Caliper-in-the-Loop: Black-Box Optimization for Hyperledger Fabric Performance Tuning
📰 ArXiv cs.AI
Optimize Hyperledger Fabric performance using Caliper-in-the-Loop, a black-box optimization approach with Bayesian optimization and dimensionality reduction
Action Steps
- Deploy Hyperledger Fabric with initial configuration parameters
- Use Caliper to benchmark the performance of the deployed configuration
- Apply Bayesian optimization with dimensionality reduction to identify optimal configuration parameters
- Update the configuration parameters and re-deploy the network
- Repeat the benchmarking and optimization process until optimal performance is achieved
Who Needs to Know This
DevOps and blockchain development teams can benefit from this approach to automate performance tuning of Hyperledger Fabric, reducing manual effort and improving efficiency
Key Insight
💡 Automated performance tuning of Hyperledger Fabric using Caliper-in-the-Loop can significantly improve throughput and reduce manual effort
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🚀 Optimize #HyperledgerFabric performance with Caliper-in-the-Loop, a black-box optimization approach using Bayesian optimization and dimensionality reduction 📈
Key Takeaways
Optimize Hyperledger Fabric performance using Caliper-in-the-Loop, a black-box optimization approach with Bayesian optimization and dimensionality reduction
Full Article
Title: Caliper-in-the-Loop: Black-Box Optimization for Hyperledger Fabric Performance Tuning
Abstract:
arXiv:2605.02690v1 Announce Type: cross 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 t
Abstract:
arXiv:2605.02690v1 Announce Type: cross 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 t
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