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Update plotOverleaf.py
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@UmakantKulkarni
UmakantKulkarni committed Dec 3, 2021
1 parent 162fdbf commit 31abd57
Showing 1 changed file with 92 additions and 39 deletions.
131 changes: 92 additions & 39 deletions plotOverleaf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,25 +19,39 @@
my_file_name = os.path.basename(__file__)
path = Path("{}/{}".format(dir_path, my_file_name)).parent.parent

CREATE_COMPARISON_CSVS = 1
FIGURE3 = 1
FIGURE4 = 1
FIGURE5 = 1
FIGURE6 = 1
FIGURE7 = 1

dict_map = {"Fully-Stateful": {"amfTimeTaken": [], "plotname": "Stateful", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [i for i in range(1, 11)], "amfQueueLength": 0},
"Fully-Procedural-Stateless": {"amfTimeTaken": [], "plotname": "Procedural Stateless", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [], "amfQueueLength": 0},
"Fully-Transactional-Stateless": {"amfTimeTaken": [], "plotname": "Transactional Stateless", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [1, 3, 4, 6, 8], "amfQueueLength": 0},
"Nonblocking-Api-Enabled": {"amfTimeTaken": [], "plotname": "Non-Blocking", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [1, 2, 3, 4, 7, 8, 9, 10], "amfQueueLength": 0},
"N1n2-Amf-Update-Api-Disabled": {"amfTimeTaken": [], "plotname": "Delete-Create API", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [], "amfQueueLength": 0},
"Amf-Smf-Share-Udsf": {"amfTimeTaken": [], "plotname": "AMF-SMF Share Database", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [1, 2, 4, 5, 7, 8, 9], "amfQueueLength": 0},
"All-NFs-Share-Udsf": {"amfTimeTaken": [], "plotname": "All NFs Share Database", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [], "amfQueueLength": 0}
CREATE_COMPARISON_CSVS = 0
MIN_MAX_COMPARISON = 1
FIGURE3 = 0
FIGURE4 = 0
FIGURE5 = 0
FIGURE6 = 0
FIGURE7 = 0
FIGURE8 = 0

dict_map = {"Fully-Stateful": {"amfTimeTaken": [], "plotname": "Stateful", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [i for i in range(1, 11)], "amfQueueLength": 0, "amfMinTime": [], "amfMaxTime": [], "dbMinTime": [], "dbMaxTime": []},
"Fully-Procedural-Stateless": {"amfTimeTaken": [], "plotname": "Procedural Stateless", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [], "amfQueueLength": 0, "amfMinTime": [], "amfMaxTime": [], "dbMinTime": [], "dbMaxTime": []},
"Fully-Transactional-Stateless": {"amfTimeTaken": [], "plotname": "Transactional Stateless", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [1, 3, 4, 6, 8], "amfQueueLength": 0, "amfMinTime": [], "amfMaxTime": [], "dbMinTime": [], "dbMaxTime": []},
"Nonblocking-Api-Enabled": {"amfTimeTaken": [], "plotname": "Non-Blocking", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [1, 2, 3, 4, 7, 8, 9, 10], "amfQueueLength": 0, "amfMinTime": [], "amfMaxTime": [], "dbMinTime": [], "dbMaxTime": []},
"N1n2-Amf-Update-Api-Disabled": {"amfTimeTaken": [], "plotname": "Delete-Create API", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [], "amfQueueLength": 0, "amfMinTime": [], "amfMaxTime": [], "dbMinTime": [], "dbMaxTime": []},
"Amf-Smf-Share-Udsf": {"amfTimeTaken": [], "plotname": "AMF-SMF Share Database", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [1, 2, 4, 5, 7, 8, 9], "amfQueueLength": 0, "amfMinTime": [], "amfMaxTime": [], "dbMinTime": [], "dbMaxTime": []},
"All-NFs-Share-Udsf": {"amfTimeTaken": [], "plotname": "All NFs Share Database", "amfDbReadTime": [], "amfDbWriteTime": [], "amfDbTotalTime": [], "valied_1000_runs": [], "amfQueueLength": 0, "amfMinTime": [], "amfMaxTime": [], "dbMinTime": [], "dbMaxTime": []}
}

dict_map_keys = list(dict_map.keys()).copy()
session_list = [i*100 for i in range(1,11)]

