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import math
import matplotlib.pyplot as plt
#%%
# each tuple contains a mix of read workload and read workload
xs = [(18, 12), (18, 24), (10, 50), (50, 10)]
#%%
def model(s_w, s_r, c, rf, f):
return math.ceil(s_w * rf / c + s_r * rf / (rf - f) / c)
#%%
from matplotlib.ticker import MaxNLocator
fig, ax = plt.subplots() # Create a figure containing a single axes.
fig.set_size_inches(8, 5)
ax.set_xlabel('RF')
ax.set_ylabel('N')
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
for s_w, s_r in xs:
px = []
py = []
for rf in range(1, 10):
px.append(rf)
py.append(model(s_w, s_r, 11, rf, 0))
ax.plot(px, py, '-o', label="$\Sigma_w = {} \quad \Sigma_r = {}$)".format(s_w, s_r))
ax.legend()
plt.savefig("sharding_vs_replication_no_f.svg")
#%%
from matplotlib.ticker import MaxNLocator
fig, ax = plt.subplots() # Create a figure containing a single axes.
fig.set_size_inches(8, 5)
ax.set_xlabel('RF')
ax.set_ylabel('N')
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
f = 1
for s_w, s_r in xs:
px = []
py = []
for rf in range(f + 1, 10):
px.append(rf)
py.append(model(s_w, s_r, 11, rf, f))
ax.plot(px, py, '-o', label="$\Sigma_w = {} \quad \Sigma_r = {} \quad F = {}$)".format(s_w, s_r, f))
ax.legend()
plt.savefig("sharding_vs_replication_f_1.svg")
#%%
from matplotlib.ticker import MaxNLocator
fig, ax = plt.subplots() # Create a figure containing a single axes.
fig.set_size_inches(8, 5)
ax.set_xlabel('RF')
ax.set_ylabel('N')
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
f = 3
for s_w, s_r in xs:
px = []
py = []
for rf in range(f + 1, 10):
px.append(rf)
py.append(model(s_w, s_r, 11, rf, f))
ax.plot(px, py, '-o', label="$\Sigma_w = {} \quad \Sigma_r = {} \quad F = {}$)".format(s_w, s_r, f))
ax.legend()
plt.savefig("sharding_vs_replication_f_3.svg")
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