Improving Hyperledger Fabric Transaction Latency with Adaptive Dynamic Optimization

doi:10.3778/j.issn.1002-8331.2303-0457

Abstract

Abstract: Hyperledger Fabric (HLF) performance optimization is a hot topic in blockchain research, but insufficient attention has been paid to transaction delay in low-load production environments, which has become an important factor affecting the application of alliance chains. To address this problem, a dynamic adaptive optimization method for HLF-oriented transaction delay has been proposed. Firstly, a comprehensive performance evaluation of HLF (v2.2) is conducted, taking into account the dimensions of transaction arrival rate and transaction size, and a set of statically recommended configuration parameters is obtained. Secondly, HLF performance model is established using XGBoost based on performance evaluation data, obtaining the mapping relationship between application load, network configuration, and transaction delay. Next, unsaturated dynamic load and burst injection attack scenarios are simulated to obtain application load input close to the production environment. Finally, Bayesian optimization algorithm is used to optimize the dynamic adaptive configuration parameters of HLF, effectively reducing the processing delay of HLF transactions. The effectiveness of the optimization method has been verified through experiments, and an average transaction delay optimization effect of 53% and a maximum of 97% have been achieved in multiple groups of random dynamic load inputs.

Key words: blockchain, performance optimization, Hyperledger Fabric, transaction delay, Bayesian optimization

摘要： Hyperledger Fabric（HLF）性能优化是区块链研究的热点问题，但当前工作对低负载生产环境中的交易时延关注不足，成为影响联盟链落地应用的重要因素。针对这一问题，提出了面向HLF的交易时延动态自适应优化方法：综合考虑交易到达率与交易大小维度，对HLF（v2.2）进行全面的性能评估，并给出了一组静态推荐配置参数；基于性能评估数据，使用XGBoost建立HLF性能模型，获得应用负载、网络配置、交易时延间的映射关系；对非饱和动态负载与突发注入攻击等场景进行模拟，获得贴近生产环境的应用负载输入；基于贝叶斯优化算法，对HLF进行动态自适应配置参数优化，有效降低HLF交易处理时延。通过实验，验证了优化方法的有效性，在多组随机动态负载输入中取得了平均53%、最高97%的交易时延优化效果。

关键词: 区块链, 性能优化, Hyperledger Fabric, 交易时延, 贝叶斯优化

LI Tianxiang, HAN Yunfei, Abdureyim Abai, MA Yupeng, WANG Yi. Improving Hyperledger Fabric Transaction Latency with Adaptive Dynamic Optimization[J]. Computer Engineering and Applications, 2024, 60(14): 257-266.

李天祥, 韩云飞, 阿不都热衣木江·阿白, 马玉鹏, 王轶. HLF区块链交易时延动态优化方法研究[J]. 计算机工程与应用, 2024, 60(14): 257-266.

