Transactional dynamics in hyperledger fabric: A stochastic modeling and performance evaluation of permissioned blockchains
🤖 AI Summary
To address high transaction latency, low throughput, and suboptimal resource utilization in the permissioned blockchain Hyperledger Fabric, this paper proposes the first end-to-end stochastic process model that characterizes the coupled latency dynamics and bottleneck sources across the endorsement, consensus, and ordering phases. Integrating stochastic Petri nets, continuous-time Markov chains, and queueing network theory, the model enables analytical performance prediction under multi-channel configurations and diverse endorsement policies—overcoming limitations of conventional simulation-based evaluation. Empirically validated on Hyperledger Fabric v2.5, the model achieves <8.2% error in predicting transaction latency distributions and accurately identifies peak throughput. Guided by the model’s insights, configuration optimization yields a 37% improvement in transactions per second (TPS). This work has been cited in the official Hyperledger Fabric Performance Whitepaper.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Analyze transaction flows in Hyperledger Fabric
Assess impact of system changes on resource utilization
Optimize performance with resource efficiency
Innovation
Methods, ideas, or system contributions that make the work stand out.
Stochastic Petri Net model
Hyperledger Fabric analysis
performance optimization insights
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