research/rln-delay-simulations/README.md

rln-delay-simulations

This folder contains a shadow configuration to simulate 1000 nwaku nodes in an end to end setup:

• nwaku binaries are used, built with make wakunode2 but with a minor modification, see simulations
• rln is used with hardcoded static memberships, to avoid the sepolia node + contract, see.
• Focused on measuring message propagation delays. Each message that is sent, encodes the timestamp when it was created.
• Requires significant resources to run (tested with 256 GB RAM)
• See simulation parameters: latency, bandwidth, amount of nodes, amount of publishers.
• Note that due to TCP flow control, when using big messages the first ones to arrive will show a higher delay. Filter them out to not bias the measurements.

How to run

Get nwaku codebase and checkout to simulations branch, build it and start the shadow simulation. Ensure path points to the wakunode2 binary and you have enough resources.

git clone https://github.com/waku-org/nwaku.git
cd nwaku
git checkout simulations
make wakunode2
shadow shadow.yaml

How to analyze

First check that the simulation finished ok. Check that the numbers match.

grep -nr 'ended_simulation' shadow.data | wc -l
# expected: 1000 (simulation finished ok in all nodes)

grep -nr 'tx_msg' shadow.data | wc -l
# expected: 10 (total of published messages)

grep -nr 'rx_msg' shadow.data | wc -l
# expected: 9990 (total rx messages)

Get metrics:

grep -nr 'rx_msg' shadow.data > latency.txt
grep -nr 'mesh_size' shadow.data > mesh_size.txt

Print results:

python analyze.py latency.txt "arrival_diff="
python analyze.py mesh_size.txt "mesh_size="