用机器学习预测联盟链性能，精准指导资源扩容配置。
Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations
- 基于机器学习模型，根据扩容配置预测网络可靠性和吞吐量。
- 预测误差仅约1.9%，具备实际部署价值。
- 适合云服务商和系统管理员优化BaaS性能配置。
区块链正越来越多地由云服务提供商以区块链即服务（BaaS）的形式提供。然而，为实现最佳性能与可靠性而配置BaaS仍依赖试错。主要挑战在于BaaS常被视为“黑箱”，导致性能与资源规划存在不确定性。以往研究尝试解决此问题，但垂直扩展和水平扩展的影响仍不明确。为此，我们提出了基于机器学习的模型，根据扩容配置预测网络可靠性和吞吐量。在评估中，模型预测误差约为1.9%，精度极高，可直接应用于实际场景。
原文摘要 · Abstract (English)
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.
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