Hybrid Models for AI-Powered Automation In Cloud Data Reliability Engineering - Pentyala, Dillep kumar (International Journal of Scientific Research and Management (IJSRM), 2021)
The rapid growth of cloud computing has intensified the need for robust data reliability engineering to ensure system resilience and service continuity. Traditional approaches, relying on manual processes or rule-based automation, often fail to meet the demands of dynamic and complex cloud environments. While AI-driven solutions have emerged as alternatives, they face challenges such as limited adaptability, over-fitting, and interpret-abilityinterpretability issues.This research explores hybrid AI models as a novel approach to automating cloud data reliability tasks. By integrating machine learning, deep learning, and rule-based systems, hybrid models combine the strengths of these paradigms to deliver enhanced scalability, adaptability, and precision in detecting and mitigating reliability issues. The study proposes a comprehensive framework that includes data preprocessing, ensemble learning, and feedback-driven optimization for real-time monitoring and fault resolution.Experimental validation using synthetic and real-world datasets demonstrates that hybrid AI models outperform traditional and single-model approaches, particularly in handling dynamic workloads and large-scale environments. Key performance improvements include reduced downtime and enhanced resource efficiency.This research highlights hybrid AI models as a transformative tool for cloud reliability engineering, offering insights for future applications in multi-cloud and edge computing scenarios while addressing scalability, security, and ethical challenges.