AI-Powered Automation of Cloud Database Management using Deep Reinforcement Learning and Digital Twins

Abstract

With an emphasis on financial applications with extremely high performance requirements, this article explores the revolutionary combination of deep reinforcement learning and digital twin technologies for automating cloud database administration. As data volumes and query workloads increase, cloud database systems become more sophisticated, creating management challenges that conventional methods are unable to handle. A potent framework for automated management is produced by combining digital twins, which offer safe virtual replicas for training, with DRL, which enables autonomous learning through contextual interaction. Important functions, including workload management, disaster recovery procedures, resource allocation, query execution planning, compliance maintenance, and cost efficiency optimization, are all enhanced by this relationship. While administrators can do in-depth "what-if" analyses, digital twins offer safe environments for agent training. The integrated system employs phased deployment approaches, customized multi-agent architectures, and sophisticated training mechanisms, including offline reinforcement learning and curriculum learning, to guarantee reliability and safety. Technology convergence benefits financial institutions greatly by resulting in much better performance metrics, more robust systems, and reduced operating expenses while maintaining strict regulatory compliance. While federated learning techniques enable collaborative growth without compromising data privacy, explainable AI systems provide the transparency and auditability needed in financial settings.