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rln-delay-simulations/analyze.py

@@ -9,6 +9,11 @@ latencies = []
 with open(file, "r") as file:
     for line in file.readlines():
         if field in line:
+            seq = int(line.strip().split("seq=")[1].split(" ")[0])
+            # first message bias the latency due to tcp flow control
+            if seq in [0]:
+                continue
+            seq = line.strip().split("seq=")[1].split(" ")[0]
             x = line.strip().split(field)[1].split(" ")[0]
             latencies.append(int(x))
 

rln-delay-simulations/plot.py

@@ -0,0 +1,55 @@
+import matplotlib.pyplot as plt
+import scienceplots
+import numpy as np
+import math
+import random
+
+def load(file):
+    field = "arrival_diff="
+    latencies = []
+    with open(file, "r") as file:
+        for line in file.readlines():
+            if field in line:
+                x = line.strip().split(field)[1].split(" ")[0]
+                latencies.append(int(x))
+    return np.array(latencies)
+
+latencies = [load("latency_10kb.txt"), load("latency_100kb.txt"), load("latency_500kb.txt")]
+
+with plt.style.context(['science', 'ieee']):
+    fig, ax = plt.subplots()
+
+    labels = []
+
+    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 rate (msg/s)", 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)
+
+    ax.autoscale(tight=False)
+
+#means = [i.mean(axis=0) for i in q]
+#stds =[i.std(axis=0) for i in q]
+#per_95 = [np.percentile(i, 95) for i in q]
+#for i, line in enumerate(bp['medians']):
+#    x, y = line.get_xydata()[1]
+#    text = r' $ \mu =$ ' + '{:.2f}\n'.format(means[i]) + r' $ p_{95} = $ ' + '{:.2f}'.format(per_95[i])
+#    ax.annotate(text, xy=(x+0.1, y), fontsize=6)
+#
+#
+
+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.savefig('plot.jpg', dpi=600)

rln-delay-simulations/shadow.yaml

@@ -1,7 +1,7 @@
 general:
   bootstrap_end_time: 30s
   heartbeat_interval: 12s
-  stop_time: 4m
+  stop_time: 10m
   progress: true
 
 experimental:
@@ -40,7 +40,7 @@ hosts:
             --rln-relay-membership-index=0
             --nat=extip:0.0.0.0
       start_time: 5s
-      environment: {"PEERS": "1000", "CONNECTTO": "50", "NUMPUBLISHERS": "5", "MSGSIZEKBYTES": "1"}
+      environment: {"PEERS": "1000", "CONNECTTO": "50", "NUMPUBLISHERS": "1", "MSGSIZEKBYTES": "10"}
   peer2: *client_host
   peer3: *client_host
   peer4: *client_host