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

2026-05-04

AI Summary

This work addresses the challenge of performance tuning in Hyperledger Fabric, which is hindered by a high-dimensional (317-dimensional), tightly coupled configuration space and significant measurement noise that render manual optimization impractical. To tackle this, the authors propose an end-to-end automated tuning framework that integrates Bayesian optimization with dimensionality reduction techniques—specifically PCA and REMBO—within a Caliper-in-the-loop setup. The framework further incorporates efficient high-dimensional exploration algorithms such as DYCORS to navigate the complex parameter landscape. Experimental results demonstrate that the DYCORS-PCA variant achieves a 12% throughput improvement over the initial configuration, while MPI-REMBO yields a 9% gain, thereby validating the effectiveness and practicality of the proposed approach for automatically optimizing Fabric performance under high-dimensional and noisy conditions.

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.

Problem

• Hyperledger Fabric
• performance tuning
• black-box optimization
• throughput optimization
• configuration parameters

Innovation

• Bayesian Optimization
• Dimensionality Reduction
• Hyperledger Fabric
• Black-Box Optimization
• Performance Tuning