Enhancing Distributed Cloud Data Engineering with AI-Based Failure Prediction Models

Abstract

In the evolving landscape of cloud computing, ensuring the reliability and efficiency of distributed data engineering systems is increasingly critical. This study explores the integration of Artificial Intelligence (AI) for failure prediction in distributed cloud data engineering environments. We propose and evaluate a novel AI-based failure prediction model that leverages machine learning algorithms to anticipate and mitigate potential system failures. The model combines historical data analysis with real-time monitoring to provide predictive insights, thereby enhancing the overall resilience of cloud infrastructure. Using a dataset of operational metrics from a large-scale distributed cloud environment, we benchmark the performance of our model against traditional failure detection methods. Our results demonstrate a significant improvement in prediction accuracy and lead time, with a reduction in both false positives and false negatives. This approach not only minimizes downtime but also optimizes resource allocation and system performance. The findings underscore the potential of AI-driven solutions to transform failure management in cloud environments, paving the way for more robust and efficient data engineering practices.