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Commit 5991c7ca authored by Bartolomeo Berend Müller's avatar Bartolomeo Berend Müller
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Add graphs for rate with cutoff

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pq-tls-benchmark-framework/emulation-exp/code/kex/saved/qlogdir/cquiche-bbr2-minimal-cwnd-is-2.sqlog 0 → 100644
+ 37
− 0
View file @ 5991c7ca
{"qlog_version":"0.3","qlog_format":"JSON-SEQ","title":"cquiche_s_timer qlog","description":"cquiche_s_timer qlog id=172ce7b0686cf7d3cd8e9bef4203a68030375ceb","trace":{"vantage_point":{"type":"client"},"title":"cquiche_s_timer qlog","description":"cquiche_s_timer qlog id=172ce7b0686cf7d3cd8e9bef4203a68030375ceb","configuration":{"time_offset":0.0}}}
{"time":0.0,"name":"transport:parameters_set","data":{"owner":"local","tls_cipher":"None","disable_active_migration":false,"max_idle_timeout":30000,"max_udp_payload_size":1200,"ack_delay_exponent":3,"max_ack_delay":25,"active_connection_id_limit":2,"initial_max_data":10000000,"initial_max_stream_data_bidi_local":1000000,"initial_max_stream_data_bidi_remote":1000000,"initial_max_stream_data_uni":1000000,"initial_max_streams_bidi":100,"initial_max_streams_uni":100}}
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{"time":0.05108,"name":"recovery:metrics_updated","data":{"smoothed_rtt":333.0,"rtt_variance":166.5,"congestion_window":12000,"bytes_in_flight":1200,"ssthresh":18446744073709551615}}
{"time":7.533018,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":1,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":7.533018,"frames":[{"frame_type":"crypto","offset":1133,"length":1132}]}}
{"time":7.533018,"name":"recovery:metrics_updated","data":{"bytes_in_flight":2400}}
{"time":7.5579987,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":2,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":7.5579987,"frames":[{"frame_type":"crypto","offset":2265,"length":1132}]}}
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{"time":7.5664277,"name":"recovery:metrics_updated","data":{"bytes_in_flight":4800}}
{"time":7.573536,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":4,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":7.573536,"frames":[{"frame_type":"crypto","offset":4529,"length":1132}]}}
{"time":7.573536,"name":"recovery:metrics_updated","data":{"bytes_in_flight":6000}}
{"time":7.580239,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":5,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":7.580239,"frames":[{"frame_type":"crypto","offset":5661,"length":1132}]}}
{"time":7.580239,"name":"recovery:metrics_updated","data":{"bytes_in_flight":7200}}
{"time":7.591206,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":6,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":7.591206,"frames":[{"frame_type":"crypto","offset":6793,"length":1132}]}}
{"time":7.591206,"name":"recovery:metrics_updated","data":{"bytes_in_flight":8400}}
{"time":7.597909,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":7,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":7.597909,"frames":[{"frame_type":"crypto","offset":7925,"length":1132}]}}
{"time":7.597909,"name":"recovery:metrics_updated","data":{"bytes_in_flight":9600}}
{"time":7.604916,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":8,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":7.604916,"frames":[{"frame_type":"crypto","offset":9057,"length":1132}]}}
{"time":7.604916,"name":"recovery:metrics_updated","data":{"bytes_in_flight":10800}}
{"time":7.611313,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":9,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":7.611313,"frames":[{"frame_type":"crypto","offset":10189,"length":1132}]}}
{"time":7.611313,"name":"recovery:metrics_updated","data":{"bytes_in_flight":12000}}
