Towards Adaptive Blockchain for the Hyperledger Fabric Platform
Abstract
Blockchain technology provides secure and decentralized solutions for data management. However, there are challenges for these solutions to perform efficiently in scenarios with variations in the transaction input rate. In this sense, adaptive strategies have been proposed aiming at near-optimal performance in throughput and latency, considering these variations. In this paper, we analyze the state-of-the-art aHLF and FabMAN strategies for dynamically adjusting block size and timeout in the context of the Hyperledger Fabric permissioned blockchain. We conduct performance analysis via experiments on a blockchain network and compare these strategies. The results show that aHLF responds more efficiently to high input rate scenarios, while FabMAN performs better in low input rate scenarios. Therefore, we analyze how a hybrid approach that explores both strategies can result in an effective adaptive blockchain for different scenarios.
Keywords:
Blockchain, Adaptive Strategies, Hyperledger Fabric, Performance, Dynamic Block Adjustment
References
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Ongaro, D. and Ousterhout, J. (2014). In search of an understandable consensus algorithm. In 2014 USENIX Annual Technical Conference (USENIX ATC 14), pages 305–319.
Roy, U. and Ghosh, N. (2024). Fabman: A framework for ledger storage and size management for Hyperledger Fabric-based IoT applications. IEEE Transactions on Network and Service Management.
Saeed, S. H., Hadi, S., and Hamad, A. H. (2022). Performance evaluation of e-voting based on Hyperledger Fabric blockchain platform. Revue d’Intelligence Artificielle.
Shalaby, S., Abdellatif, A., Al-Ali, A., Mohamed, A. M., Erbad, A., and Guizani, M. (2020). Performance evaluation of Hyperledger Fabric. 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), pages 608–613.
Silva, F., Gonçalves, G., Fé, I., Feitosa, L., and Soares, A. (2023). Avaliação de desempenho de blockchains permissionadas Hyperledger orientada ao planejamento de capacidade de recursos computacionais. In Anais do XLI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 71–84, Porto Alegre, RS, Brasil. SBC.
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Baliga, A., Subhod, I., Kamat, P., and Chatterjee, S. (2018). Performance evaluation of the quorum blockchain platform. arXiv preprint arXiv:1809.03421.
Castro, M., Liskov, B., et al. (1999). Practical Byzantine fault tolerance. In OsDI, volume 99, pages 173–186.
Chacko, J. A., Mayer, R., and Jacobsen, H.-A. (2021). Why do my blockchain transactions fail? A study of Hyperledger Fabric. In Proceedings of the 2021 International Conference on Management of Data, pages 221–234.
de Sá, A. S., Silva Freitas, A. E., and de Araújo Macêdo, R. J. (2013). Adaptive request batching for Byzantine replication. ACM SIGOPS Operating Systems Review, 47(1), 35–42.
Greve, F., Sampaio, L., Abijaude, J., Coutinho, A. A., Brito, I., and Queiroz, S. (2018). Blockchain e a revolução ao do consenso sob demanda. In Proceedings of SBRC Minicursos.
Liu, C.-M., Badigineni, M., and Lu, S. W. (2021). Adaptive blocksize for IoT payload data on Fabric blockchain. In 2021 30th Wireless and Optical Communications Conference (WOCC), pages 92–96. IEEE.
Mendonça, R., Moura, E., Gonçalves, G., Vieira, A., and Nacif, J. (2023). Comparação e análise de custo e desempenho entre nós de redes blockchain permissionadas e públicas. In Anais do XLI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 141–154, Porto Alegre, RS, Brasil. SBC.
Moura, E., Melo, C., Gonçalves, G., Silva, F., and Soares, A. (2024). Uma ferramenta de avaliação de desempenho para plataforma blockchain Hyperledger Fabric: Hlf-pet. In Anais do II Colóquio em Blockchain e Web Descentralizada, pages 8–13, Porto Alegre, RS, Brasil. SBC.
Ongaro, D. and Ousterhout, J. (2014). In search of an understandable consensus algorithm. In 2014 USENIX Annual Technical Conference (USENIX ATC 14), pages 305–319.
Roy, U. and Ghosh, N. (2024). Fabman: A framework for ledger storage and size management for Hyperledger Fabric-based IoT applications. IEEE Transactions on Network and Service Management.
Saeed, S. H., Hadi, S., and Hamad, A. H. (2022). Performance evaluation of e-voting based on Hyperledger Fabric blockchain platform. Revue d’Intelligence Artificielle.
Shalaby, S., Abdellatif, A., Al-Ali, A., Mohamed, A. M., Erbad, A., and Guizani, M. (2020). Performance evaluation of Hyperledger Fabric. 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), pages 608–613.
Silva, F., Gonçalves, G., Fé, I., Feitosa, L., and Soares, A. (2023). Avaliação de desempenho de blockchains permissionadas Hyperledger orientada ao planejamento de capacidade de recursos computacionais. In Anais do XLI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 71–84, Porto Alegre, RS, Brasil. SBC.
Wai, K. and Thein, N. (2023). Performance analysis on block size valuation of Hyperledger Fabric blockchain. 2023 IEEE Conference on Computer Applications (ICCA), pages 50–55.
Xu, X., Weber, I., and Staples, M. (2019). Architecture for blockchain applications. Springer.
Published
2025-05-19
How to Cite
DE ARAÚJO MOURA, Ericksulino Manoel; SANTIAGO GAMA, Felipe; DIAS GONÇALVES, Glauber; SILVA FREITAS, Allan Edgard; SOARES, André.
Towards Adaptive Blockchain for the Hyperledger Fabric Platform. In: BLOCKCHAIN WORKSHOP: THEORY, TECHNOLOGY AND APPLICATIONS (WBLOCKCHAIN), 8. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 15-28.
DOI: https://doi.org/10.5753/wblockchain.2025.8740.
