ACSarF: a DRL-based adaptive consortium blockchain sharding framework for supply chain finance

Abstract

Blockchain technologies have been used to facilitate Web 3.0 and FinTech applications. However, conventional blockchain technologies suffer from long transaction delays and low transaction success rates in some Web 3.0 and FinTech applications such as Supply Chain Finance (SCF). Blockchain sharding has been proposed to improve blockchain performance. However, the existing sharding methods either use a static sharding strategy, which lacks the adaptability for the dynamic SCF environment, or are designed for public chains, which are not applicable to consortium blockchain-based SCF. To address these issues, we propose an adaptive consortium blockchain sharding framework named ACSarF, which is based on the deep reinforcement learning algorithm. The proposed framework can improve consortium blockchain sharding to effectively reduce transaction delay and adaptively adjust the sharding and blockout strategies to increase the transaction success rate in a dynamic SCF environment. Furthermore, we propose to use a consistent hash algorithm in the ACSarF framework to ensure transaction load balancing in the adaptive sharding system to further improve the performance of blockchain sharding in dynamic SCF scenarios. To evaluate the proposed framework, we conducted extensive experiments in a typical SCF scenario. The obtained experimental results show that the ACSarF framework achieves a more than 60% improvement in user experience compared to other state-of-the-art blockchain systems.

Keywords

Web 3.0 / Consortium blockchain / Blockchain sharding / Supply chain finance / FinTech regulation

CRediT authorship contribution statement

Shijing Hu: Conceptualization, Writing - original draft. Junxiong Lin: Data curation, Writing - original draft. Xin Du: Writing - review & editing. Wenbin Huang: Data curation. Zhihui Lu: Methodology. Qiang Duan: Formal analysis, Writing - review & editing. Jie Wu: Methodology.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The work of this paper is supported by the National Key Research and Development Program of China (2022YFC3302300), National Natural Science Foundation of China under Grant (No. 61873309, No. 92046024, No. 92146002) and Shanghai Science and Technology Project under Grant (No. 22510761000).

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