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.
@article{arxiv.2503.15769,
title = {Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations},
author = {Seungwoo Jung and Yeonho Yoo and Gyeongsik Yang and Chuck Yoo},
journal= {arXiv preprint arXiv:2503.15769},
year = {2025}
}