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Dynamic sharding model and performance optimization method for consortium blockchain

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Abstract

Consortium blockchain has been widely used in finance, e-government due to the characteristics of controllability, supervision, and operability. However, traditional consortium blockchains have bottlenecks in throughput. To solve the bottlenecks, the paper proposes a Dynamic Sharding Model and Performance Optimization Method for Consortium Blockchain (DSPO-CB), which offers a new shard architecture and dynamically optimizes the architecture through the Deep Q-Network (DQN). Firstly, the model reduces redundancy and improves space utilization by classifying the nodes ensuring security. Secondly, the model proposes the shard structure through a dynamic clustering method based on the node status to reduce the proportion of cross-shard transactions. Finally, the DQN is used to dynamically optimize the sharding and consensus architecture. Experiments show that DSPO-CB improves the throughput by 33% and saves up to 78% storage space compared with the existing consortium blockchain.

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Funding

This work is supported by the National Key Research and Development Program (Grant No.2023YFC3304904), the Artificial Intelligence Technology Innovation Project of Liaoning Province (Grant No. 2023JH26/10300019), the Basic Research Project of Liaoning Provincial Department of Education for Universities, (Grant No. LJ242410140013).

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Author notes

  1. Y. Wang, Z. Gong, A. Tan, and M. Liu contributed equally to this work.

Authors and Affiliations

  1. School of Information, Liaoning University, Shenyang, 110036, Liaoning, China

    Yan Wang, Dayu Jia, Aiping Tan & Minchao Liu

  2. School of Computer Science and Software Engineering, Dalian Neusoft University of Information, Dalian, 116023, China

    Zheng Gong

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  1. Yan Wang
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  2. Zheng Gong
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  3. Dayu Jia
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Contributions

YW: investigation; writing review & editing (lead). ZG: writing-original draft preparation; software; methodology; validation; writing review (equal). DJ: software; reviewing (equal). AT: methodology; investigation(equal). ML: discussion; reviewing (equal).

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Correspondence to Dayu Jia.

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Wang, Y., Gong, Z., Jia, D. et al. Dynamic sharding model and performance optimization method for consortium blockchain. J Supercomput 81, 411 (2025). https://doi.org/10.1007/s11227-024-06870-8

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  • DOI: https://doi.org/10.1007/s11227-024-06870-8

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