AI BASED CLOUD COMPUTATION METHOD AND PROCESS DEVELOPMENT

Abstract

Cloud computing has revolutionized how organizations deploy and manage computational resources, yet traditional cloud architectures face challenges in dynamic resource allocation, workload prediction, and cost optimization. This research presents the development and evaluation of an artificial intelligence-based cloud computation method designed to enhance resource utilization, reduce operational costs, and improve service quality in cloud environments. The proposed system integrates machine learning algorithms for workload prediction, reinforcement learning for dynamic resource allocation, and neural networks for anomaly detection. Through implementation across three cloud environments serving different application domains—web services, scientific computing, and enterprise applications—the AI-based method demonstrated significant improvements over traditional rule-based approaches. Results show an average 31% reduction in resource wastage, 24% improvement in response times, and 28% decrease in operational costs while maintaining service level agreements. The system achieved 89% accuracy in workload prediction and reduced SLA violations by 43%. This research contributes both a comprehensive AI-based framework for cloud resource management and empirical validation demonstrating practical feasibility and benefits. The findings suggest that intelligent automation in cloud computing represents not merely an optimization opportunity but an operational necessity as cloud environments grow increasingly complex and dynamic.