# Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations

arXiv: https://arxiv.org/abs/2503.15769 · HTML: https://mlsanity.com/paper/2503.15769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Blocks: Collaborative and Persistent Augmented Reality Experiences](https://mlsanity.com/paper/1908.02409/) (2019)
- [Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform](https://mlsanity.com/paper/1805.11390/) (2018)

## Abstract (arXiv)

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.