Due to the development of new technologies such as the Internet and cloud computing, high requirements have been placed on the storage and management of big data. At the same time, new applications in the cloud computing environment also pose new requirements for cloud storage systems, such as strong scalability and high concurrency. Currently, the existing nosql database system is based on cloud computing virtual resources, supporting dynamic addition and deletion of virtual nodes. Based on the study of phase space reconstruction, the necessity of considering traffic flow as a chaotic time series is analyzed. In addition, offline data migration methods based on load balancing are also studied. Firstly, a data migration model is proposed through analysis, and the factors that affect migration performance are analyzed. Based on this, optimization objectives for migration are proposed. Then, the system design of data migration is presented, and optimization research is conducted from two aspects around the migration optimization objectives: optimizing from the data source layer, and proposing the LBS method to convert data sources into distributed data sources, ensuring the balanced distribution of data and meeting the scalability requirements of the system. This paper applies cloud computing technology and phase space reconstruction to load balancing scheduling algorithms to promote their development.