Simulate and test your DLT

Agreement Solutions’ simulations enable performance data generation prior to development, deployment or release of a DLT system. We simulate the behaviour of a DLT system down to the communication protocol and reflect physical resources of the system, ensuring a high degree of real-world applicability. Different DLT platforms and consensus algorithms are implemented in the simulator. It allows a to run sweeps of design options and the identification of bottlenecks. The simulator can map usage scenarios to improve risk management and understanding of critical rare events. The parameterization allows to generate data on the system behaviour with a fraction of the effort of other available methods. Depending on your use case data can be generated within days to weeks.

Platform

HL fabric, Quantum Ledger, etc.

Algorithm

Paxos, Raft, AllConcur+, etc.

Number of

hosts, clients, orderers

Size of

messages

Application model

consensus algorithm

communication protocol

cryptographic primitives

System model

physical network

network congestion

CPU work loads

Latencies

Throughput

Scenario-based risk management

Show case of an analysis by simulations of a Hyperledger Fabric system

To demonstrate the potential of Agreement Solutions’ simulations, we have published a showcase analysis of a Hyperledger Fabric system. The simulations reveal a variety of non-intuitive system behaviours, e.g. scaling bottlenecks of various architecture designs. Improve your decision making by incorporating simulation data into the design phase of your DLT project.

Simplified version of simulations available free of charge

We offer simplified versions of our simulations free of charge. Feel free to send us your parametrization via this link.

Summary of benefits of working with Agreement Solutions’ simulations

  • Data generation prior to development, deployment or release of a DLT system
  • Efficient process from system definition to analysis with short lead times
  • Evaluate different DLT platforms and consensus algorithms
  • Compare different design options quantitatively
  • Identify and remedy future bottlenecks in your design
  • Improve your risk management by deepening your understanding of critical, rare-event scenarios