A Learning to Prediction Based Transaction Traffic Management Approach to Enhance Healthcare Blockchain Performance

Abstract

The transaction processing capacity of blockchain systems remains a critical barrier to adoption in real-time applications. Recent studies have explored different optimization techniques, including sharding, off-chain processing, and hybrid consensus algorithms. However, most of those techniques change the original architecture or process of the blockchain and may raise compatibility issues. Resolving these challenges calls for creative methods that can effectively balance transaction throughput with latency without compromising blockchains' core infrastructures. This paper proposes a learning to prediction framework combining a Kalman filter and artificial neural network for transaction throughput forecasting, integrated with a fuzzy logic controller embedded in smart contracts. The approach can dynamically optimize transaction traffic flow based on the predicted throughput and the observed transaction latency, thus improving blockchain performance in real-time. Deployed on a hyperledger fabric healthcare testbed and evaluated through a series of ablation experiments, the results demonstrate a significant improvement over the baseline and therefore illustrate the potential of the proposed approach in improving blockchain performance for practical applications.