def run3(prefix):
parameters = System.Parameters(n_users=25,
n_CPUs=4,
easeInTime=20.0,
maxIters=10000,
bufferCapacity=800,
threadpoolSize=200,
quantum=0.5,
ctxxTime=0.01,
serviceTimeMean=2.0,
requestTimeoutMin=5.0,
requestTimeoutMax=15.0,
thinkTimeMean=12.0,
thinkTimeStdv=4.0,
retryThinkTimeMean=12.0,
retryThinkTimeStdv=4.0)
x_data = []
throughput_y_data = []
responseTime_y_data = []
responseTime_ci = []
utilization_y_data = []
goodput_y_data = []
badput_y_data = []
dropRate_y_data = []
droppedFraction_y_data = []
timedOutFraction_y_data = []
min_quantum = 0.5
max_quantum = 4.0
step = 0.5
beta = 0.95
for ctxxTime in [
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3,
1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 3.0, 4.0
]:
print("Quantum:", ctxxTime)
repeatRuns = 10
parameters.quantum = ctxxTime
x_data.append(ctxxTime)
throughput = 0.0
responseTime_samples = []
utilization = 0.0
goodput = 0.0
badput = 0.0
dropRate = 0.0
droppedFraction = 0.0
timedOutFraction = 0.0
for _ in range(0, repeatRuns):
simulator = Simulator(parameters)
metrics = simulator.run()
throughput += metrics.throughput
responseTime_samples.append(metrics.responseTime)
utilization += metrics.utilization
goodput += metrics.goodput
badput += metrics.badput
dropRate += metrics.dropRate
droppedFraction += metrics.droppedFraction
timedOutFraction += metrics.timedOutFraction
(responseTime_lb, responseTime_mean,
responseTime_ub) = confidenceIntervals(responseTime_samples, beta)
throughput_y_data.append(scipy.mean(throughput) / repeatRuns)
utilization_y_data.append(scipy.mean(utilization) / repeatRuns)
responseTime_y_data.append(responseTime_mean)
responseTime_ci.append((responseTime_ub - responseTime_lb) / 2)
goodput_y_data.append(scipy.mean(goodput) / repeatRuns)
badput_y_data.append(scipy.mean(badput) / repeatRuns)
dropRate_y_data.append(scipy.mean(dropRate) / repeatRuns)
droppedFraction_y_data.append(scipy.mean(droppedFraction) / repeatRuns)
timedOutFraction_y_data.append(
scipy.mean(timedOutFraction) / repeatRuns)
# f = open(prefix + "_data.tsv", "w")
# print("ctxxTime\tThroughput\tResponseTime\tUtilization\tGoodput\tBadput\tDropRate", file=f)
# for i in range(min_users - 1, max_users):
# print(x_data[i], end="\t", file=f)
# print("{0:.4f}".format(throughput_y_data[i]), end="\t", file=f)
# print("{0:.4f}".format(responseTime_y_data[i]), end="\t", file=f)
# print("{0:.4f}".format(utilization_y_data[i]), end="\t", file=f)
# print("{0:.4f}".format(goodput_y_data[i]), end="\t", file=f)
# print("{0:.4f}".format(badput_y_data[i]), end="\t", file=f)
# print("{0:.4f}".format(dropRate_y_data[i]), file=f)
# f.close()
plt.title("Throughput v/s Quantum")
plt.xlabel("Quantum")
plt.ylabel("Throughput (req / s)")
plt.plot(x_data, throughput_y_data, "c")
plt.grid(True)
plt.savefig(prefix + "_Throughput.png")
plt.close()
plt.title("Goodput v/s Quantum")
plt.xlabel("Quantum")
plt.ylabel("Goodput (req / s)")
plt.plot(x_data, goodput_y_data, "c")
plt.grid(True)
plt.savefig(prefix + "_Goodput.png")
plt.close()
plt.title("Badput v/s Quantum")
plt.xlabel("Quantum")
plt.ylabel("Badput (req / s)")
plt.plot(x_data, badput_y_data, "c")
plt.grid(True)
plt.savefig(prefix + "_Badput.png")
plt.close()
plt.title("Request Drop Rate v/s Quantum")
plt.xlabel("Quantum")
plt.ylabel("Request Drop Rate (req / s)")
plt.plot(x_data, dropRate_y_data, "c")
plt.grid(True)
plt.savefig(prefix + "_DropRate.png")
plt.close()
plt.title("Request Drop Fracttion v/s Quantum")
plt.xlabel("Quantum")
plt.ylabel("Request Drop Fraction")
plt.plot(x_data, droppedFraction_y_data, "c")
plt.grid(True)
plt.savefig(prefix + "_DroppedFraction.png")
plt.close()