{"time":1007.7312,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":10,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":1007.7312,"frames":[{"frame_type":"crypto","offset":0,"length":1133}]}}
{"time":1007.7312,"name":"recovery:metrics_updated","data":{"bytes_in_flight":13200}}
{"time":3008.1707,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":11,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":3008.1707,"frames":[{"frame_type":"crypto","offset":0,"length":1133}]}}
{"time":3008.1707,"name":"recovery:metrics_updated","data":{"bytes_in_flight":14400}}
{"time":3008.514,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":12,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":3008.514,"frames":[{"frame_type":"crypto","offset":1133,"length":1132}]}}
{"time":3008.514,"name":"recovery:metrics_updated","data":{"bytes_in_flight":15600}}
{"time":7008.554,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":13,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":7008.554,"frames":[{"frame_type":"crypto","offset":0,"length":1133}]}}
{"time":7008.554,"name":"recovery:metrics_updated","data":{"bytes_in_flight":16800}}
{"time":7009.311,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":14,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":7009.311,"frames":[{"frame_type":"crypto","offset":1133,"length":1132}]}}
{"time":7009.311,"name":"recovery:metrics_updated","data":{"bytes_in_flight":18000}}
{"time":15010.692,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":15,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":15010.692,"frames":[{"frame_type":"crypto","offset":0,"length":1133}]}}
{"time":15010.692,"name":"recovery:metrics_updated","data":{"bytes_in_flight":19200}}
{"time":15010.9375,"name":"transport:packet_sent","data":{"header":{"packet_type":"initial","packet_number":16,"version":"1","scil":20,"dcil":16,"scid":"172ce7b0686cf7d3cd8e9bef4203a68030375ceb","dcid":"60dbf399c81795d4d4400145aeec37ad"},"raw":{"length":1200,"payload_length":1137},"send_at_time":15010.9375,"frames":[{"frame_type":"crypto","offset":1133,"length":1132}]}}
{"time":15010.9375,"name":"recovery:metrics_updated","data":{"bytes_in_flight":20400}}
{"time":30017.871,"name":"connectivity:connection_closed","data":{"owner":"local","reason":"Failed to establish connection","trigger":"handshake_timeout"}}
pq-tls-benchmark-framework/emulation-exp/code/kex/scripts/generate_graphs.py
+ 132
− 8
View file @ 5991c7ca
......@@ -674,6 +674,33 @@ def transform_cmap_color(
return r, g, b, a
def get_label_for_scenario(label: str) -> str:
match label:
case "delay":
return "Latenz (ms)"
case "jitter_delay20ms":
return "Jitter (ms)"
case "packetloss":
return "Paketverlustrate (%)"
case "duplicate":
return "Duplikationsrate (%)"
case "corrupt":
return "Korruptierungsrate (%)"
case "reorder":
return "Neuanordnungsrate (%)"
case "rate_both":
return "Bandbreite (Mbit/s)"
case "rate_client":
return "Bandbreite Client (Mbit/s)"
case "rate_server":
return "Bandbreite Server (Mbit/s)"
case "static":
return "Nummerierung der Messung"
case _:
print(f"NO LABEL FOUND FOR {label}", file=sys.stderr)
sys.exit(1)
# plots lines of different statistical values
def plot_lines(data):
def plot_lines_for_sec_level(
......@@ -732,8 +759,14 @@ def plot_lines(data):
plt.ylim(bottom=0)
plt.xlim(left=0, right=x.max() + (x.max() / 50))
plt.xlabel(row["scenario"])
plt.ylabel(f"Time-to-first-byte (ms)")
plt.xlabel(get_label_for_scenario(row["scenario"]))
y_label = f"Time-to-first-byte (ms)"
if line_type == "iqr":
y_label = "Interquartilsabstand (ms)"
if line_type == "riqr":
y_label = "Interquartilsabstand / Median"
plt.ylabel(y_label)
# plt.title(
# f"Medians of {row['scenario']} in {row['protocol']} in {row['sec_level']}"
# )
......@@ -814,8 +847,13 @@ def plot_lines(data):
plt.ylim(bottom=0)
plt.xlim(left=0, right=x.max() + (x.max() / 50))
plt.xlabel(row["scenario"])
plt.ylabel(f"Time-to-first-byte (ms)")
