A Concurrent Conflict Transaction Optimization Method for Consortium Blockchain Hyperledger Fabric

Wu Haibo; Liu Hui; Sun Yi; Li Jun

doi:10.7544/issn1000-1239.202220644

Journal of Computer Research and Development > 2024 > 61(8): 2110-2126. > DOI: 10.7544/issn1000-1239.202220644

Wu Haibo, Liu Hui, Sun Yi, Li Jun. A Concurrent Conflict Transaction Optimization Method for Consortium Blockchain Hyperledger Fabric[J]. Journal of Computer Research and Development, 2024, 61(8): 2110-2126. DOI: 10.7544/issn1000-1239.202220644

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A Concurrent Conflict Transaction Optimization Method for Consortium Blockchain Hyperledger Fabric

Wu Haibo^{1, 2,},
Liu Hui^{1, 2},
Sun Yi^{3, 4},
Li Jun^{1, 2, ,}

1.
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
Shandong Key Laboratory of Blockchain Finance (Shandong University of Finance and Economics), Jinan 250014
4.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190

Funds: This work was supported by the National Key Research and Development Program of China (2021YFE0111500), the International Partnership Program of the Chinese Academy of Sciences (241711KYSB20200023), and the Frontier Research on Open Science Infrastructure Governance (CNIC20220101).

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Author Bio:
Wu Haibo: born in 1981. PhD, associate professor. Senior member of CCF. His current research interests include blockchain technology，future Internet architecture, data-driven network, and NLP

Liu Hui: born in 1996. Master. His main research interests include blockchain technology, NDN, and distributed database

Sun Yi: born in 1979. PhD, professor. His main research interests include blockchain technology, distributed applications, and Internet video distribution

Li Jun: born in 1968. PhD, professor. His research interests include Internet security, Internet architecture, artificial intelligence, and big data application
Received Date: July 23, 2022
Revised Date: October 19, 2023
Available Online: March 13, 2024

Graphical Abstract

Abstract

Abstract

With the prevalence of blockchain technology, Hyperledger Fabric (Fabric for short), as a well-known open source blockchain platform, has received wide attention. However, Fabric still suffers from conflicts between concurrent transactions. Conflicts will cause a large number of invalid transactions entering the chain, resulting in a decrease in throughput and hindering its development. For this problem, existing intra-block-conflict-oriented schemes lack efficient conflict detection and avoidance methods, and ignore the adverse impact of inter-block conflicts on throughput. We propose an optimization scheme for Fabric, Fabric-HT (Fabric with high throughput), from both intra-block and inter-block aspects to effectively reduce concurrency inter-transaction conflicts and improve system throughput. For intra-block transaction conflicts, we present a transaction scheduling mechanism, in which an efficient data structure (the dependency chain) is defined to identify and abort transactions with “dangerous structures” in advance, reasonably schedule transactions and eliminate conflicts; for inter-block transaction conflicts, the conflict transaction detection is moved to the sorting node to complete, and an early conflict transaction avoidance mechanism following “push-match” pattern is established. A large number of experiments are carried out in multiple scenarios, and the results show that Fabric-HT overperforms existing schemes in terms of throughput, transaction abort rate, average transaction execution time, and invalid transaction space occupancy. The results show that the throughput of Fabric-HT can reach up to 9.51x that of Fabric and 1.18x of the latest optimized scheme FabricSharp; compared with FabricSharp, the space utilization is increased by 14%. In addition, Fabric-HT also shows good robustness and anti-attack ability in solving concurrent transaction conflict.
- concurrency conflict,
- blockchain,
- Hyperledger Fabric,
- transaction scheduling,
- conflict detection

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References (37)

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