Tuning Block Size for Workload Optimization in Consortium Blockchain Networks

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Auteurs principaux:	Dadkhah, Narges, Mohammadi, Somayeh, Wunder, Gerhard
Format:	Preprint
Publié:	2025
Sujets:	Cryptography and Security
Accès en ligne:	Acceder al recurso
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author	Dadkhah, Narges Mohammadi, Somayeh Wunder, Gerhard
author_facet	Dadkhah, Narges Mohammadi, Somayeh Wunder, Gerhard
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2509_03367
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Tuning Block Size for Workload Optimization in Consortium Blockchain Networks Dadkhah, Narges Mohammadi, Somayeh Wunder, Gerhard Cryptography and Security 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.
title	Tuning Block Size for Workload Optimization in Consortium Blockchain Networks
topic	Cryptography and Security
url	https://arxiv.org/abs/2509.03367

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