Caliper-in-the-Loop: Black-Box Optimization for Hyperledger Fabric Performance Tuning

TL;DR

This paper demonstrates that Bayesian optimization with dimensionality reduction effectively automates Hyperledger Fabric performance tuning, achieving significant throughput improvements in high-dimensional configuration spaces.

Contribution

It introduces an end-to-end pipeline using Bayesian optimization with dimensionality reduction for automated, high-dimensional Hyperledger Fabric tuning.

Findings

DYCORS-PCA achieves 12% throughput improvement over initial configurations.

MPI-REMBO achieves 9% throughput improvement.

BO with DR is practical for high-dimensional blockchain system tuning.

Abstract

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.