plt.xlabel(get_label_for_scenario(row["scenario"]))
y_label = f"Time-to-first-byte (ms)"
if line_type == "iqr":
y_label = "Interquartilsabstand (ms)"
if line_type == "riqr":
y_label = "Interquartilsabstand / Median"
plt.ylabel(y_label)
# plt.title(
# f"Medians of {row['scenario']} in {row['protocol']} in {row['sec_level']}"
# )
......@@ -874,8 +912,13 @@ def plot_lines(data):
plt.ylim(bottom=0)
plt.xlim(left=0, right=x.max() + (x.max() / 50))
plt.xlabel(row["scenario"])
plt.ylabel(f"Time-to-first-byte (ms)")
plt.xlabel(get_label_for_scenario(row["scenario"]))
y_label = f"Time-to-first-byte (ms)"
if line_type == "iqr":
y_label = "Interquartilsabstand (ms)"
if line_type == "riqr":
y_label = "Interquartilsabstand / Median"
plt.ylabel(y_label)
plt.grid()
plt.legend(
bbox_to_anchor=(0.5, 1), loc="lower center", ncol=3, fontsize="small"
......@@ -892,6 +935,85 @@ def plot_lines(data):
)
plt.close()
def plot_medians_for_rate_with_cutoff(data, cutoff=5, combined_with_hybrids=False):
print("plot_medians_for_rate")
os.makedirs(
f"{PLOTS_DIR}/lines/medians-rate-with-cutoff/combined-with-hybrids",
mode=0o777,
exist_ok=True,
)
line_type = "median"
for scenario in ["rate_both", "rate_client", "rate_server"]:
# get all combination of scenario, protocol, sec_level
ldata = data
ldata = ldata.query("scenario == @scenario")
unique_combinations = ldata[
["scenario", "protocol", "sec_level"]
].drop_duplicates()
# print(len(unique_combinations))
# print(unique_combinations)
for _, row in unique_combinations.iterrows():
sec_level = row["sec_level"]
if combined_with_hybrids:
sec_level = map_security_level_hybrid_together(row["sec_level"])
if sec_level is None:
continue
filtered_data = filter_data(
data,
scenario=scenario,
protocol=row["protocol"],
sec_level=sec_level,
)
# print(
# f"scenario: {scenario}, protocol: {row['protocol']}, sec_level: {row['sec_level']}"
# )
plt.figure()
for idx, kem_alg in enumerate(
filtered_data["kem_alg"].unique().sort_values()
):
color, mode = get_color_and_mode(
kem_alg, combined_with_hybrids=combined_with_hybrids
)
filtered_data_single_kem_alg = filter_data(
filtered_data, kem_alg=kem_alg
)
y = filtered_data_single_kem_alg[line_type]
x = get_x_axis(scenario, filtered_data_single_kem_alg, len(y))
plt.plot(
x, y, linestyle=mode, marker=".", color=color, label=kem_alg
)
plt.ylim(bottom=0)
plt.xlim(left=0, right=cutoff + 0.2)
plt.xlabel(get_label_for_scenario(scenario))
plt.ylabel("Time-to-first-byte (ms)")
plt.xticks(np.arange(0, cutoff + 0.5, 0.5))
plt.grid()
plt.legend(
bbox_to_anchor=(0.5, 1),
loc="lower center",
ncol=3,
fontsize="small",
)
plt.tight_layout()
subdir = ""
appendix = ""
cutoff_str = f"-cutoff-at-{cutoff}"
if combined_with_hybrids:
subdir = "combined-with-hybrids/"
appendix = "-combined-with-hybrids"
plt.savefig(
f"{PLOTS_DIR}/lines/medians-rate-with-cutoff/{subdir}{line_type}-{scenario}-{row['protocol']}-{row['sec_level']}{cutoff_str}{appendix}.pdf"
)
plt.close()
def plot_qtls_of_single_algorithm(data):
print("plot_qtls_of_single_algorithms")
os.makedirs(
......@@ -932,7 +1054,7 @@ def plot_lines(data):
plt.ylim(bottom=0)
plt.xlim(left=0, right=x.max() + (x.max() / 50))
plt.xlabel(row["scenario"])
plt.xlabel(get_label_for_scenario(row["scenario"]))
plt.ylabel(f"Time-to-first-byte (ms)")
plt.grid()
plt.legend(
......@@ -980,6 +1102,8 @@ def plot_lines(data):
)
plot_qtls_of_single_algorithm(data)
plot_medians_for_rate_with_cutoff(data, cutoff=5, combined_with_hybrids=False)
plot_medians_for_rate_with_cutoff(data, cutoff=5, combined_with_hybrids=True)
# plots distributions of the individual data points
......@@ -1084,7 +1208,7 @@ def plot_distributions(data):
plt.ylim(bottom=0, top=1.5 * filtered_data["qtl_95"].max())
plt.xticks(tick_values)
plt.xlim(left=-1.5)
plt.xlabel(row["scenario"])
plt.xlabel(get_label_for_scenario(row["scenario"]))
plt.ylabel(f"Time-to-first-byte (ms)")
plt.grid()
......
