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process_results.py
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import json
import os
import pathlib
import re
from pathlib import Path
from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
from scapy.all import rdpcap
from scapy.layers.inet import IP, TCP, UDP
if TYPE_CHECKING:
from lab import ExperimentResult
def Average(lst):
if lst:
return sum(lst) / len(lst)
else:
return 0
def get_godash_result_as_dataframe(godash_result_path: str) -> pd.DataFrame:
with open(godash_result_path, "r+") as f:
lines = f.readlines()
f.seek(0)
f.writelines([line for line in lines if not re.match(r"^[0-9]+$", line)])
f.truncate()
return pd.read_csv(godash_result_path, delim_whitespace=True) # type: ignore
def process_pcap_tcp(experiment_result: "ExperimentResult"):
server_ip = experiment_result["server_ip"]
pcap = rdpcap(experiment_result["experiment_host_pcap_path"])
godash_result = get_godash_result_as_dataframe(
experiment_result["experiment_godash_result_path"]
)
godash_timestamps = godash_result["Arr_time"]
godash_timestamps_before = pd.concat(
[pd.Series([0]), godash_timestamps[:-1]], ignore_index=True
)
godash_timestamps_after = godash_timestamps[0:]
connection = set()
for ppp in pcap:
if ppp.haslayer(TCP) and ppp.haslayer(IP):
if ppp[IP].dst == server_ip:
if ppp[TCP].flags == "FA" or ppp[TCP].flags == "F":
connection.add(ppp[TCP].sport)
df = pd.DataFrame(
columns=[
"Arr_time",
"interIntervalTimeDP",
"interIntervalTimeDP_GT100",
"AvgTimeBWDPK",
"AvgTimeBWDPK_GT100",
"DThroughput",
"DTTime",
"DTotal_Packets",
"DTotal_Packets_GT100",
"STD_Less",
"STD_GT",
"DP_Times_str",
"Packets_str",
"GT100_Times_str",
"GT100_Packets_str",
]
)
for beginning, end in zip(godash_timestamps_before, godash_timestamps_after):
beginning = int(beginning)
end = int(end)
segment_time = (end - beginning) / 1000
dl_05 = 0
dc_05 = 0
dc_gt100_05 = 0
da_05 = 0
dt_05 = 0
packets_time_05 = []
d_ia_05 = []
d_pk_size_05 = []
d_pk_size_gt100_05 = []
ul_05 = 0
uc_gt100_05 = 0
ua_05 = 0
ut_05 = 0
uc_05 = 0
u_ia_05 = []
u_pk_size_05 = []
u_pk_size_gt100_05 = []
d_packets_gt_100_time = []
u_packets_time_05 = []
u_packets_gt_100_time = []
for p in pcap:
packet_elapsed_time = float(p.time - pcap[0].time)
if (
p.haslayer(TCP)
and (packet_elapsed_time >= beginning / 1000)
and (packet_elapsed_time < end / 1000)
and (p[TCP].sport in connection or p[TCP].dport in connection)
):
if p[IP].src == server_ip: # downlink
dl_05 = dl_05 + (int(len(p[IP])) * 8)
dc_05 = dc_05 + 1
if int(len(p)) >= 100: # ignore ack pk
dc_gt100_05 = dc_gt100_05 + 1
packets_time_05.append(float(p.time))
da_05 = float(p.time - dt_05)
d_ia_05.append(da_05)
dt_05 = p.time
d_pk_size_05.append(int(len(p[IP])))
if int(len(p)) >= 100: # ignore ack pk
d_packets_gt_100_time.append(p.time)
d_pk_size_gt100_05.append(int(len(p[IP])))
if p[IP].dst == server_ip: # uplink
ul_05 = ul_05 + (int(len(p[IP])) * 8)
uc_05 = uc_05 + 1
if int(len(p)) >= 100: # ignore ack pk
uc_gt100_05 = uc_gt100_05 + 1
## ia and packet size
u_packets_time_05.append(float(p.time))
ua_05 = float(p.time - ut_05)
u_ia_05.append(ua_05)
ut_05 = p.time
u_pk_size_05.append(int(len(p[IP])))
if int(len(p)) >= 100: # ignore ack pk
u_packets_gt_100_time.append(p.time)
u_pk_size_gt100_05.append(int(len(p[IP])))
# -----------------------------------Download Block------------------------
if packets_time_05:
if dc_05 > 2:
tpTime_05 = packets_time_05[-1] - packets_time_05[0]
std_less = np.std(d_pk_size_05)
else:
tpTime_05 = 1
std_less = 0
if dc_gt100_05 > 2:
tpTime_05_GT100 = d_packets_gt_100_time[-1] - d_packets_gt_100_time[0]
AvgTime_List_GT100 = [
d_packets_gt_100_time[i + 1] - d_packets_gt_100_time[i]
