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.
@article{arxiv.2605.02690,
title = {Caliper-in-the-Loop: Black-Box Optimization for Hyperledger Fabric Performance Tuning},
author = {Yash Madhwal and Arseny Bolotnikov and Mark Prikhno and Irina Lebedeva and Ivan Laishevskiy and Vladimir Gorgadze and Artem Barger and Yury Yanovich},
journal= {arXiv preprint arXiv:2605.02690},
year = {2026}
}