def compareInviteNeighbor():
s = Simulator()
random.seed(125)
s.setupSimulation(strategy="neighbors",
workMeasurement="one",
numNodes=1000,
numTasks=100000,
churnRate=0)
loads1, medians1, means1, maxs1, devs1 = s.simulateLoad()
random.seed(125)
s = Simulator()
s.setupSimulation(strategy="invite",
workMeasurement="one",
numNodes=1000,
numTasks=100000,
churnRate=0)
loads2 = s.simulateLoad()[0]
for i in range(0, len(loads1), 5):
x1 = loads1[i]
x2 = loads2[i]
colors = ["k", "w"]
labels = ["Neighbors", "Invitation"]
plt.hist([x1, x2], 25, normed=1, color=colors, label=labels)
plt.legend(loc=0)
plt.title('Invitation vs Smart Neighbors at Tick ' + str(i))
plt.xlabel('Tasks Per Node')
plt.ylabel('Fraction of the Network')
#plt.ylim(0, 0.05)
plt.show()
def compareChurnInjection():
s = Simulator()
random.seed(125)
s.setupSimulation(strategy="churn",
workMeasurement="one",
numNodes=1000,
numTasks=100000,
churnRate=0.01)
loads1, medians1, means1, maxs1, devs1 = s.simulateLoad()
random.seed(125)
s = Simulator()
s.setupSimulation(strategy='randomInjection',
workMeasurement="one",
numNodes=1000,
numTasks=100000,
churnRate=0)
loads2 = s.simulateLoad()[0]
for i in range(0, len(loads1), 5):
x1 = loads1[i]
x2 = loads2[i]
colors = ["r", "b"]
labels = ["Churn", "Random Injection"]
plt.hist([x1, x2], 25, normed=1, color=colors, label=labels)
plt.legend(loc=0)
plt.title('Churn vs Random Injection at Tick ' + str(i))
plt.xlabel('Tasks Per Node')
plt.ylabel('Fraction of the Network')
#plt.ylim(0, 0.05)
plt.show()
def compareInjectionStable():
s = Simulator()
random.seed(125)
s.setupSimulation(strategy="churn",
homogeneity="equal",
workMeasurement="one",
numNodes=1000,
numTasks=100000,
churnRate=0)
loads1, medians1, means1, maxs1, devs1 = s.simulateLoad()
random.seed(125)
s = Simulator()
s.setupSimulation(strategy="randomInjection",
homogeneity="perStrength",
workMeasurement="one",
numNodes=1000,
numTasks=100000,
churnRate=0)
loads2 = s.simulateLoad()[0]
for i in range(0, len(loads1), 5):
x1 = loads1[i]
x2 = loads2[i]
colors = ["k", "w"]
labels = ["No Strategy", "Random Injection"]
plt.hist([x1, x2], 25, normed=1, color=colors, label=labels)
plt.legend(loc=0)
plt.title('Random Injection in a Heterogeneous Network at Tick ' +
str(i))
plt.xlabel('Tasks Per Node')
plt.ylabel('Fraction of the Network')
#plt.ylim(0, 0.05)
plt.show()
def testChurnSteps():
s = Simulator()
s.setupSimulation(strategy='churn',
workMeasurement="one",
numNodes=1000,
numTask=100000,
churnRate=0.001)
loads, medians, means, maxs, devs = s.simulateLoad()
print(medians)
print(devs)
def testInjectionSteps():
s = Simulator()
s.setupSimulation(strategy = 'randomInjections',workMeasurement="one", numNodes=1000, numTasks=100000)
loads, medians, means, maxs, devs = s.simulateLoad()
for i in range(0,len(loads), 5):
x = loads[i]
plt.hist(x, 100, normed =1 )
plt.xlabel('Tasks Per Node')
plt.ylabel('Probability')
plt.axvline(medians[i], color='r', linestyle='--')
plt.axvline(means[i], color='k', linestyle='--')
#plt.ylim(0, 0.05)
plt.show()
def compareChurnStable():
s = Simulator()
random.seed(125)
s.setupSimulation(strategy= "churn", workMeasurement= "one", numNodes= 1000, numTasks = 100000, churnRate =0)
loads1, medians1, means1, maxs1, devs1 = s.simulateLoad()
random.seed(125)
s=Simulator()
s.setupSimulation(strategy= "churn", workMeasurement= "one", numNodes= 1000, numTasks = 100000, churnRate =0.1)
loads2 = s.simulateLoad()[0]
for i in range(0,len(loads1), 5):
x1= loads1[i]
x2 =loads2[i]
colors = ["k", "w"]
labels = ["No Strategy","Churn"]
plt.hist([x1,x2], 25, normed =1, color=colors, label=labels)
plt.legend(loc=0)
plt.title('Churn vs No Strategy at Tick ' + str(i))
plt.xlabel('Tasks Per Node')
plt.ylabel('Fraction of the Network')
#plt.ylim(0, 0.05)
plt.show()
def testInjectionSteps():
s = Simulator()
s.setupSimulation(strategy='randomInjections',
workMeasurement="one",
numNodes=1000,
numTasks=100000)
loads, medians, means, maxs, devs = s.simulateLoad()
for i in range(0, len(loads), 5):
x = loads[i]
plt.hist(x, 100, normed=1)
plt.xlabel('Tasks Per Node')
plt.ylabel('Probability')
plt.axvline(medians[i], color='r', linestyle='--')
plt.axvline(means[i], color='k', linestyle='--')
#plt.ylim(0, 0.05)
plt.show()
def testChurnSteps():
s = Simulator()
s.setupSimulation(strategy="churn", workMeasurement="one", numNodes=1000, numTask=100000, churnRate=0.001)
loads, medians, means, maxs, devs = s.simulateLoad()
print(medians)
print(devs)