Deep Learning Techniques for Enhancing Data Reliability and Failure Mitigation in Large-Scale Cloud Infrastructures

Abstract

Ensuring data reliability and mitigating failures are critical challenges in large-scale cloud infrastructures, given their complexity, dynamic nature, and the increasing demand for real-time data processing. Traditional approaches often struggle with scalability, adaptability, and predictive accuracy, necessitating innovative solutions. Deep learning, with its ability to model complex patterns and predict outcomes, has emerged as a transformative tool for addressing these challenges.

This article explores the application of deep learning techniques to enhance data reliability and failure mitigation in large-scale cloud systems. It examines methods such as anomaly detection using auto-encoders and convolutional neural networks (CNNs), predictive maintenance through recurrent neural networks (RNNs) and long short-term memory (LSTM) models, and fault localization enabled by deep reinforcement learning. Additionally, intelligent resource allocation, adaptive scaling, and data recovery processes are highlighted as critical areas where deep learning delivers significant advancements.

Through real-world case studies and experimental evaluations, the research demonstrates the superiority of deep learning approaches over traditional methods in terms of accuracy, scalability, and efficiency. While the findings underscore deep learning's potential, the discussion also addresses limitations, ethical considerations, and integration challenges. This study not only establishes a framework for leveraging deep learning in cloud reliability and resilience but also outlines future directions for research, emphasizing model interpret-ability, federated learning, and sustainable AI practices.