Revert "rln-delay-simulations"

.gitignore

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-**/.DS_Store
-rln-delay-simulations/.idea/*
-**/__pycache__

rln-delay-simulations/README.md

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-## 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](https://github.com/waku-org/nwaku/compare/master...simulations)
-* `rln` is used with hardcoded static memberships, to avoid the sepolia node + contract, [see](https://raw.githubusercontent.com/waku-org/nwaku/master/waku/waku_rln_relay/constants.nim).
-* 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](https://github.com/waku-org/nwaku/tree/simulations) branch, build it and start the [shadow](https://github.com/shadow/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="
-```

rln-delay-simulations/analyze.py

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-import numpy as np
-import sys
-def load(file_name, field):
-    latencies = []
-    with open(file_name, "r") as file:
-        for line in file.readlines():
-            if field in line:
-                # first message bias the latency due to tcp flow control
-                if "seq=" in line:
-                    seq = int(line.strip().split("seq=")[1].split(" ")[0])
-                    if seq in [0]:
-                        continue
-                x = line.strip().split(field)[1].split(" ")[0]
-                latencies.append(int(x))
-    return np.array(latencies)
-
-
-if __name__ == "__main__":
-    file = sys.argv[1]
-    field = sys.argv[2]
-    array = load(file, field)
-
-    print("Data file:", file, "field:", field)
-    print(f"number_samples={array.size}")
-    print(f"Percentiles. P75={np.percentile(array, 75)} P95={np.percentile(array, 95)}")
-    print(f"Statistics. mean={np.mean(array)} max={array.max()} min={array.min()}")

rln-delay-simulations/plot.py

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-import matplotlib.pyplot as plt
-import scienceplots
-import numpy as np
-from analyze import load
-
-latencies = [load("raw/latency_10kb.txt", "arrival_diff="),
-             load("raw/latency_100kb.txt", "arrival_diff="),
-             load("raw/latency_500kb.txt", "arrival_diff=")]
-
-print(latencies)
-
-with plt.style.context(['science', 'ieee']):
-    fig, ax = plt.subplots()
-    bp = ax.boxplot(latencies,notch=True, vert=True, patch_artist=True,
-                    showfliers=True, whis=100000000000)
-
-    for patch, color in zip(bp['boxes'], ['red', 'blue', 'green']):
-        patch.set_facecolor(color)
-
-    ax.set(title="Message latencies distribution\nD=6 nodes=1000 samples="+str(latencies[1].size), xlabel='Scenario', ylabel='Message propagation time (ms)')
-    ax.grid(linestyle='-')
-    my_legend = []
-    for msg_size in [10, 100, 500]:
-        my_legend.append("Message size: " + str(msg_size) + " kB")
-    ax.legend([bp["boxes"][i] for i in range(len(my_legend))], my_legend, loc='upper left', fontsize=5)
-
-for i, line in enumerate(bp['medians']):
-    x, y = line.get_xydata()[1]
-    text = r' $ \mu =$ ' + '{:.0f} ms\n'.format(latencies[i].mean(axis=0)) + r' $ p_{95} = $ ' + '{:.0f} ms'.format(np.percentile(latencies[i], 95))
-    ax.annotate(text, xy=(x + 0.1, y), fontsize=6)
-
-fig.set_size_inches(4, 3)
-fig.savefig('plot.jpg', dpi=600)