ENHANCING CLOUD COMPUTING PERFORMANCE THROUGH AI-DRIVEN DYNAMIC RESOURCE ALLOCATION AND AUTO-SCALING STRATEGIES
Keywords:
Cloud Computing, Artificial Intelligence, Dynamic Resource Allocation, Auto-Scaling, Machine Learning, Performance OptimizationAbstract
With the growing adoption of cloud computing, managing resources efficiently has become a critical challenge. Traditional resource allocation and auto-scaling techniques rely on rule-based policies, which often lead to suboptimal performance and increased operational costs. This paper explores AI-driven methods for dynamic resource allocation and auto-scaling in cloud environments. By integrating machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques, cloud platforms can predict workload demands, optimize resource distribution, and ensure high availability while reducing costs. A comparative analysis highlights the superiority of AI-based solutions over traditional static and reactive scaling approaches. The findings demonstrate that AI-driven cloud computing optimizes efficiency, reliability, and sustainability, paving the way for intelligent cloud infrastructures.
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