Performance Modeling and Evaluation of Hyperledger Fabric: An Analysis Based on Transaction Flow and Endorsement Policies

TL;DR

This paper evaluates Hyperledger Fabric's performance, analyzing how factors like block size, arrival rate, and gateways affect throughput, latency, and resource utilization through modeling and experiments.

Contribution

It introduces a comprehensive framework for performance evaluation of Hyperledger Fabric based on transaction flow and endorsement policies, with detailed experimental analysis.

Findings

Block size and arrival rate significantly impact throughput and latency.

Multiple gateways can reduce latency but may decrease throughput.

Performance trade-offs depend on configuration parameters.

Abstract

Blockchain is a paradigm derived from distributed systems, protocols, and security concepts. However, can blockchain applications provide services in industrial environments, especially concerning performance issues? In blockchains, long response times can impair both user and service experience, and intensive resource use may increase the costs of service provision. The proposed paper tries to answer this question by evaluating the performance of one of the most popular permissioned blockchain platforms, the Hyperledger Fabric (HLF). We provide a framework for performance evaluation based on modeling and experimentation. The results indicate that block size and arrival rate can compromise throughput (by -70%), latency (by +1,500%), and environment utilization (by +28%) and that multiple gateways can reduce latency (by -75%), and throughput (by -60%)