def calc_min_max (i,j,param="amfTime"):
if param == "amfTime":
p1 = "amfMaxTime"
p2 = "amfMinTime"
elif param == "dbTime":
p1 = "dbMaxTime"
p2 = "dbMinTime"
a = dict_map[dict_map_keys[j]][p1]
b = dict_map[dict_map_keys[i]][p2]
max_diff = np.mean([(a_i - b_i)*(100/b_i) for a_i, b_i in zip(a, b)])
print("Max time difference between {} and {} is {}".format(dict_map_keys[j], dict_map_keys[i], max_diff))

def plot_q_cpu_instance(ax,df_t,df_q,NF,label=False):

df_t = df_t[['Time (ms)','CPU-Usage']]
Expand All @@ -46,8 +60,8 @@ def plot_q_cpu_instance(ax,df_t,df_q,NF,label=False):

df_t['CPU-Usage'] = df_t['CPU-Usage'].astype(int)
from matplotlib.ticker import MaxNLocator
ax1 = df_t['CPU-Usage'].plot(ax=ax,label='CPU',marker='x',style='#34495e')
ax1.set_ylabel('CPU (%)')
ax1 = df_t['CPU-Usage'].plot(ax=ax,label='CPU',marker='x',style='#34495e',ms=4)
ax1.set_ylabel('CPU (%)', fontsize=13)
ax1.yaxis.set_major_locator(MaxNLocator(integer=True,nbins=4))
# ax1.yaxis.set_major_formatter(PercentFormatter(100)) # percentage using 1 for 100%

Expand All @@ -56,19 +70,21 @@ def plot_q_cpu_instance(ax,df_t,df_q,NF,label=False):
#print(df_q.head())

df_q['Q Length'].plot(secondary_y=True,style="#2ecc71",ax=ax)
ax1.right_ax.set_ylabel('Q Length')
ax1.right_ax.set_ylabel('Q Length', fontsize=13)
#sb.set_ylim(5,16)
if NF == "UPF":
#ax1.right_ax.set_ylim(-0.1,1)
ck = [0,1]
ax1.right_ax.set_yticks(ck)
ax1.right_ax.set_yticks(ck, fontsize=13)
#ax1.right_ax.set_yticks(np.arange(min(ck), max(ck)+10, 10))

if label == True:
lines = ax.get_lines() + ax.right_ax.get_lines()
ax.legend(lines, [l.get_label() for l in lines], loc='lower center',frameon=True,ncol=2)
ax.legend(lines, [l.get_label() for l in lines], loc='upper left',frameon=True,ncol=1, fontsize=11, bbox_to_anchor=(0,1.035))

ax.set_title(NF, y=1.0, pad=-14, x=.051)
#ax.set_title(NF, y=1.0, pad=-14, x=.051)
ax.set_title(NF, y=1.02, pad=-14, x=.5835, fontsize=13)
#ax.set_title(NF, y=1.02, pad=-14, x=.5, fontsize=13)


def q_cpu_time_series():
Expand Down Expand Up @@ -143,6 +159,8 @@ def main():
df[col].values[this_mean_index] = col_mean
if col == "amfTimeTaken":
dict_map[folder_name]["amfTimeTaken"].append(col_mean)
dict_map[folder_name]["amfMinTime"].append(min(col_values))
dict_map[folder_name]["amfMaxTime"].append(max(col_values))
df_new = pd.DataFrame()
df_new["amfTimeTaken"] = col_values
df_new["Config"] = dict_map[folder_name]["plotname"]
Expand All @@ -159,6 +177,8 @@ def main():
df_new["amfDbWriteTime"] = col_values
elif col == "amfDbTotalTime":
dict_map[folder_name]["amfDbTotalTime"].append(col_mean)
dict_map[folder_name]["dbMinTime"].append(min(col_values))
dict_map[folder_name]["dbMaxTime"].append(max(col_values))
df_new["amfDbTotalTime"] = col_values
df_all = df_all.append(df_new, ignore_index=True)
prev_mean_index = this_mean_index+1
Expand All @@ -185,52 +205,66 @@ def main():
df_queue_cpu["Config"] = valid_cpuq_folders
df_queue_cpu["Value"] = cpu_q_values

if MIN_MAX_COMPARISON:
calc_min_max(0,1)
calc_min_max(0,2)
calc_min_max(3,2)
calc_min_max(4,2)
calc_min_max(5,2)
calc_min_max(6,2)
calc_min_max(4,2,"dbTime")


if CREATE_COMPARISON_CSVS:

