Recurrent Neural Network (RNN)–Based Approach to Uncover the Relationship Between Block Size and Blockchain Performance

Abstract

Blockchain is a distributed system where transactions are recorded on blocks. The blocks are linked with previous blocks creating a chain of blocks that ensures the integrity of the blockchain. Throughput operates as a pivotal performance indicator in blockchain contributing to its popularity among organizations and individual users. To achieve optimized use of blockchain for an increasing number of users, accurate prediction of performance at different timestamps is necessary. In this paper, two recurrent neural network (RNN)–based predictive models, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) are proposed that predict the performance (throughput) of blockchain in different scenarios. Moreover, the study analyzes blockchain data to find optimal block size, impact of block size on performance, and uncover the relationship between performance and block size. The RNN models are evaluated using a dataset collected from Hyperledger Caliper on the HF framework 2.3. This study found that the performance of LSTM is approximately close to GRU regarding evaluation metrics MAE and RMSE. However, GRU is more suitable for practical applications and performs better overall. This study also shows a Spearman correlation value of 0.032, which is indicative of a weak association between the two factors.