for i in range(len(d_packets_gt_100_time) - 1)
]
AvgTime_GT100 = Average(AvgTime_List_GT100)
std_grt = np.std(d_pk_size_gt100_05)
else:
AvgTime_GT100 = 0
tpTime_05_GT100 = 0
std_grt = 0
TP_05 = dl_05 / tpTime_05
TP_05_Time = dl_05 / segment_time
# Average time between packets
time_between_each_packets_05 = [
packets_time_05[i + 1] - packets_time_05[i]
for i in range(len(packets_time_05) - 1)
]
average_05 = Average(time_between_each_packets_05)
tms_string = ""
for tms in packets_time_05:
tms_string = tms_string + str(tms) + "~"
pkt_string = ""
for pk in d_pk_size_05:
pkt_string = pkt_string + str(pk) + "~"
d_packet_gt100_time_str = ""
for dpg100t in d_packets_gt_100_time:
d_packet_gt100_time_str = d_packet_gt100_time_str + str(dpg100t) + "~"
d_packet_gt100_str = ""
for dpg100 in d_pk_size_gt100_05:
d_packet_gt100_str = d_packet_gt100_str + str(dpg100) + "~"
df = df.append(
pd.Series(
[
end,
tpTime_05,
tpTime_05_GT100,
average_05,
AvgTime_GT100,
TP_05,
TP_05_Time,
dc_05,
dc_gt100_05,
std_less,
std_grt,
tms_string,
pkt_string,
d_packet_gt100_time_str,
d_packet_gt100_str,
],
index=df.columns,
),
ignore_index=True,
)
df.to_csv(
(
pathlib.Path(experiment_result["experiment_folder"]) / "qos.csv"
).as_posix(),
index=False,
header=True,
)
merged_columns = pd.merge(godash_result, df, on="Arr_time", how="left")
merged_columns.to_csv(
(pathlib.Path(experiment_result["experiment_folder"]) / "all.csv").as_posix(),
index=False,
header=True,
)
def process_pcap_quic(experiment_result: "ExperimentResult"):
server_ip = experiment_result["server_ip"]
pcap = rdpcap(experiment_result["experiment_host_pcap_path"])
godash_result = get_godash_result_as_dataframe(
experiment_result["experiment_godash_result_path"]
)
godash_timestamps = godash_result["Arr_time"]
godash_timestamps_before = pd.concat(
[pd.Series([0]), godash_timestamps[:-1]], ignore_index=True
)
godash_timestamps_after = godash_timestamps[0:]
connection = []
for ppp in pcap:
if (
experiment_result["experiment"]["server_protocol"] == "quic"
and ppp.haslayer(UDP)
and ppp.haslayer(IP)
):
if ppp[IP].dst == server_ip or ppp[IP].src == server_ip:
connection.append(ppp)
df = pd.DataFrame(
columns=[
"Arr_time",
"interIntervalTimeDP",
"interIntervalTimeDP_GT100",
"AvgTimeBWDPK",
"AvgTimeBWDPK_GT100",
"DThroughput",
"DTTime",
"DTotal_Packets",
"DTotal_Packets_GT100",
"STD_Less",
"STD_GT",
"DP_Times_str",
"Packets_str",
"GT100_Times_str",
"GT100_Packets_str",
]
)
for beginning, end in zip(godash_timestamps_before, godash_timestamps_after):
beginning = int(beginning)
end = int(end)
segment_time = (end - beginning) / 1000
dl_05 = 0
dc_05 = 0
dc_gt100_05 = 0
da_05 = 0
dt_05 = 0
packets_time_05 = []
d_ia_05 = []
d_pk_size_05 = []
d_pk_size_gt100_05 = []
ul_05 = 0
uc_gt100_05 = 0
ua_05 = 0
ut_05 = 0
uc_05 = 0
u_ia_05 = []
u_pk_size_05 = []
u_pk_size_gt100_05 = []
d_packets_gt_100_time = []
u_packets_time_05 = []
u_packets_gt_100_time = []
for p in connection:
packet_elapsed_time = float(p.time - pcap[0].time)
if (packet_elapsed_time >= beginning / 1000) and (
packet_elapsed_time < end / 1000
):
if p[IP].src == server_ip: # downlink
dl_05 = dl_05 + (int(len(p[IP])) * 8)
dc_05 = dc_05 + 1
if int(len(p)) >= 100: # ignore ack pk
dc_gt100_05 = dc_gt100_05 + 1
packets_time_05.append(float(p.time))
da_05 = float(p.time - dt_05)
d_ia_05.append(da_05)
dt_05 = p.time
d_pk_size_05.append(int(len(p[IP])))
if int(len(p)) >= 100: # ignore ack pk
d_packets_gt_100_time.append(p.time)
d_pk_size_gt100_05.append(int(len(p[IP])))
if p[IP].dst == server_ip: # uplink
ul_05 = ul_05 + (int(len(p[IP])) * 8)
uc_05 = uc_05 + 1
if int(len(p)) >= 100: # ignore ack pk
uc_gt100_05 = uc_gt100_05 + 1
