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Proposal Distribution optimization for Endorsement Strategy in Hyperledger Fabric

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Abstract

High throughput and low latency are essential for the endorsement phase in the Hyperledger Fabric system (HFS). Currently, endorser nodes can be selected by clients through static configuration or service discovery to verify user-submitted proposals. However, the transparent identities of the endorser nodes in the HFS channel render them more vulnerable to attacks, and nodes with high resource consumption among the endorsers can worsen system performance. To overcome these limitations, we propose Pdo-Fabric, a proposal distribution strategy that determines the distribution weight of proposals among organizations and dynamically selects endorsers based on the resource load of nodes. This paper discusses this strategy from the following aspects: (1) the transaction latency among organizations and the selection of endorsers are investigated, together with the formalization of the problem for the endorsement phase including endorser’s resource load, endorser consensus, and proposal communication latency. (2) Based on fuzzy logic, the CPU and memory resources of the endorsers are fuzzified to select endorser nodes. The number of endorser nodes is determined by a user-defined fault-tolerance probability. (3) Clients perceive the latency in processing transaction proposals and form a roulette based on the latency among multiple organizations. This roulette determines the weight of which organization will receive the distributed proposals. (4) Pdo-Fabric is implemented on top of Hyperledger Fabric platform and evaluated on metrics such as system throughput and response latency in a simulated Hyperledger Fabric environment. With a comprehensive evaluation, the proposed Pdo-Fabric system exhibits promising advancements over the existing Hyperledger Fabric endorsement strategy.

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Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code Availability

The code for the performance evaluation is available from the corresponding author on reasonable request.

References

  1. Ernest B, Shiguang J (2020) Privacy enhancement scheme (pes) in a blockchain-edge computing environment. IEEE Access 8:25863–25876

    Article  Google Scholar 

  2. Sodhro AH, Pirbhulal S, Muzammal M, Zongwei L (2020) Towards blockchain-enabled security technique for industrial internet of things based decentralized applications. J Grid Comput 18:615–628

    Article  Google Scholar 

  3. Foschini L, Martuscelli G, Montanari R, Solimando M (2020) Smart contracts for service-level agreements in edge-to-cloud computing. J Grid Comput 18:673–690

    Article  Google Scholar 

  4. Pires A, Simao J, Veiga L (2021) Distributed and decentralized orchestration of containers on edge clouds. J Grid Comput 19:36

    Article  Google Scholar 

  5. Bajoudah S, Dong C, Missier P (2019) Toward a decentralized, trust-less marketplace for brokered iot data trading using blockchain. In: 2019 IEEE International Conference on Blockchain (Blockchain), pp 339–346

  6. Ahamed NN, Thivakaran TK, Karthikeyan P (2021) Perishable food products contains safe in cold supply chain management using blockchain technology. In: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp 167–172

  7. Wu S, Du J (2019) Electronic medical record security sharing model based on blockchain. Association for Computing Machinery, ???

  8. Wu M, Zhu G, Wu S (2020) Improved consensus mechanism of blockchain based on proof-of-work and proof-of-stake. J Comput Appl 40:2274–2278

    Google Scholar 

  9. Li W, Andreina S, Bohli J, Karame G (2017) Securing proof-of-stake blockchain protocols. Data Privacy Management, Cryptocurrencies and Blockchain Technology, pp 297–315

  10. ZG, LY (2020) Optimization scheme of consensus mechanism based on practical byzantine fault tolerance algorithm. (eds) Blockchain Technology and Application. CBCC 2019. Communications in Computer and Information Science 1176

  11. Huang D, Ma X, Zhang S (2020) Performance analysis of the raft consensus algorithm for private blockchains. EEE Trans Syst Man Cybernet Syst 50(1):172–181

    Article  Google Scholar 

  12. Buterin V Ethereum. https://ethereum.org/en/

  13. Consortium, F.B.S.: Fisco Bcos. https://fisco-bcos-documentation.readthedocs.io/zh_CN/latest/

  14. IBM: Hyperledger Fabric. https://www.hyperledger.org/use/fabric

  15. Corda: Corda. https://www.corda.net/

  16. Takuya N, Zhang Q, Yohei U, Tatsushi I, Moriyoshi O (2020) Hyperledger fabric performance characterization and optimization using goleveldb benchmark. In: 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp 1–9

