def _plot_intro(self):
data_list = []
nof_states = 0
for elem in self.data:
if "precision" in elem and elem["ctx"][0] == "Colt-intro":
data_list.append(elem["precision"])
elif "finished" in elem and elem["ctx"][0] == "Colt-intro":
nof_states = elem["finished"]["num_explored"]
df_ours = pd.DataFrame(data_list, columns=["precision"])
link_failure_prob = 0.001
list_imprecision = np.logspace(-5, 0, num=100)
list_num_links = [191]
data_bf = []
data_hoeffding = []
hoeffding_confidence = 0.95
for imprecision in list_imprecision:
samples_hoeffding = hoeffding_samples_for_target_precision(
1 - imprecision, hoeffding_confidence)
data_hoeffding.append([imprecision, samples_hoeffding])
for num_links in list_num_links:
states_brute_force = bf_states_for_target_precision(
1 - imprecision, num_links, link_failure_prob)
data_bf.append([imprecision, num_links, states_brute_force])
df_bf = pd.DataFrame(data_bf,
columns=["imprecision", "num_links", "states"])
df_hoeffding = pd.DataFrame(data_hoeffding,
columns=["imprecision", "samples"])
sph.new_figure(13, 7)
plt.axhline(y=1E-4,
linewidth=1,
label="four 9s guarantee",
linestyle="dotted",
color="#A0A0A0")
plt.plot("samples",
"imprecision",
data=df_hoeffding,
label="random sampling",
linestyle="dashed")
for num_links in list_num_links:
plt.plot("states",
"imprecision",
data=df_bf[df_bf.num_links == num_links],
label="partial exploration".format(num_links),
linestyle="dotted")
plt.plot(range(0, nof_states),
"precision",
data=df_ours,
label="\\textbf{this work}")
plt.xlabel("\# states")
plt.ylabel("imprecision")
plt.xlim([1, 1E11])
plt.loglog()
plt.legend()
sph.savefig(os.path.join(self.output_dir, "plot_intro.pdf"))
def _plot_5(self):
data_list = []
for elem in self.data:
if "fraction_hot" in elem and elem["ctx"][0].endswith("-default"):
data_list.append(elem["fraction_hot"])
sph.new_figure(5.5, 5)
sph.cdf.create(data_list, '-r')
plt.xlabel("fraction of hot edges")
sph.savefig(os.path.join(self.output_dir, "plot_5.pdf"))
def _plot_2(self):
data_list = []
for elem in self.data:
if "time-explore" in elem and elem["ctx"][0].startswith(
"Uninett2010-br"):
res = re.match(r'^Uninett2010-br([0-9]*)', elem["ctx"][0])
data_list.append((int(res.group(1)), elem["ctx"][1],
elem["time-explore"] / 60.0))
df = pd.DataFrame(data_list, columns=["brs", "rep", "time_min"])
fig, ax = sph.new_figure(10, 6)
df.boxplot(column="time_min",
by="brs",
ax=ax,
grid=False,
flierprops=self.flierprops)
ax.grid(b=True, which='major', axis='y', color='w')
ax.set_axisbelow(True)
plt.xlabel("number of border routers")
plt.ylabel("time [min]")
plt.title("")
fig.suptitle("")
sph.savefig(os.path.join(self.output_dir, "plot_2a.pdf"))
data_list = []
for elem in self.data:
if "time-explore" in elem and elem["ctx"][0].startswith(
"Uninett2010-rr"):
res = re.match(r'^Uninett2010-rr([0-9]*)', elem["ctx"][0])
n_rrs = int(res.group(1))
if n_rrs == 0:
n_rrs = "0"
data_list.append(
(n_rrs, elem["ctx"][1], elem["time-explore"] / 60.0))
df = pd.DataFrame(data_list, columns=["rrs", "rep", "time_min"])
fig, ax = sph.new_figure(10, 6)
df.boxplot(column="time_min",
by="rrs",
ax=ax,
grid=False,
flierprops=self.flierprops,
positions=[2, 3, 4, 5, 6, 1])
ax.grid(b=True, which='major', axis='y', color='w')
ax.set_axisbelow(True)
plt.xlabel("number of route reflectors (0 = iBGP full mesh)")
plt.ylabel("time [min]")
plt.title("")
fig.suptitle("")
sph.savefig(os.path.join(self.output_dir, "plot_2b.pdf"))
def _plot_3(self):
data_list = []
for elem in self.data:
if "time-explore" in elem and elem["ctx"][0].startswith(
"AS-3549-Xcongest"):
res = re.match(r'^AS-3549-Xcongest-([0-9]*)', elem["ctx"][0])
data_list.append((int(res.group(1)), elem["ctx"][1],
elem["time-explore"] / 60.0))
df = pd.DataFrame(data_list, columns=["flows", "rep", "time_min"])
fig, ax = sph.new_figure(10, 6)
df.boxplot(column="time_min",
by="flows",
ax=ax,
