Prediction of permissioned blockchain performance for resource scaling configurations
Seungwoo Jung, Yeonho Yoo, Gyeongsik Yang, Chuck Yoo
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.