def plot_relative(src_gst_latency, src_src_latency, src_pos):
triu = np.triu_indices(src_gst_latency.shape[0], 1)
ixp_routed = np.around(src_gst_latency[triu], 6)
city_gst = np.around(src_src_latency[triu], 6)
pairwise_src = DistanceMetric.pairwise(
DistanceMetric.get_metric("haversine"), np.deg2rad(src_pos),
np.deg2rad(src_pos))
pairwise_src = pairwise_src * EARTH_RADIUS
percent = (ixp_routed - city_gst) / city_gst * 100
pairwise = pairwise_src[triu]
vals, avg_c, min_c, max_c, percent = vector_map_statistics(
pairwise, percent, 100, [25, 75])
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
cur_color = colors[0]
plt.figure(figsize=(8, 6))
gs1 = gridspec.GridSpec(2, 1)
gs1.update(wspace=0.01, hspace=0.11) # set the spacing between axes.
axes = [plt.subplot(gs1[0]), plt.subplot(gs1[1])]
axes[0].axhline(0, c='grey', linewidth=0.5)
axes[0].semilogy(vals, avg_c, label="Average", c=cur_color, linewidth=3)
axes[0].semilogy(vals,
percent[25],
"--",
label="Quartiles",
c=cur_color,
linewidth=3)
axes[0].semilogy(vals, percent[75], "--", c=cur_color, linewidth=3)
axes[0].plot(vals,
max_c,
":",
linewidth=3,
label="Min-max variability",
c=cur_color)
axes[0].set_ylim(90, 1000)
axes[0].set_ylabel("log-scale")
axes[0].set_xticks([])
axes[0].legend()
axes[1].axhline(0, c='grey', linewidth=0.5)
axes[1].plot(vals,
avg_c,
label="Average city-city",
c=cur_color,
linewidth=3)
axes[1].plot(vals,
percent[25],
"--",
label="Quartiles",
c=cur_color,
linewidth=3)
axes[1].plot(vals, percent[75], "--", c=cur_color, linewidth=3)
axes[1].plot(vals,
max_c,
":",
linewidth=3,
label="Min-max variability",
c=cur_color)
axes[1].plot(vals,
min_c,
":",
linewidth=3,
label="Min-max variability",
c=cur_color)
axes[1].set_xlabel("SRC-DST great-circle distance (km)")
axes[1].set_ylabel("Loss IXP deployment (%)")
# axes[1].set_ylabel("Latency increase IXP deployment (%)")
axes[1].set_ylim(-50, 90)
plt.savefig("figures/percent-ixp-loss.png")
def plot_absolute(src_gst_latency, src_src_latency, src_pos):
triu = np.triu_indices(src_gst_latency.shape[0], 1)
ixp_routed = np.around(src_gst_latency[triu], 6)
city_gst = np.around(src_src_latency[triu], 6)
SCALING = 1e3
plt.figure(figsize=(8, 6))
pairwise_src = DistanceMetric.pairwise(
DistanceMetric.get_metric("haversine"), np.deg2rad(src_pos),
np.deg2rad(src_pos))
pairwise_src = pairwise_src * EARTH_RADIUS
pairwise = pairwise_src[triu]
vals, avg_c, min_c, max_c, _ = vector_map_statistics(
pairwise, city_gst, 10)
avg_c = np.asarray(avg_c) * SCALING
min_c = np.asarray(min_c) * SCALING
max_c = np.asarray(max_c) * SCALING
plt.plot(vals, avg_c, label="Average city-city", linewidth=3)
plt.xlabel("SRC-DST distance (km)")
plt.ylabel("Latency (s)")
plt.legend(loc=2)
pairwise = pairwise_src[triu]
vals, avg_g, min_g, max_g, _ = vector_map_statistics(
pairwise, ixp_routed, 10)
avg_g = np.asarray(avg_g) * SCALING
min_g = np.asarray(min_g) * SCALING
max_g = np.asarray(max_g) * SCALING
plt.plot(vals, avg_g, label="Average IXP-city", linewidth=3)
plt.plot(vals,
vals / LIGHT_IN_FIBER * SCALING,
':',
linewidth=3,
label="Great-circle in fiber")
plt.plot(vals,
vals / LIGHT_IN_VACUUM * SCALING,
'--',
label="Great-circle in vacuum",
linewidth=3)
plt.plot(vals,
vals * FIBER_PATH_STRETCH / LIGHT_IN_FIBER * SCALING,
'-.',
label="Path-stretch in fiber",
linewidth=3)
plt.ylim(0, 150)
plt.xlim(0, np.max(vals))
plt.xlabel("SRC-DST great-circle distance (km)")
plt.ylabel("One-way latency (s)")
plt.legend(loc=9, ncol=2, mode="expand")
# Save figures
# plt.savefig("figures/latency-distance.pdf")
plt.savefig("figures/latency-distance.png")