plt.title("Timed out Fracttion v/s Quantum")
plt.xlabel("Quantum")
plt.ylabel("Timed Out Fraction")
plt.plot(x_data, timedOutFraction_y_data, "c")
plt.grid(True)
plt.savefig(prefix + "_TimedOutFraction.png")
plt.close()
plt.title("Response Time v/s Quantum")
plt.xlabel("Quantum")
plt.ylabel("Response Time (s)")
plt.plot(x_data, responseTime_y_data, "c")
plt.errorbar(x_data,
responseTime_y_data,
yerr=responseTime_ci,
linestyle="None")
plt.grid(True)
plt.savefig(prefix + "_ResponseTime.png")
plt.close()
plt.title("CPU Utilization v/s Quantum")
plt.xlabel("Quantum")
plt.ylabel("CPU Utilization")
plt.plot(x_data, utilization_y_data, "c")
plt.grid(True)
plt.savefig(prefix + "_Utilization.png")
plt.close()
plt.title("CPU Utilization v/s Throughput")
plt.xlabel("Throught")
plt.ylabel("CPU Utilization")
plt.plot(throughput_y_data, utilization_y_data, "c")
plt.grid(True)
plt.savefig(prefix + "_UtilizationThroughput.png")
plt.close()
#!/usr/bin/env python3
from Simulator import Simulator
from System import System
parameters = System.Parameters(n_users=50,
n_CPUs=4,
easeInTime=20.0,
maxIters=10000,
bufferCapacity=800,
threadpoolSize=200,
quantum=0.5,
ctxxTime=1,
serviceTimeMean=2,
requestTimeoutMin=100.0,
requestTimeoutMax=300.0,
thinkTimeMean=12.0,
thinkTimeStdv=4.0,
retryThinkTimeMean=12.0,
retryThinkTimeStdv=4.0)
simulator = Simulator(parameters)
metrics = simulator.run()
metrics.display()
def correctness(prefix):
parameters = System.Parameters(n_users=50,
n_CPUs=1,
easeInTime=20.0,
maxIters=10000,
bufferCapacity=8000000,
threadpoolSize=2000000,
quantum=10000.0,
ctxxTime=0.0,
serviceTimeMean=0.5,
requestTimeoutMin=1000.0,
requestTimeoutMax=2000.0,
thinkTimeMean=12.0,
thinkTimeStdv=4.0,
retryThinkTimeMean=12.0,
retryThinkTimeStdv=4.0)
x_data = []
throughput_y_data = []
responseTime_y_data = []
responseTime_ci = []
utilization_y_data = []
min_users = 1
max_users = 60
beta = 0.95
for users in range(min_users, max_users + 1):
print("Users:", users)
repeatRuns = 10
parameters.n_users = users
x_data.append(users)
throughput = 0.0
responseTime_samples = []
utilization = 0.0
for _ in range(0, repeatRuns):
simulator = Simulator(parameters)
metrics = simulator.run()
throughput += metrics.throughput
responseTime_samples.append(metrics.responseTime)
utilization += metrics.utilization
(responseTime_lb, responseTime_mean,
responseTime_ub) = confidenceIntervals(responseTime_samples, beta)
throughput_y_data.append(scipy.mean(throughput))
utilization_y_data.append(scipy.mean(utilization))
responseTime_y_data.append(responseTime_mean)
responseTime_ci.append((responseTime_ub - responseTime_lb) / 2)
f = open(prefix + "_data.tsv", "w")
print("Users\tThroughput\tResponseTime\tUtilization", file=f)
for i in range(min_users - 1, max_users):
print(x_data[i], end="\t", file=f)
print("{0:.4f}".format(throughput_y_data[i]), end="\t", file=f)
print("{0:.4f}".format(responseTime_y_data[i]), end="\t", file=f)
print("{0:.4f}".format(utilization_y_data[i]), file=f)
f.close()
plt.title("Throughput v/s Users")
plt.xlabel("Users")
plt.ylabel("Throughput (req / s)")
plt.plot(x_data, throughput_y_data, "c")
plt.grid(True)
plt.savefig(prefix + "_Throughput.png")
plt.close()
plt.title("Response Time v/s Users")
plt.xlabel("Users")
plt.ylabel("Response Time (s)")
plt.plot(x_data, responseTime_y_data, "m")
plt.errorbar(x_data,
responseTime_y_data,
yerr=responseTime_ci,
linestyle="None")
plt.grid(True)
plt.savefig(prefix + "_ResponseTime.png")
plt.close()
plt.title("CPU Utilization v/s Users")
plt.xlabel("Users")
plt.ylabel("CPU Utilization")
plt.plot(x_data, utilization_y_data, "y")
plt.grid(True)
plt.savefig(prefix + "_Utilization.png")
plt.close()
