Blockchain simulator

Chain-based

Bitcoin Simulator

Arthur Gervais (ETH Zurich, Switzerland) et al.

In VIVES paper,

it can only simulate up to 6,000 nodes and has no transactions

VIBES

Master’s Thesis by Fabian Sch¨ussler

Parameters (in Section 5.5)

E.g. blockTime: average, numberOfNeighbours, numberOfNodes: number of nodes, latency, maxBlockSize, networkBandwidth, attacker's hashRate, number of confirmations

Attacks

Double-spending or flood attack (selfish-mining is future work)

eVIBES: Extension for Ethereum

Visualization in React

Distributed Consensus Simulator

Shanghai Jiao Tong University

Python

SimBlock

TrustChain

OMNeT++

Shadow

Bitcoin

Scalability Analysis of Blockchains Through Blockchain Simulation

Sneha Goswami (University of Nevada)

Pierre-Yves Piriou, Jean-Francois Dumas (EDF R&D)

Monte Carlo simulations

shardSim

for running in a supercomputer

Sharding simulator

Ethereum grant

Barcelona Supercomputing Center – Scalability Grant – $50K. Sharding simulation.

BFT-based

Ripple simulator

Raftscope

Mir-protocol, Tendermint, Algorand

No malicious node

Hyperledger Blockchain Performance Metrics

DAG

DAG Sim

Asynchronous, continuous time, and multi-agent simulation framework for DAG-based cryptocurrencies.

IOTA, SPECTRE(WIP)

Model honest and semi-honest actors in the system to analyse the behaviour of IOTA.

Result: the agents that have low latency and a high connection degree have a higher probability of having their transactions accepted in the network with honest and semi-honest strategies.

CIDDS

Master's thesis by Mohamed Riswan Abdul Lathif (Technical University of Munich)

For IOTA

Parameters: the number of nodes, the number of transactions (n), the transaction rate (λ), the level of randomness (α) and the tip (i.e. a transaction to approve) selection algorithm

Homogeneous poisson point process for incoming transactions

SNOW

Demo app of Snow (consensus in Avalanche)

PHANTOM

Others