A Model-Based Machine Learning Approach for Assessing the Performance of
  Blockchain Applications
Adel Albshri Ali Alzubaidi Ellis Solaiman
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.