Caliper-in-the-Loop: Black-Box Optimization for Hyperledger Fabric Performance Tuning
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915978208608256 |
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| author | Madhwal, Yash Bolotnikov, Arseny Prikhno, Mark Lebedeva, Irina Laishevskiy, Ivan Gorgadze, Vladimir Barger, Artem Yanovich, Yury |
| author_facet | Madhwal, Yash Bolotnikov, Arseny Prikhno, Mark Lebedeva, Irina Laishevskiy, Ivan Gorgadze, Vladimir Barger, Artem Yanovich, Yury |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02690 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Caliper-in-the-Loop: Black-Box Optimization for Hyperledger Fabric Performance Tuning Madhwal, Yash Bolotnikov, Arseny Prikhno, Mark Lebedeva, Irina Laishevskiy, Ivan Gorgadze, Vladimir Barger, Artem Yanovich, Yury Distributed, Parallel, and Cluster Computing Artificial Intelligence 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. |
| title | Caliper-in-the-Loop: Black-Box Optimization for Hyperledger Fabric Performance Tuning |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2605.02690 |