  17. Wang C, Wen X (2020) Performance characterization and bottleneck analysis of hyperledger fabric. In: 2020 IEEE 40th International Conference on Distributed Computing Systems, pp 1281–1286

  18. Khakkar P, Nathan S, Viswanathan B (2018) Performance benchmarking and optimizing hyperledger fabric blockchain platform. In: 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), pp 264–276

  19. Zhang L (2021) Hyperledger fabric endorsement strategy proposal distribution improvement plan. computer science and application, pp 1157–1164

  20. Meng W, Zhang D (2020) Optimization scheme for hyperledger fabric consensus mechanism. acta automatica sinica, pp 1–14

  21. Liu C, Li M, Wang Y, Wang Y, Huo D, Chen Y (2021) Achieve better endorsement balance on blockchain systems. In: 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp 581–586

  22. Kwon M, Yu H (2019) Performance improvement of ordering and endorsement phase in hyperledger fabric. In: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp 428–432

  23. Sukhwani H, Wang N, Trivedi K, Rindos A (2018) Performance modeling of hyperledger fabric (permissioned blockchain network). In: 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)

  24. Jiang L, Chang X, Liu Y, Misic J, Misic V (2020) Performance analysis of hyperledger fabric platform: a hierarchical model approach. Peer-to-Peer Netw Appl 2020:1014–1025

    Article  Google Scholar 

  25. Androulaki E, Manevich Y, Muralidharan S, Murthy C, Laventman G (2018) Hyperledger fabric: a distributed operating system for permissioned blockchains. In: The Thirteenth EuroSys Conference

  26. Demichev A, Kryukov A, Prikhod’ko N (2021) Business process engineering for data storing and processing in a collaborative distributed environment based on provenance metadata, smart contracts and blockchain technology. J Grid Comput 19:3

    Article  Google Scholar 

  27. Providers MS MSP. http://hyperledger-fabric.readthedocs.io/en/release-1.1/msp.html

  28. Omohundro Steve (2014) Cryptocurrencies, smart contracts, and artificial intelligence. Ai Matters 1(2):19–21

    Article  MathSciNet  Google Scholar 

  29. Wu M, Zhang Y, Yu J, Zhou Z (2023) A dynamic resource-aware endorsement strategy for improving throughput in blockchain systems. Expert Syst Appl 225:1–13

    Article  Google Scholar 

  30. Carlsson C, Fullér R (2002) Fuzzy sets and fuzzy logic. Physica-Verlag HD

  31. Zhai Y, Wu X (2017) Efficient bottleneck detection in stream process system using fuzzy logic model. In: 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)

  32. Cingolani P, Alcala-Fdez J (2012) jfuzzylogic: a robust and flexible fuzzy-logic inference system language implementation. In: IEEE International Conference on Fuzzy Systems

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Funding

This work was supported by the SongShan Laboratory Pre-research Project (YYJC032022022); Science and Technology Planning Project of Henan Province (No. 232102210059, No. 232102210045); National Key Research and Development Program of China (No. 2020YFB1712400).

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Author notes

  1. Lin Ge and Minghui Wu have contributed equally to this work.

Authors and Affiliations

  1. School of Intelligent Engineering, Zhengzhou University of Aeronautics, Wenyuan West Road, Zhengzhou, 450046, Henan, China

    Jianguo Yu & Lin Ge

  2. School of Information Engineering, China University of Geosciences, Xueyuan Road, Bei**g, 100083, China

    Minghui Wu

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  1. Jianguo Yu
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  2. Lin Ge
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  3. Minghui Wu
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Contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by JY and all authors commented on previous versions of the manuscript. Performance evaluation and data analysis were performed by LG and MW. All authors read and approved the final manuscript.

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Correspondence to Lin Ge.

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Yu, J., Ge, L. & Wu, M. Proposal Distribution optimization for Endorsement Strategy in Hyperledger Fabric. J Supercomput 80, 15038–15065 (2024). https://doi.org/10.1007/s11227-024-06056-2

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