Performance Modeling of Hyperledger Fabric 2.0: A Queuing Theory-Based Approach

Abstract

Hyperledger Fabric (shortened to Fabric) is an open-source, enterprise-level, permissioned distributed ledger technology platform with a highly modular, configurable architecture. It supports writing smart contracts in general-purpose programing languages and has become the preferred choice for enterprise-level blockchain applications. However, the transaction throughput of the Fabric system remains a critical factor that restricts the further application of this technology in various fields. Therefore, it is necessary to evaluate and optimize the performance of the Fabric blockchain platform. Existing performance modeling methods need to be improved in terms of compatibility and effectiveness. To address this, we propose a performance-compatible modeling method for Fabric using queuing theory, which considers the limited transaction pool and the situation where node groups are attacked. Using the Fabric 2.0 version as an example, we have established a model of the transaction process in the Fabric network. By analyzing the model’s continuous 3D time Markov process, we solved the system stationary equation and obtained analytical expressions for performance indicators such as system throughput, system steady-state queue length, and system average response time. We conducted extensive analyses and simulations to verify the models’ and formulations’ accuracy and validity. We believe this approach can be extended to various scenarios in other blockchain systems.