Latency performance modeling and analysis for hyperledger fabric blockchain network. Issue 1 (January 2021) - Xu, Xiaoqiong; Sun, Gang; Luo, Long; Cao, Huilong; Yu, Hongfang; Vasilakos, Athanasios V.
Highlights: Based on the transaction flow of the Fabric blockchain, we decompose the transaction latency into three phases and each with several substeps for fine-grained analysis, and we indicate the configuration parameters that may affect the latency of each phase. We present an analytical model to calculate the average transaction latency in single and multiple channels Fabric blockchains. Besides, we give a quantitative latency analysis of two typical consensus mechanisms in Fabric blockchain, i.e. Solo and PBFT consensus. We conduct a series of experiments to evaluate the validity of our proposed analytical model, and the results show that our model predicts the experimental results with an accuracy error of less than 6.1 % . We discuss the general implications of our results for latency performance in a configurable Fabric blockchain system. Additionally, based on the analytical model, we identify performance bottlenecks and provide feasible insights. Abstract: Blockchain has been one of the most attractive technologies for many modern and even future applications. Fabric, an open-source framework to implement the permissioned enterprise-grade blockchain, is getting increasing attention from innovators. The latency performance is crucial to the Fabric blockchain in assessing its effectiveness. Many empirical studies were conducted to analyze this performance based on different hardware platforms. These experimental results are not comparable as they are highly dependent on the underlying networks. Moreover, theoretical analysis on the latency of Fabric blockchain still receives much less attention. This paper provides a novel theoretical model to calculate the transaction latency under various network configurations such as block size, block interval, etc. Subsequently, we validate the proposed latency model with experiments, and the results show that the difference between analytical and experimental results is as low as 6.1 % . We also identify some performance bottlenecks and give insights from the developer's perspective.