Cloud-Scale Data Reliability Engineering: A Deep Learning Approach for Failure Mitigation

Abstract

In the evolving landscape of cloud computing, ensuring data reliability is paramount to maintaining high availability and performance. This study presents a novel deep learning approach for failure mitigation in cloud-scale data engineering. Leveraging advanced deep learning techniques, we propose a comprehensive framework designed to predict, detect, and address potential data failures before they impact system performance. Our approach integrates Convolutional Neural Networks (CNNs) for feature extraction with Long Short-Term Memory (LSTM) networks for temporal sequence prediction, creating a hybrid model that enhances predictive accuracy and reliability. We evaluate the effectiveness of this framework using a largescale dataset from a leading cloud service provider, demonstrating its ability to accurately identify failure patterns and preemptively mitigate risks. The results show a significant reduction in failure rates and improvements in system uptime, highlighting the potential of deep learning to transform data reliability engineering in cloud environments. This work not only advances the state-of-theart in failure mitigation but also provides actionable insights for implementing robust data reliability strategies in complex cloud architectures.