A Reinforcement-Learning-Based Model for Resilient Load Balancing in Hyperledger Fabric

Abstract

Blockchain with its numerous advantages is often considered a foundational technology with the potential to revolutionize a wide range of application domains, including enterprise applications. These enterprise applications must meet several important criteria, including scalability, performance, and privacy. Enterprise blockchain applications are frequently constructed on private blockchain platforms to satisfy these criteria. Hyperledger Fabric is one of the most popular platforms within this domain. In any privacy blockchain system, including Fabric, every organisation needs to utilise a peer node (or peer nodes) to connect to the blockchain platform. Due to the ever-increasing size of blockchain and the need to support a large user base, the monitoring and the management of different resources of such peer nodes can be crucial for a successful deployment of such blockchain platforms. Unfortunately, little attention has been paid to this issue. In this work, we propose the first-ever solution to this significant problem by proposing an intelligent control system based on reinforcement learning for distributing the resources of Hyperledger Fabric. We present the architecture, discuss the protocol flows, outline the data collection methods, analyse the results and consider the potential applications of the proposed approach.