grid=False,
flierprops=self.flierprops)
ax.grid(b=True, which='major', axis='y', color='w')
ax.set_axisbelow(True)
plt.xlabel("number of flows")
plt.ylabel("time [min]")
plt.ylim([0, 120])
plt.title("")
fig.suptitle("")
plt.gcf().text(0.27, 0.93, "timeout (2 h):", fontsize=12)
plt.gcf().text(0.54, 0.93, "1", fontsize=12)
plt.gcf().text(0.74, 0.93, "2", fontsize=12)
plt.gcf().text(0.84, 0.93, "3", fontsize=12)
sph.savefig(os.path.join(self.output_dir, "plot_3.pdf"))
def _plot_1bc(self):
data_list = []
nof_states = {}
for elem in self.data:
if "precision" in elem and elem["ctx"][0] == "Colt-trace":
data_list.append((elem["ctx"][1], elem["precision"]))
elif "finished" in elem and elem["ctx"][0] == "Colt-trace":
nof_states[elem["ctx"][1]] = elem["finished"]["num_explored"]
df = pd.DataFrame(data_list, columns=["rep", "precision"])
sph.new_figure(9, 6)
self._multi_trace_plot(df, nof_states)
sph.savefig(os.path.join(self.output_dir, "plot_1bi.pdf"))
time_data_list = []
for elem in self.data:
if "time-explore" in elem and elem["ctx"][0] == "isp-trace":
time_data_list.append(("ISP", elem["time-explore"]))
if len(time_data_list) == 0:
log.warning("skipping plot_1cii as ISP data is missing")
return
df = pd.DataFrame(time_data_list, columns=["net", "time"])
max_y = 120
log.info("Outliers:\n%s", str(df[df.time > max_y]))
nof_greater_max_y = df[df.time > max_y].count()["net"]
fig, ax = sph.new_figure(2, 6)
df.boxplot(column="time",
by="net",
ax=ax,
grid=False,
flierprops=self.flierprops)
ax.grid(b=True, which='major', axis='y', color='w')
ax.set_axisbelow(True)
plt.ylim([0, max_y])
plt.xlabel("")
plt.ylabel("time [s]")
plt.title("")
fig.suptitle("")
plt.gcf().text(0.4,
0.93,
"+ {}".format(nof_greater_max_y),
fontsize=12)
sph.savefig(os.path.join(self.output_dir, "plot_1cii.pdf"))
def _plot_1a(self):
data_list = []
prec_list = []
is_timeout = set()
max_time_explore = {}
for elem in self.data:
if "timeout_after_seconds" in elem and elem["ctx"][0].endswith(
"-default"):
is_timeout.add((self._get_topo_name(elem), elem["ctx"][1]))
elif "time-explore" in elem and elem["ctx"][0].endswith(
"-default"):
nof_links = self.nof_links_for_topology[self._get_topo_name(
elem)]
if self._get_topo_name(
elem) not in max_time_explore or max_time_explore[
self._get_topo_name(elem)] < elem["time-explore"]:
max_time_explore[self._get_topo_name(
elem)] = elem["time-explore"]
if nof_links <= 75:
range = "50--75"
elif nof_links <= 100:
range = "76--100"
elif nof_links <= 200:
range = "101--200"
else:
range = "$>$ 200"
timeout = (self._get_topo_name(elem),
elem["ctx"][1]) in is_timeout
data_list.append(
(elem["ctx"][0], nof_links, range, elem["ctx"][1],
elem["time-explore"], timeout))
elif "finished" in elem and elem["ctx"][0].endswith("-default"):
prec_list.append(
(elem["ctx"][0], elem["finished"]["precision"]))
df = pd.DataFrame(data_list,
columns=[
"experiment", "links", "range", "rep", "time",
"is_timeout"
])
df_prec = pd.DataFrame(prec_list, columns=["experiment", "precision"])
# count number of timeouts per topology
df_to = df[["experiment", "is_timeout"]].groupby("experiment").sum()
log.info("Number of timeouts:\n%s", str(df_to[df_to.is_timeout > 0]))
# compute the worst-case imprecision
log.info(
"Worst imprecision:\n%s",
str(df_prec[df_prec.precision > 1E-4].groupby("experiment").max()))
sph.new_figure(11, 5.5)
plt.axhline(60 * 60, c="gray", lw=1, label="1 h (timeout)")
df_max = df[["experiment", "time",
"links"]].groupby("experiment").max()
plt.plot("links",
"time",
"x",
data=df_max,
markersize=4,
mew=0.6,
label="maximum")
df_med = df[["experiment", "time",
"links"]].groupby("experiment").median()
plt.plot("links",
"time",
"+",
data=df_med,
markersize=4,
mew=0.6,
label="median")
plt.xlabel("links")
plt.ylabel("time [s]")
plt.legend(handletextpad=0.3)
plt.loglog()
sph.savefig(os.path.join(self.output_dir, "plot_1a.pdf"))