprehensive research methodology combining theoretical analysis, architectural evaluation, simulation-based experimentation, and performance assessment is proposed to examine the effectiveness of intelligent cloud infrastructure models. The research emphasizes the development of self-adaptive cloud ecosystems that can automatically respond to changing computational demands while minimizing operational costs and improving service quality. The findings contribute to the advancement of next-generation cloud platforms by demonstrating how AI-enabled distributed optimization can support autonomous, resilient, and sustainable infrastructure management

References

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