LEVERAGING KUBERNETES AND AI FOR IMPROVED DISASTER RECOVERY IN CLOUD COMPUTING

Abstract

This article presents a groundbreaking approach to disaster recovery in cloud computing by integrating Artificial Intelligence (AI) capabilities with Kubernetes container orchestration. The article introduces a novel multi-layered architecture that combines deep learning-based predictive analytics, automated recovery mechanisms, and intelligent resource optimization algorithms to enhance system resilience and minimize downtime. Our framework demonstrated remarkable improvements in key performance metrics through extensive testing across geographically distributed clusters, achieving a 73% reduction in Recovery Time Objective (RTO) and maintaining Recovery Point Objective (RPO) under 10 seconds for critical workloads. The implementation resulted in a 94% reduction in false positive failure predictions and a 78% increase in successful automated recoveries while reducing operational costs by 45%. The system's hybrid AI approach, combining supervised and unsupervised learning techniques, achieved 89% accuracy in failure prediction with a 15-minute warning window. This article provides comprehensive evidence that AI-enhanced Kubernetes orchestration represents a significant advancement in cloud infrastructure resilience, offering practical solutions for organizations requiring robust disaster recovery capabilities. The article demonstrates that this integrated approach improves system reliability and provides a cost-effective, scalable foundation for next-generation cloud computing disaster recovery strategies.