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Scalable blockchain storage systems: research progress and models

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Abstract

Blockchain is believed to be able to build trust among multiple parties and improve the operational efficiency of economy and society, which is attributed to its decentralization property. However, the endless growth of Blockchain data keeps challenging the storage capacity of Blockchain nodes, compromising decentralization, and revealing the issue that the state-of-the-art storage systems of Blockchain are not scalable. From the perspective of improving the scalability of Blockchain storage systems, this paper introduces the logical and physical data structure used by Blockchain storage systems, surveys the current schemes of Blockchain storage systems in terms of the approaches to reducing data redundancy, the corresponding influence on its degree of decentralization and data reliability, and conducts quantitative analysis on data redundancy for schemes of Blockchain storage systems. The study finds that the key to realizing scalable Blockchain storage systems is to deal with the contradiction between data redundancy and its decentralization characteristic. Based on the findings, the node-based scalable model for Blockchain storage systems (SMBSS) is proposed, experimental analysis on the prototypes of the SMBSS is carried out to verify its validity, and future directions for scalable Blockchain storage systems are concluded.

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Acknowledgements

This research is supported by National Natural Science Foundation of China (62072326) and the International Cooperation Project of the Major Research, Development Program of Shanxi (201903D421007) and Hubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology (201903).

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  1. School of Information and Computer, Taiyuan University of Technology, No. 209, Daxue Street, Jinzhong, 030600, Shanxi, China

    Xing Fan, Baoning Niu & Zhenliang Liu

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  1. Xing Fan
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Correspondence to Baoning Niu.

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Fan, X., Niu, B. & Liu, Z. Scalable blockchain storage systems: research progress and models. Computing 104, 1497–1524 (2022). https://doi.org/10.1007/s00607-022-01063-8

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