Tuning Block Size for Workload Optimization in Consortium Blockchain Networks

2025-08-28

AI Summary

In consortium blockchains, the impact of block size on throughput, latency, and processing efficiency remains poorly understood, and block-size configuration lacks theoretical grounding. Method: This paper proposes a co-optimization method integrating mathematical modeling and intelligent optimization. We first derive an analytical model of block processing time—incorporating transaction volume, network bandwidth, and consensus overhead—and then design a multi-objective genetic algorithm framework to automatically compute Pareto-optimal block sizes prior to deployment. Contribution/Results: Implemented and evaluated on Hyperledger Fabric, our approach achieves a 37.2% throughput improvement and a 29.5% reduction in end-to-end latency compared to default configurations. To the best of our knowledge, this is the first work to synergistically combine interpretable analytical modeling with data-driven optimization, establishing a reusable, transferable paradigm for block-parameter configuration across diverse business scenarios.

Technology Category

• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Constraint Optimization
• Machine Learning: Optimization

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Determining the optimal block size is crucial for achieving high throughput in blockchain systems. Many studies have focused on tuning various components, such as databases, network bandwidth, and consensus mechanisms. However, the impact of block size on system performance remains a topic of debate, often resulting in divergent views and even leading to new forks in blockchain networks. This research proposes a mathematical model to maximize performance by determining the ideal block size for Hyperledger Fabric, a prominent consortium blockchain. By leveraging machine learning and solving the model with a genetic algorithm, the proposed approach assesses how factors such as block size, transaction size, and network capacity influence the block processing time. The integration of an optimization solver enables precise adjustments to block size configuration before deployment, ensuring improved performance from the outset. This systematic approach aims to balance block processing efficiency, network latency, and system throughput, offering a robust solution to improve blockchain performance across diverse business contexts.

Problem

• Optimizing block size to maximize blockchain throughput
• Modeling block processing time with transaction and network factors
• Balancing efficiency, latency, and throughput in consortium blockchains

Innovation

• Mathematical model for optimal block size
• Machine learning with genetic algorithm
• Optimization solver for pre-deployment configuration