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arXiv · 2503.15769

Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations

Seungwoo Jung·Yeonho Yoo·Gyeongsik Yang·Chuck Yoo
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Abstract

Blockchain is increasingly offered as blockchain-as-a-service (BaaS) by cloud service providers. However, configuring BaaS appropriately for optimal performance and reliability resorts to try-and-error. A key challenge is that BaaS is often perceived as a ``black-box,'' leading to uncertainties in performance and resource provisioning. Previous studies attempted to address this challenge; however, the impacts of both vertical and horizontal scaling remain elusive. To this end, we present machine learning-based models to predict network reliability and throughput based on scaling configurations. In our evaluation, the models exhibit prediction errors of ~1.9%, which is highly accurate and can be applied in the real-world.

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Seungwoo Jung, Yeonho Yoo, Gyeongsik Yang, Chuck Yoo. 2025-03-20. Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations. https://doi.org/10.1016/j.icte.2024.09.003

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