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.

翻译：区块链正日益被云服务提供商以区块链即服务（BaaS）的形式提供。然而，为获得最佳性能和可靠性而对BaaS进行适当配置，目前仍依赖于试错法。一个关键挑战在于BaaS常被视为一个“黑盒”，导致其性能与资源供给存在不确定性。先前的研究尝试应对这一挑战；然而，垂直伸缩与水平伸缩的影响仍未明确。为此，我们提出了基于机器学习的模型，用于根据伸缩配置预测网络可靠性与吞吐量。在我们的评估中，这些模型的预测误差约为1.9%，具有很高的准确性，可在实际场景中应用。