Performance modeling and analysis of a Hyperledgerbased system using GSPN Abstract   As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of industry use cases ranging from governance to finance. In this paper, we propose a model to analyze the performance of a Hyperledgerbased system by using Generalized Stochastic Petri Nets (GSPN). This model decomposes a transaction flow into multiple phases and provides a simulationbased approach to obtain the system latency and throughput with a specific arrival rate. Based on this model, we analyze the impact of different configurations of ordering service on system performance to find out the bottleneck. Moreover, a mathematical configuration selection approach is proposed to determine the best configuration which can maximize the system throughput. Finally, extensive experiments are performed on a realtime system to validate the proposed model and approaches.

FastFabric: Scaling Hyperledger Fabric to 20,000 Transactions per Second. Blockchain technologies are expected to make a significant impact on a variety of industries. However, one issue holding them back is their limited transaction throughput, especially compared to established solutions such as distributed database systems. In this paper, we rearchitect a modern permissioned blockchain system, Hyperledger Fabric, to increase transaction throughput from 3,000 to 20,000 transactions per second. We focus on performance bottlenecks beyond the consensus mechanism, and we propose architectural changes that reduce computation and IO overhead during transaction ordering and validation to greatly improve throughput. Notably, our optimizations are fully plugandplay and do not require any interface changes to Hyperledger Fabric.