research/rln-delay-simulations

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
• Minor changes in nwaku are required, to timestamp messages and connect the peers without discovery. See simulations branch.
• rln is used with hardcoded 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.
• Same setup can be reused with different parameters, configured either via flags (see shadow.yaml) or modifying the code (see simulations).
• TODO delay + bandwidth TODO add payload messages.

How to run

Get nwaku code with the modifications and compile it. See diff of latest commit.

Get the 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: 15 (total of published messages)

• no errors in any stderr: eg: shadow.data/hosts/peer1/wakunode2.1000.stderr
• see msg published: grep -nr '[TX MSG]*' shadow.data | wc -l
• grep -nr '[RX MSG]*' shadow.data | wc -l

calculate metrics:

latency

grep -nr '\[RX MSG\]*' shadow.data > latency.txt
python metrics.py latency.txt "diff: " " milliseconds"

Amount of samples: 14985
percentile 75:  300.0
percentile 25:  201.0
mode : ModeResult(mode=300, count=4650)
worst:  401
best:  100



file:  latency.txt
parse start:  diff:   parse end:   milliseconds
[301 400 400 ... 300 502 601]
Amount of samples: 14985
percentile 75:  402.0
percentile 25:  202.0
mode : ModeResult(mode=400, count=1542)
worst:  1300
best:  100

mesh

grep -nr 'mesh size' shadow.data > mesh.txt
python metrics.py mesh.txt "mesh size: " " of topic"

Amount of samples: 1000
percentile 75:  7.0
percentile 25:  5.0
mode : ModeResult(mode=5, count=248)
worst:  12
best:  4

Amount of samples: 1000
percentile 75:  3.0
percentile 25:  2.0
mode : ModeResult(mode=2, count=469)
worst:  5
best:  2

TODO

Output

TODO

Amount of samples: 14985
percentile 75:  300.0
percentile 25:  201.0
mode : ModeResult(mode=300, count=4650)
worst:  401
best:  100