Data Reliability Engineering in Multi-Cloud Environments: An AIDriven Approach

Abstract

In the evolving landscape of cloud computing, multi-cloud environments have emerged as a
critical strategy for enhancing data reliability and operational flexibility. However, managing data
reliability across diverse cloud platforms presents unique challenges, including data consistency,
fault tolerance, and performance optimization. This paper introduces an AI-driven approach to
data reliability engineering in multi-cloud environments, aiming to address these challenges by
leveraging advanced artificial intelligence techniques. The proposed framework integrates AIpowered tools for real-time monitoring, anomaly detection, and predictive maintenance, enabling
proactive management of data reliability issues. By employing machine learning algorithms and
automated decision-making processes, the framework enhances data consistency, reduces system
downtime, and optimizes resource utilization across multiple cloud platforms. Results from the
implementation of the AI-driven approach in a multi-cloud environment demonstrate significant
improvements in data integrity, fault detection accuracy, and overall operational efficiency. This
paper provides a comprehensive evaluation of the framework's effectiveness, including
quantitative metrics and performance comparisons with traditional methods. The findings offer
valuable insights into the benefits of incorporating AI into multi-cloud data reliability engineering
and highlight potential directions for future research.