pq-tls-benchmark-framework/emulation-exp/code/kex/scripts/queries.py
+ 54
− 16
View file @ 5991c7ca
......@@ -12,7 +12,8 @@ def main():
data = pd.read_feather(f"{FEATHERS_DIR}/data.feather")
# data = pd.read_feather(f"{FEATHERS_DIR}/data_run_20241028.feather")
loss_calculations()
bandwith_calcs()
# loss_calculations()
# static_scenario_statistical_analysis(data)
# median_of_all_static_runs_per_algorithm(data)
# stats_of_qtl95_of_packetloss(data)
......@@ -23,21 +24,39 @@ def main():
# print_kem_ids()
def loss_calculations():
udp_packets_df = pd.read_feather("feathers/udp_packets.feather")
df = ap.get_packets_sent_by_node(udp_packets_df)
def bandwith_calcs():
df = get_cic_and_sic()
print("\n\n Loss calculations")
df = df.drop(columns=["length_public_key", "length_ciphertext"])
# print(df)
df["cic"] = df["client_sent_packets_with_crypto_count"] - 1
df["sic"] = df["server_sent_packets_with_crypto_count"]
df = df.drop(
columns=[
"client_sent_packets_with_crypto_count",
"server_sent_packets_with_crypto_count",
]
)
def calc_delay_cuz_of_bandwidth_in_ms(cic, sic, bandwidth, packetlength=1200):
"""
Calculates the delay in ms caused by bandwidth.
Args:
cic: client initial count.
sic: server initial count.
bandwidth: bandwidth in bits per second.
packetlength: length of a packet in bytes.
Returns:
delay in seconds.
"""
SECONDS_IN_MS = 1000
return (cic + sic) * packetlength * 8 / bandwidth * SECONDS_IN_MS
for bw in [0.1, 0.25, 0.5, 1, 3, 5, 500]:
df[f"t_delay_{bw}"] = df.apply(
lambda row: calc_delay_cuz_of_bandwidth_in_ms(
row["cic"], row["sic"], bw * 1000000
),
axis=1,
)
print(df)
return df
def loss_calculations():
df = get_cic_and_sic()
# p_noOneSec does not make sense if cic or sic is bigger than 10 -> look thesis
df = df.query("cic <= 10 and sic <= 10")
......@@ -257,4 +276,23 @@ def print_kem_ids():
print(data)
main()
def get_cic_and_sic():
udp_packets_df = pd.read_feather("feathers/udp_packets.feather")
df = ap.get_packets_sent_by_node(udp_packets_df)
print("\n\n Loss calculations")
df = df.drop(columns=["length_public_key", "length_ciphertext"])
# print(df)
df["cic"] = df["client_sent_packets_with_crypto_count"] - 1
df["sic"] = df["server_sent_packets_with_crypto_count"]
df = df.drop(
columns=[
"client_sent_packets_with_crypto_count",
"server_sent_packets_with_crypto_count",
]
)
return df
if __name__ == "__main__":
main()
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