Optimization and performance improvement of distributed data storage in hybrid storage systems

With the rapid development of information technology, the storage and processing of massive data has become one of the important challenges facing the current computing field. Traditional centralized storage systems have been unable to meet the needs of big data applications, while distributed storage systems have become the infrastructure of modern data centers because of their high scalability and high availability. However, in practical applications, a single distributed storage model is often difficult to balance cost-effectiveness and performance requirements. Therefore, this paper proposes a hybrid storage system model combining local storage and cloud storage resources and discusses a series of optimization strategies for this model. The focus of the research is to improve the overall performance of the system through the design of intelligent data sharding, redundancy and fault tolerance mechanisms, and the application of effective load balancing technology. The effectiveness and superiority of the proposed method are verified by testing and analyzing several typical application scenarios on the experimental platform. The experimental results show that the optimized hybrid storage system not only significantly improves the speed of data access, but also effectively reduces the storage cost, demonstrating its potential in future large-scale data management.