Adaptive Resource Allocation in Cloud Environments: A Deep Learning Approach to Data Reliability

Abstract

Adaptive resource allocation in cloud environments is crucial for ensuring data reliability and system performance, especially in the face of dynamic workloads and varying user demands. This paper presents a novel deep learning approach for adaptive resource allocation, leveraging advanced neural network architectures to predict and manage resource needs in real-time. By integrating deep learning models with traditional resource management strategies, the proposed approach enhances the efficiency and effectiveness of resource allocation processes. The methodology includes the development of a predictive model using Long Short-Term Memory (LSTM) networks to forecast resource requirements based on historical usage patterns. The system's performance is evaluated through a series of simulations and real-world case studies, demonstrating significant improvements in resource utilization, cost efficiency, and data reliability. The results indicate that deep learning-based adaptive resource allocation can significantly reduce resource wastage and improve system responsiveness, making it a valuable strategy for modern cloud environments.