A Model-Based Machine Learning Approach for Assessing the Performance of Blockchain Applications

TL;DR

This paper introduces machine learning models, including kNN, SVM, and an enhanced salp swarm optimization, to predict and optimize blockchain application performance, providing a reliable and accurate evaluation method.

Contribution

It presents a novel ML-based framework using kNN, SVM, and an improved salp swarm optimization for performance prediction and configuration optimization of blockchain applications.

Findings

kNN outperforms SVM by 5% in accuracy

ISO reduces inaccuracy deviation by 4% compared to standard SO

Models demonstrate competitive performance in blockchain evaluation

Abstract

The recent advancement of Blockchain technology consolidates its status as a viable alternative for various domains. However, evaluating the performance of blockchain applications can be challenging due to the underlying infrastructure's complexity and distributed nature. Therefore, a reliable modelling approach is needed to boost Blockchain-based applications' development and evaluation. While simulation-based solutions have been researched, machine learning (ML) model-based techniques are rarely discussed in conjunction with evaluating blockchain application performance. Our novel research makes use of two ML model-based methods. Firstly, we train a $k$ nearest neighbour ($k$NN) and support vector machine (SVM) to predict blockchain performance using predetermined configuration parameters. Secondly, we employ the salp swarm optimization (SO) ML model which enables the investigation of optimal blockchain configurations for achieving the required performance level. We use rough set theory to enhance SO, hereafter called ISO, which we demonstrate to prove achieving an accurate recommendation of optimal parameter configurations; despite uncertainty. Finally, statistical comparisons indicate that our models have a competitive edge. The $k$NN model outperforms SVM by 5\% and the ISO also demonstrates a reduction of 4\% inaccuracy deviation compared to regular SO.