## ia and packet size
u_packets_time_05.append(float(p.time))
ua_05 = float(p.time - ut_05)
u_ia_05.append(ua_05)
ut_05 = p.time
u_pk_size_05.append(int(len(p[IP])))
if int(len(p)) >= 100: # ignore ack pk
u_packets_gt_100_time.append(p.time)
u_pk_size_gt100_05.append(int(len(p[IP])))
# -----------------------------------Download Block------------------------
if packets_time_05:
if dc_05 > 2:
tpTime_05 = packets_time_05[-1] - packets_time_05[0]
std_less = np.std(d_pk_size_05)
else:
tpTime_05 = 1
std_less = 0
if dc_gt100_05 > 2:
tpTime_05_GT100 = d_packets_gt_100_time[-1] - d_packets_gt_100_time[0]
AvgTime_List_GT100 = [
d_packets_gt_100_time[i + 1] - d_packets_gt_100_time[i]
for i in range(len(d_packets_gt_100_time) - 1)
]
AvgTime_GT100 = Average(AvgTime_List_GT100)
std_grt = np.std(d_pk_size_gt100_05)
else:
AvgTime_GT100 = 0
tpTime_05_GT100 = 0
std_grt = 0
TP_05 = dl_05 / tpTime_05
TP_05_Time = dl_05 / segment_time
# Average time between packets
time_between_each_packets_05 = [
packets_time_05[i + 1] - packets_time_05[i]
for i in range(len(packets_time_05) - 1)
]
average_05 = Average(time_between_each_packets_05)
tms_string = ""
for tms in packets_time_05:
tms_string = tms_string + str(tms) + "~"
pkt_string = ""
for pk in d_pk_size_05:
pkt_string = pkt_string + str(pk) + "~"
d_packet_gt100_time_str = ""
for dpg100t in d_packets_gt_100_time:
d_packet_gt100_time_str = d_packet_gt100_time_str + str(dpg100t) + "~"
d_packet_gt100_str = ""
for dpg100 in d_pk_size_gt100_05:
d_packet_gt100_str = d_packet_gt100_str + str(dpg100) + "~"
df = df.append(
pd.Series(
[
end,
tpTime_05,
tpTime_05_GT100,
average_05,
AvgTime_GT100,
TP_05,
TP_05_Time,
dc_05,
dc_gt100_05,
std_less,
std_grt,
tms_string,
pkt_string,
d_packet_gt100_time_str,
d_packet_gt100_str,
],
index=df.columns,
),
ignore_index=True,
)
df.to_csv(
(
pathlib.Path(experiment_result["experiment_folder"]) / "qos.csv"
).as_posix(),
index=False,
header=True,
)
merged_columns = pd.merge(godash_result, df, on="Arr_time", how="left")
merged_columns.to_csv(
(pathlib.Path(experiment_result["experiment_folder"]) / "all.csv").as_posix(),
index=False,
header=True,
)
def process_pcap(experiment_result: "ExperimentResult"):
if experiment_result["experiment"]["server_protocol"] == "quic":
process_pcap_quic(experiment_result)
else:
process_pcap_tcp(experiment_result)
def merge_all_experiments_into_csv(experiments_root: str):
experiments_all = set()
experiments_results = set()
for path in Path(experiments_root).rglob("all.csv"):
experiments_all.add(os.path.dirname(path))
for path in Path(experiments_root).rglob("result.json"):
experiments_results.add(os.path.dirname(path))
valid_experiments = experiments_all.intersection(experiments_results)
all_results = pd.DataFrame()
for path in valid_experiments:
with open(os.path.join(path, "result.json"), "r") as f:
experiment_result: "ExperimentResult" = json.load(f)
result: pd.DataFrame = pd.read_csv(os.path.join(path, "all.csv")) # type: ignore
result["mode"] = experiment_result["experiment"]["mode"]
result["id"] = experiment_result["experiment"]["id"]
result["server_protocol"] = experiment_result["experiment"]["server_protocol"]
result["server_type"] = experiment_result["experiment"]["server_type"]
result["video_name"] = experiment_result["experiment"]["mpd_path"].split("/")[3]
all_results = pd.concat([all_results, result], ignore_index=True)
all_results.to_csv(
os.path.join("/home/steckhelena", "all_results.csv"), index=False, header=True
)
def cleanup_pcap(experiment_result: "ExperimentResult"):
os.remove(experiment_result["experiment_host_pcap_path"])
if __name__ == "__main__":
merge_all_experiments_into_csv("experiment_results")