df = pd.DataFrame({"Number-of-Sessions": session_list, "Time-{}".format(dict_map_keys[0]): dict_map[dict_map_keys[0]]["amfTimeTaken"], "Time-{}".format(dict_map_keys[2]): dict_map[dict_map_keys[2]]["amfTimeTaken"], "Change %": [0]*10})
df['Change %'] = (df["Time-{}".format(dict_map_keys[2])] - df["Time-{}".format(dict_map_keys[0])])*(100/(df["Time-{}".format(dict_map_keys[0])]))
df1 = df.append({"Number-of-Sessions": "Average", "Time-{}".format(dict_map_keys[0]): "-", "Time-{}".format(dict_map_keys[2]): "-", 'Change %': np.mean(df["Change %"].values)}, ignore_index=True)
df1.to_csv("Stateful-Vs-Stateless-TimeTaken.csv", index=False)
df1.to_csv("Stateful-Vs-Stateless-Avg-Time-Taken.csv", index=False)

df = pd.DataFrame({"Number-of-Sessions": session_list, "Time-{}".format(dict_map_keys[0]): dict_map[dict_map_keys[0]]["amfTimeTaken"], "Time-{}".format(dict_map_keys[1]): dict_map[dict_map_keys[1]]["amfTimeTaken"], "Change %": [0]*10})
df['Change %'] = (df["Time-{}".format(dict_map_keys[1])] - df["Time-{}".format(dict_map_keys[0])])*(100/(df["Time-{}".format(dict_map_keys[0])]))
df1 = df.append({"Number-of-Sessions": "Average", "Time-{}".format(dict_map_keys[0]): "-", "Time-{}".format(dict_map_keys[1]): "-", 'Change %': np.mean(df["Change %"].values)}, ignore_index=True)
df1.to_csv("Stateful-Vs-Procedural-Avg-Time-Taken.csv", index=False)

df = pd.DataFrame({"Number-of-Sessions": session_list, "Time-{}".format(dict_map_keys[1]): dict_map[dict_map_keys[1]]["amfTimeTaken"], "Time-{}".format(dict_map_keys[2]): dict_map[dict_map_keys[2]]["amfTimeTaken"], "Change %": [0]*10})
df['Change %'] = (df["Time-{}".format(dict_map_keys[2])] - df["Time-{}".format(dict_map_keys[1])])*(100/(df["Time-{}".format(dict_map_keys[1])]))
df2 = df.append({"Number-of-Sessions": "Average", "Time-{}".format(dict_map_keys[1]): "-", "Time-{}".format(dict_map_keys[2]): "-", 'Change %': np.mean(df["Change %"].values)}, ignore_index=True)
df2.to_csv("Procedural-Vs-Transactional-Time-Taken.csv", index=False)
df2.to_csv("Procedural-Vs-Transactional-Avg-Time-Taken.csv", index=False)

df = pd.DataFrame({"Number-of-Sessions": session_list, "Time-{}".format(dict_map_keys[3]): dict_map[dict_map_keys[3]]["amfTimeTaken"], "Time-Blocking-Api-Enabled": dict_map[dict_map_keys[2]]["amfTimeTaken"], "Change %": [0]*10})
df['Change %'] = (df["Time-Blocking-Api-Enabled"] - df["Time-{}".format(dict_map_keys[3])])*(100/(df["Time-{}".format(dict_map_keys[3])]))
df3 = df.append({"Number-of-Sessions": "Average", "Time-{}".format(dict_map_keys[3]): "-", "Time-Blocking-Api-Enabled": "-", 'Change %': np.mean(df["Change %"].values)}, ignore_index=True)
df3.to_csv("Blocking-Vs-Nonblocking-Time-Taken.csv", index=False)
df3.to_csv("Blocking-Vs-Nonblocking-Avg-Time-Taken.csv", index=False)

df = pd.DataFrame({"Number-of-Sessions": session_list, "Time-Delete-Create-Api-Enabled": dict_map[dict_map_keys[4]]["amfTimeTaken"], "Time-Update-Api-Enabled": dict_map[dict_map_keys[2]]["amfTimeTaken"], "Change %": [0]*10})
df['Change %'] = (df["Time-Update-Api-Enabled"] - df["Time-Delete-Create-Api-Enabled"])*(100/(df["Time-Delete-Create-Api-Enabled"]))
df4 = df.append({"Number-of-Sessions": "Average", "Time-Delete-Create-Api-Enabled": "-", "Time-Update-Api-Enabled": "-", 'Change %': np.mean(df["Change %"].values)}, ignore_index=True)
df4.to_csv("Update-Vs-Delete-Create-Time-Taken.csv", index=False)
df4.to_csv("Update-Vs-Delete-Create-Avg-Time-Taken.csv", index=False)

