Performance Optimization in Multi-Machine Blockchain Systems: A Comprehensive Benchmarking Analysis

Abstract

The increase in decentralized applications has put blockchain technology in a very crucial position in the industries and various sectors such as finance, healthcare, and logistics in the United States. The need to optimize performance in blockchain systems and especially those operating on more than one machine grows along with the demand for secure and distributed ledgers.  The main objective of the study was to develop a predictive dynamic benchmarking framework that allows optimizing the performance of the multi-machine blockchain systems. Through machine learning algorithms, which include random forests, gradient boosting, and support vector machines. The dataset used in this study is made up of high-resolution performance logs of a simulated multi-machine blockchain setting after having run 30 days of logs across both a permissioned (Hyperledger Fabric) and an accessible (Ethereum) environment. The main measurements observed are the block propagation times which is how long it takes a new block to be broadcasted to every single node of the network, along with the transaction confirmation latencies which will be the time difference between the moment a transaction is submitted and that same transaction is finally confirmed on the distributed ledger. The selection of three supervised machine learning models to be deployed was based on the capacity to work with high dimensionality, nonlinear as well as possible imbalanced performance classification problems. Several evaluation measures were calculated to have an overall picture of the classification ability of each model. Accuracy measures the level of overall accuracy of predictions, whereas precision, recall, and F1-score offer information about performance on minor classes needed to locate rare but serious blockchain performance reductions.  The Confusion matrix analysis was employed in identifying the particular types of misclassifications. The Random Forest model outperformed the other two models, attaining the highest accuracy, with near-identical precision, recall, and F1 score values, indicating consistent and reliable predictions across all classes. Gradient Boosting performs closely and achieves a strong balance across other metrics, suggesting it is nearly as effective and particularly useful for more nuanced prediction tasks or imbalanced datasets. The application of optimized multi-machine blockchain performance is particularly applicable in many of the large sectors in the United States, in which fast, secure, and scalable digital infrastructure is in demand. Infrastructure-wise, it is possible to use the results of machine learning-powered benchmarking to dictate more optimal hardware and software settings of blockchain nodes set up in the U.S.-based data centers and cloud environments. From a public policy perspective, the research implications for performance optimization are largely compatible with a series of current federal efforts aimed at enhancing the digital trust infrastructure. In the future, it is possible to research the integration of Machine Learning-based benchmarking systems into real-time optimization engines, whereby we would be able to tune the behavior of nodes on the fly, given the latest telemetry of performance.