Transactional Dynamics in Hyperledger Fabric: A Stochastic Modeling and Performance Evaluation of Permissioned Blockchains

TL;DR

The paper addresses performance optimization for Hyperledger Fabric by introducing a Stochastic Petri Net (SPN) model of the endorsement–ordering–committing pipeline. It validates the SPN against a Fabric testbed, demonstrates accurate replication of mean response times and throughput, and conducts a comprehensive sensitivity analysis to identify critical parameters such as block size and arrival rate. Key findings show that block size can alter throughput and mean response time by up to approximately 200%, and that arrival rate and resource allocation are pivotal for system efficiency. The work provides practical guidance for configuring HLF deployments to balance latency, throughput, and resource use, with formal bottleneck detection to support scalable, energy-efficient operations.

Abstract

Blockchain, often integrated with distributed systems and security enhancements, has significant potential in various industries. However, environmental concerns and the efficiency of consortia-controlled permissioned networks remain critical issues. We use a Stochastic Petri Net model to analyze transaction flows in Hyperledger Fabric networks, achieving a 95% confidence interval for response times. This model enables administrators to assess the impact of system changes on resource utilization. Sensitivity analysis reveals major factors influencing response times and throughput. Our case studies demonstrate that block size can alter throughput and response times by up to 200%, underscoring the need for performance optimization with resource efficiency.

Figures (4)

• Figure 1: Hyperledger Fabric: Architecture and Stochastic Model
• Figure 2: Sensitivity Analyses of MRT wrt. Impacting Factors
• Figure 3: Sensitivity Analyses of Throughput wrt. Impacting Factors
• Figure 4: Utilization Analysis