Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations

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{jung2025_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 }
}