AI-Powered Decision Making for Data Reliability in Distributed Cloud Infrastructures

Abstract

As distributed cloud infrastructures continue to expand and evolve, ensuring data reliability becomes increasingly challenging. This paper explores the integration of artificial intelligence (AI) techniques to enhance decision-making processes related to data reliability in distributed cloud environments. We propose a novel framework that leverages machine learning algorithms and predictive analytics to optimize data consistency, fault detection, and recovery strategies. By employing a combination of supervised and unsupervised learning models, our approach dynamically adjusts to varying workloads and failure patterns, thus improving overall system resilience. Through extensive experimentation and real-world case studies, we demonstrate the efficacy of AI-powered decision-making in managing data reliability, offering a significant advancement over traditional methods.