| Abstract: |
Cloud computing has revolutionized how computational resources are allocated and managed in modern digital environments. With increasing demand for cloud services, efficient resource allocation has become a critical challenge. Traditional allocation techniques often suffer from inefficiencies, leading to increased latency, resource wastage, and higher operational costs. This paper explores the use of deep learning techniques for predictive resource allocation in cloud computing. We discuss various deep learning models, including Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), and Reinforcement Learning (RL), that enhance the efficiency of cloud resource management. The study presents a comparative analysis of existing methodologies, explores the role of deep learning in real-time decision-making, and proposes a predictive framework for optimal resource allocation. |
