用自动优化提升区块链性能，12%吞吐量提升。
Caliper-in-the-Loop: Black-Box Optimization for Hyperledger Fabric Performance Tuning
- 将性能调优视为黑箱优化问题，结合贝叶斯优化与降维方法。
- 在317维配置空间中，最高实现12%的TPS提升。
- 适合需要自动化调优区块链系统的研发人员参考。
Hyperledger Fabric 的性能受多个相互作用的配置参数影响，手动调优困难。本文将基准测试视为噪声黑箱优化问题，采用贝叶斯优化（BO）结合降维（DR）进行自动化吞吐量调优。构建了端到端的 Caliper-in-the-loop 管道，部署候选配置、执行基准测试，并基于观测吞吐量更新优化器。搜索空间源自 Fabric 配置文件，共317维。在云测试环境中，评估了16种 BO+DR 变体及随机搜索基线。最佳方法 DYCORS-PCA 相较于首个评估配置实现12%的TPS提升，MPI-REMBO 达到9%。结果表明，结合降维的贝叶斯优化是高维 Hyperledger Fabric 调优的有效方法，同时揭示了测量噪声对性能增益解读的影响。
原文摘要 · Abstract (English)
Hyperledger Fabric performance depends on many interacting configuration parameters, making manual tuning difficult. We study automated throughput tuning by treating benchmarking as a noisy black-box optimization problem and applying Bayesian optimization (BO) with dimensionality reduction (DR). We implement an end-to-end Caliper-in-the-loop pipeline that deploys candidate configurations, benchmarks them, and updates the optimizer from observed throughput. The search space, derived from Fabric configuration files, has 317 dimensions. In a cloud testbed, we evaluate 16 BO+DR variants and a random-search baseline. The best method, DYCORS-PCA, achieves a 12% TPS improvement relative to the first evaluated configuration, while MPI-REMBO achieves 9%. These results suggest that BO with DR is a practical approach for high-dimensional Hyperledger Fabric tuning, while also highlighting the role of measurement noise in interpreting gains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。