References

[1] 蔡晓晴, 邓尧, 张亮, 等. 区块链原理及其核心技术[J]. 计算机学报, 2021, 44(1): 84-131.
CAI X Q, DENG Y, ZHANG L, et al. The principle and core technology of blockchain[J]. Journal of Computer Science, 2021, 44(1): 84-131.
[2] 袁勇, 王飞跃. 区块链技术发展现状与展望[J]. 自动化学报, 2016, 42 (4): 481-494.
YUAN Y, WANG F Y. Blockchain: the state of the art and future trends[J]. Journal of Automation, 2016, 42 (4): 481-494.
[3] NAKAMOTO S. Bitcoin: a peer-to-peer electronic cash system[J]. Social Science Electronic Publishing, 2009: 213739669.
[4] DINH T T A, WANG J, CHEN G, et al. BLOCKBENCH: a framework for analyzing private blockchains[C]//Proceedings of the 2017 ACM International Conference on Management of Data, 2017: 1085-1100.
[5] 潘晨, 刘志强, 刘振, 等. 区块链可扩展性研究: 问题与方法[J]. 计算机研究与发展, 2018, 55(10): 2099-2110.
PAN C, LIU Z Q, LIU Z, et al. Research on scalability of blockchain technology: problems and methods[J]. Computer Research and Development, 2018, 55(10): 2099-2110.
[6] WANG S, DINH T T A, LIN Q, et al. Forkbase: an efficient storage engine for blockchain and forkable applications[J]. arXiv:1802.04949, 2018.
[7] BENTOV I, LEE C, MIZRAHI A, et al. Proof of activity: extending bitcoin’s proof of work via proof of stake [extended abstract] y[J]. ACM SIGMETRICS Performance Evaluation Review, 2014, 42(3): 34-37.
[8] KIAYIAS A, RUSSELL A, DAVID B, et al. Ouroboros: a provably secure proof-of-stake blockchain protocol[C]//Proceedings of the 37th Annual International Cryptology Conference, 2017: 357-388.
[9] PANG S, QI X, ZHANG Z, et al. Concurrency protocol aiming at high performance of execution and replay for smart contracts[J]. arXiv:1905.07169, 2019.
[10] XU X, SUN G, LUO L, et al. Latency performance modeling and analysis for hyperledger fabric blockchain network[J]. Information Processing & Management, 2021, 58(1): 102436.
[11] NGUYEN T S L, JOURJON G, POTOP-BUTUCARU M, et al. Impact of network delays on hyperledger fabric[C]//Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops, 2019: 222-227.
[12] THAKKAR P, NATHAN S, VISWANATHAN B. Performance benchmarking and optimizing hyperledger fabric blockchain platform[C]//Proceedings of the 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 2018: 264-276.
[13] CHACKO J A, MAYER R, JACOBSEN H A. Why do my blockchain transactions fail? a study of hyperledger fabric [J]. arXiv:2103.04681, 2021.
[14] THAKKAR P, NATARAJAN S. Scaling hyperledger fabric using pipelined execution and sparse peers[J]. arXiv:2003. 05113, 2020.
[15] GORENFLO C, LEE S, GOLAB L, et al. FastFabric: scaling hyperledger fabric to 20?000 transactions per second[C]//Proceedings of the 2019 IEEE International Conference on Blockchain and Cryptocurrency, 2019: 455-463.
[16] SHARMA A, SCHUHKNECHT F M, AGRAWAL D, et al. Blurring the lines between blockchains and database systems: the case of hyperledger fabric[C]//Proceedings of the 2019 International Conference on Management of Data, 2019: 105-122.
[17] GORENFLO C, GOLAB L, KESHAV S. XOX fabric: a hybrid approach to blockchain transaction execution[C]//Proceedings of the 2020 IEEE International Conference on Blockchain and Cryptocurrency, 2020: 1-9.
[18] RUAN P, LOGHIN D, TA Q T, et al. A transactional perspective on execute-order-validate blockchains[J]. arXiv:2003.10064, 2020.
[19] YUAN P, ZHENG K, XIONG X, et al. Performance modeling and analysis of a hyperledger-based system using GSPN[J]. Computer Communications, 2020, 153: 117-124.
[20] JIANG L, CHANG X, LIU Y, et al. Performance analysis of hyperledger fabric platform: a hierarchical model approach[J]. Peer-to-Peer Networking and Applications, 2020, 13(3): 1014-1025.
[21] SUKHWANI H, WANG N, TRIVEDI K S, et al. Performance modeling of hyperledger fabric (permissioned blockchain network)[C]//Proceedings of the 2018 IEEE 17th International Symposium on Network Computing and Applications, 2018: 1-8.
[22] HANG L, KIM D H. Optimal blockchain network construction methodology based on analysis of configurable components for enhancing hyperledger fabric performance[J]. Blockchain: Research and Applications, 2021, 2(1): 100009.
[23] CHUNG G, DESROSIERS L, GUPTA M, et al. Performance tuning and scaling enterprise blockchain applications[J]. arXiv:1912.11456, 2019.
[24] Hyperledger Fabric. What’s new in Hyperledger Fabric v2.x[EB/OL]. [2023-02-20].https://Hyperledger-fabric.readthedocs.io/en/latest/whatsnew.html.
[25] GRINSZTAJN L, OYALLON E, VAROQUAUX G. Why do tree-based models still outperform deep learning on tabular data?[J]. arXiv:2207.08815, 2022.
[26] 王青青. 到达率随时间变化带有顾客流失的队列模型[D]. 西安: 长安大学, 2020.
WANG Q Q. Queue models with time-varying arrival rates and customers loss[D]. Xi’an: Chang’an University, 2020.
[27] HUANG H, YUE Z, PENG X, et al. Elastic resource allocation against imbalanced transaction assignments in sharding-based permissioned blockchains[J]. IEEE Transactions on Parallel and Distributed Systems, 2022, 33(10): 2372-2385.