Research on Cloud Storage Optimization of Distributed Ledgers Based on Big Data

Abstract

This work proposes a complete set of optimization techniques that improve the performance of distributed ledger technology (DLT) systems over cloud storage environments under big data conditions and experimentally verifies several strategies’ efficiency in regard to storage and consistency. First, at the storage level, a compression optimization method manages to drastically reduce the occupied space by DLT data. Experimental results show that an appropriate compression ratio can improve storage efficiency and effectively manage access delay to maintain the overall responsiveness of the system. In terms of data consistency strategies, it uses an eventual consistency strategy that can be used to guarantee data reliability and consistency to achieve a DXGI-level throughput improvement. This approach will be able to satisfy the real-time responsiveness and accuracy of data of DLT systems in big data scenarios while further optimizing resource utilization. Furthermore, the comparison of several experimental results can show how different optimization strategies influence some key metrics, such as storage efficiency, access delay, throughput, and consistency, under different parameter settings. These findings serve both a theoretical and practical foundation for DLT systems that can work more efficiently in cloud environments. The result of this research carries high values in showing lessons for the future cloud storage optimization of DLT, and also lays a foundation for its wider application and promotion in big data-driven scenarios.