Performance Modeling & Analysis of Hyperledger Fabric (Permissioned Blockchain Network)

A blockchain is an immutable record of transactions (called ledger ) between a distributed set of mutually untrusting peers. Although blockchain networks provide tremendous benefits, there are concerns about whether their performance would be a hindrance to its adoption. Our research is focused on Hyperledger Fabric (HLF), which is an open-source implementation of a distributed ledger platform for running smart contracts in a modular architecture. This thesis presents our research on performance modeling of Hyperledger Fabric using a Stochastic Petri Nets modeling formalism known as Stochastic Reward Nets (SRN). We capture the key system operations and complex interactions between them. We focus on two different releases of HLF, viz. v0.6 and v1.0+ (V1). HLF v0.6 follows a traditional state-machine replication architecture followed by many other blockchain platforms, whereas HLF V1 follows a novel execute-order-validate architecture. We parameterize and validate our models with data collected from a real-world Fabric network setup. Our models provide a quantitative framework that helps compare different deployment configurations of Fabric and make design trade-off decisions. It also enables us to compute performance for a system with proposed architectural improvements before they are implemented. From our analysis, we recommend design improvements along with the estimates of performance improvement. Overall, our models provide a stepping stone to the Hyperledger Fabric community towards achieving optimal performance of Fabric in the real-world deployments.