df = pd.DataFrame({"Number-of-Sessions": session_list, "Time-{}".format(dict_map_keys[5]): dict_map[dict_map_keys[5]]["amfTimeTaken"], "Time-{}".format(dict_map_keys[6]): dict_map[dict_map_keys[6]]["amfTimeTaken"], "Time-Not-Sharing-Udsf": dict_map[dict_map_keys[2]]["amfTimeTaken"], "Change - AmfSmf Vs Unshared": [0]*10, "Change - AllShared Vs Unshared": [0]*10})
df['Change - AmfSmf Vs Unshared'] = (df["Time-Not-Sharing-Udsf"] - df["Time-{}".format(dict_map_keys[5])])*(100/(df["Time-{}".format(dict_map_keys[5])]))
df['Change - AllShared Vs Unshared'] = (df["Time-Not-Sharing-Udsf"] - df["Time-{}".format(dict_map_keys[6])])*(100/(df["Time-{}".format(dict_map_keys[6])]))
df5 = df.append({"Number-of-Sessions": "Average", "Time-{}".format(dict_map_keys[5]): "-", "Time-{}".format(dict_map_keys[6]): "-", "Time-Not-Sharing-Udsf": "-", 'Change - AmfSmf Vs Unshared': np.mean(df["Change - AmfSmf Vs Unshared"].values), 'Change - AllShared Vs Unshared': np.mean(df["Change - AllShared Vs Unshared"].values)}, ignore_index=True)
df5.to_csv("Shared-Vs-Unshared-Udsf-Time-Taken.csv", index=False)
df5.to_csv("Shared-Vs-Unshared-Udsf-Avg-Time-Taken.csv", index=False)

df = pd.DataFrame({"Number-of-Sessions": session_list, "Time-Delete-Create-Api-Enabled": dict_map[dict_map_keys[4]]["amfDbReadTime"], "Time-Update-Api-Enabled": dict_map[dict_map_keys[2]]["amfDbReadTime"], "Change %": [0]*len(dict_map[dict_map_keys[2]]["amfDbReadTime"])})
df['Change %'] = (df["Time-Update-Api-Enabled"] - df["Time-Delete-Create-Api-Enabled"])*(100/(df["Time-Delete-Create-Api-Enabled"]))
df6 = df.append({"Number-of-Sessions": "Average", "Time-Delete-Create-Api-Enabled": "-", "Time-Update-Api-Enabled": "-", 'Change %': np.mean(df["Change %"].values)}, ignore_index=True)
df6.to_csv("Update-Vs-Delete-Create-Db-Read-Time-Taken.csv", index=False)
df6.to_csv("Update-Vs-Delete-Create-Db-Read-Avg-Time-Taken.csv", index=False)

df = pd.DataFrame({"Number-of-Sessions": session_list, "Time-Delete-Create-Api-Enabled": dict_map[dict_map_keys[4]]["amfDbWriteTime"], "Time-Update-Api-Enabled": dict_map[dict_map_keys[2]]["amfDbWriteTime"], "Change %": [0]*10})
df['Change %'] = (df["Time-Update-Api-Enabled"] - df["Time-Delete-Create-Api-Enabled"])*(100/(df["Time-Delete-Create-Api-Enabled"]))
df6 = df.append({"Number-of-Sessions": "Average", "Time-Delete-Create-Api-Enabled": "-", "Time-Update-Api-Enabled": "-", 'Change %': np.mean(df["Change %"].values)}, ignore_index=True)
df6.to_csv("Update-Vs-Delete-Create-Db-Write-Time-Taken.csv", index=False)
df6.to_csv("Update-Vs-Delete-Create-Db-Write-Avg-Time-Taken.csv", index=False)

df = pd.DataFrame({"Number-of-Sessions": session_list, "Time-Delete-Create-Api-Enabled": dict_map[dict_map_keys[4]]["amfDbTotalTime"], "Time-Update-Api-Enabled": dict_map[dict_map_keys[2]]["amfDbTotalTime"], "Change %": [0]*10})
df['Change %'] = (df["Time-Update-Api-Enabled"] - df["Time-Delete-Create-Api-Enabled"])*(100/(df["Time-Delete-Create-Api-Enabled"]))
df6 = df.append({"Number-of-Sessions": "Average", "Time-Delete-Create-Api-Enabled": "-", "Time-Update-Api-Enabled": "-", 'Change %': np.mean(df["Change %"].values)}, ignore_index=True)
df6.to_csv("Update-Vs-Delete-Create-Db-Total-Time-Taken.csv", index=False)
df6.to_csv("Update-Vs-Delete-Create-Db-Total-Avg-Time-Taken.csv", index=False)


df_plot = df_all[df_all['Rate'] > 500]
df_plot = df_all[df_all['Rate'] > 500].copy()

if FIGURE3:
config_filter = ['Fully-Stateful','Fully-Procedural-Stateless', 'Fully-Transactional-Stateless']
Expand All @@ -244,7 +278,7 @@ def main():
sb.legend_.set_title(None)
plt.tight_layout()
plt.legend(loc="best")
plt.savefig('figure3.pdf', bbox_inches='tight', pad_inches=0)
plt.savefig('figure3.pdf', bbox_inches='tight')
plt.show()
plt.close()

