An In-Depth Investigation of the Performance Characteristics of Hyperledger Fabric
This provides guidelines for researchers and practitioners to better configure and implement Hyperledger Fabric networks, addressing scalability and performance prediction challenges in enterprise applications like supply chain management, but it is incremental as it builds on existing tools.
The paper tackles the issue of unpredictable real-world performance in private permissioned blockchains like Hyperledger Fabric by conducting an in-depth performance analysis, resulting in a detailed compilation of performance characteristics using an enhanced Distributed Ledger Performance Scan (DLPS) framework.
Private permissioned blockchains are deployed in ever greater numbers to facilitate cross-organizational processes in various industries, particularly in supply chain management. One popular example of this trend is Hyperledger Fabric. Compared to public permissionless blockchains, it promises improved performance and provides certain features that address key requirements of enterprises. However, also permissioned blockchains are still not as scalable as centralized systems, and due to the scarcity of theoretical results and empirical data, their real-world performance cannot be predicted with the necessary precision. We intend to address this issue by conducting an in-depth performance analysis of Hyperledger Fabric. The paper presents a detailed compilation of various performance characteristics using an enhanced version of the Distributed Ledger Performance Scan (DLPS). Researchers and practitioners alike can use the various performance properties identified and discussed as guidelines to better configure and implement their Hyperledger Fabric network. Likewise, they are encouraged to use the DLPS framework to conduct their measurements.