Research on Data Migration Model in Hybrid Cloud

With the rapid development of cloud computing technology, hybrid cloud have become an important choice for enterprise data storage and computing. However, due to the complexity of hybrid cloud, how to perform data migration efficiently and securely has become an important research topic. in this paper, we study the data migration model in hybrid cloud, which combines machine learning and optimization algorithms to improve the efficiency and security of data migration. Firstly, this paper analyzes the shortcomings of existing data migration strategies and points out the problems in resource utilization, scheduling efficiency and system stability. Secondly, a mathematical model is proposed to achieve a balance between multiple performance indicators by introducing a reinforcement learning algorithm. Experimental validation shows that the model outperforms traditional migration methods in terms of data migration speed, resource utilization, and data integrity, and is able to effectively reduce migration costs and improve migration reliability. Finally, this paper discusses the challenges and future development directions of the model in practical applications, providing a new idea and reference for data migration in hybrid cloud.