Expand All @@ -260,7 +294,7 @@ def main():
sb.legend_.set_title(None)
plt.tight_layout()
plt.legend(loc="best")
plt.savefig('figure4.pdf', bbox_inches='tight', pad_inches=0)
plt.savefig('figure4.pdf', bbox_inches='tight')
plt.show()
plt.close()

Expand All @@ -270,7 +304,7 @@ def main():
flatui = ["#3498db", "#2ecc71"]
sns.set_palette(flatui)
sb = sns.barplot(data=df_plot, x='Rate', y='amfDbTotalTime', hue='Config', palette=flatui, hue_order=order_list)
sb.set_ylim(550,1000)
sb.set_ylim(550,1010)
l1 = mpatches.Patch(color=flatui[0], label='Delete-Create API')
l2 = mpatches.Patch(color=flatui[1], label='Update API')
plt.ylabel('Time (ms)')
Expand All @@ -282,6 +316,27 @@ def main():
plt.show()
plt.close()

if FIGURE8:
config_filter = [dict_map_keys[4], dict_map_keys[2]]
order_list = [dict_map[x]["plotname"] for x in config_filter]
order_list[1] = "Update API"
config_list = [order_list[0]]*5
config_list.extend([order_list[1]]*5)
df_mongo = pd.DataFrame()
df_mongo["Config"] = config_list
amfRdTime = dict_map[config_filter[0]]["amfDbReadTime"][5:].copy()
amfRdTime.extend(dict_map[config_filter[1]]["amfDbReadTime"][5:].copy())
amfWrTime = dict_map[config_filter[0]]["amfDbWriteTime"][5:].copy()
amfWrTime.extend(dict_map[config_filter[1]]["amfDbWriteTime"][5:].copy())
df_mongo["amfDbReadTime"] = amfRdTime
df_mongo["amfDbWriteTime"] = amfWrTime
df_mongo["Rate"] = session_list[5:]*2
#ax = df_mongo.plot(x="Rate", y="amfDbWriteTime", kind="bar")
#df_mongo.plot(x="Rate", y="amfDbReadTime", kind="bar", ax=ax, color="C2")
#df_mongo[["Rate", "amfDbWriteTime", "amfDbReadTime"]].plot(x="Rate", kind="bar")
df_mongo.set_index('Rate').plot(kind='bar', stacked=True, y = ["amfDbReadTime","amfDbWriteTime"], color=['steelblue', 'red'])
plt.show()

if FIGURE6:

def get_plt_name(x):
Expand All @@ -298,25 +353,23 @@ def get_plt_name(x):

fig, ax1 = plt.subplots()
flatui = ["#34495e","#2ecc71"]
sns.set_context(context="paper",font_scale=1)
sb = sns.barplot(data=df_queue_cpu, x='Config', y='Value', hue='Type',palette=sns.color_palette(flatui),
ax=ax1,hue_order=['CPU','Q Length'],
order = ['Stateful','Transactional\nStateless','AMF-SMF\nShare DB','Non-Blocking'])
ax1.set_ylabel('CPU', fontsize=12)
ax1.set_ylabel('CPU', fontsize=14)
ax2 = ax1.twinx()
ax2.set_ylim(ax1.get_ylim())
ax1.yaxis.set_major_formatter(PercentFormatter(100)) # percentage using 1 for 100%
ax1.tick_params(labelsize=12)
ax2.tick_params(labelsize=12)
ax2.set_ylabel('Queue Length', fontsize=12)
ax1.tick_params(labelsize=13)
ax2.tick_params(labelsize=13)
ax2.set_ylabel('Queue Length', fontsize=14)
ax1.set_xlabel('')
sb.legend_.set_title(None)
plt.tight_layout()
plt.legend(loc="best")
plt.legend(loc="best", fontsize=1)
plt.savefig('figure6.pdf', bbox_inches='tight', pad_inches=0)
plt.show()
plt.close()
sns.set_context(context="paper",font_scale=1.6)

if FIGURE7:
q_cpu_time_series()
Expand Down

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