def __init__(self, sim):
"""
Initialize the counter collection.
:param sim: the simulation, the CounterCollection belongs to.
"""
self.sim = sim
# waiting time
self.cnt_wt = TimeIndependentCounter(name="waiting time")
self.hist_wt = TimeIndependentHistogram(self.sim, "w")
self.acnt_wt = TimeIndependentAutocorrelationCounter(
"waiting time with lags 1 to 20", max_lag=20)
# queue length
self.cnt_ql = TimeDependentCounter(self.sim, name="queue length")
self.hist_ql = TimeDependentHistogram(self.sim, "q")
# system utilization
self.cnt_sys_util = TimeDependentCounter(self.sim,
name="system utilization")
# blocking probability
self.cnt_bp = TimeIndependentCounter("bp")
self.hist_bp = TimeIndependentHistogram(self.sim, "bp")
# cross correlations
self.cnt_iat_wt = TimeIndependentCrosscorrelationCounter(
"inter-arrival time vs. waiting time")
self.cnt_iat_st = TimeIndependentCrosscorrelationCounter(
"inter-arrival time vs. service time")
self.cnt_iat_syst = TimeIndependentCrosscorrelationCounter(
"inter-arrival time vs. system time")
self.cnt_st_syst = TimeIndependentCrosscorrelationCounter(
"service time vs. system time")
def task_5_2_4(rho, alpha, sim_time, num):
"""
Plot confidence interval as described in the task description for task 5.2.4.
We use the function plot_confidence() for the actual plotting and run our simulation several times to get the
samples. Due to the different configurations, we receive eight plots in two figures.
"""
# TODO Task 5.2.4: Your code goes here
#rho = 0.5 / alpha = 0.1 / Sim time = 100s
TIC_SU = TimeIndependentCounter("System Utilization")
TIC_CI = []
sim_param = SimParam()
random.seed(sim_param.SEED)
sim = Simulation(sim_param)
sim.sim_param.SIM_TIME = sim_time
sim.sim_param.S = 100000
sim.sim_param.RHO = rho
random.seed(sim.sim_param.SEED_IAT)
random.seed(sim.sim_param.SEED_ST)
for i in range(100):
for j in range(30):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC_SU.count(sim.do_simulation().system_utilization)
sim.reset()
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC_CI.append(
(TIC_SU.get_mean() - TIC_SU.report_confidence_interval(alpha),
TIC_SU.get_mean() + TIC_SU.report_confidence_interval(alpha)))
TIC_SU.reset()
plot_confidence(sim, 100, TIC_CI, rho, "alpha=" + str(alpha), num, alpha)
def task_5_2_2():
"""
Run simulation in batches. Start the simulation with running until a customer count of n=100 or (n=1000) and
continue to increase the number of customers by dn=n.
Count the blocking proabability for the batch and calculate the confidence interval width of all values, that have
been counted until now.
Do this until the desired confidence level is reached and print out the simulation time as well as the number of
batches.
"""
results = [None, None, None, None]
# TODO Task 5.2.2: Your code goes here
bp = []
hw = []
sim_param = SimParam()
sim = Simulation(sim_param)
sim.sim_param.S = 4
sim.sim_param.RHO = .9
err = .0015
half_width = 1.0
count_bp = TimeIndependentCounter()
i = 0
for batch in [100, 1000]:
for alpha in [.1, .05]:
first_batch = False
count_bp.reset()
sim.reset()
while 1:
blocking_pro = sim.do_simulation_n_limit(
batch, first_batch).blocking_probability
first_batch = True #after first batch
count_bp.count(blocking_pro)
half_width = count_bp.report_confidence_interval(alpha)
sim.sim_state.stop = False #set the parameter back to original value
sim.counter_collection.reset()
sim.sim_state.num_blocked_packets = 0
sim.sim_state.num_packets = 0
if half_width < err:
break
results[i] = sim.sim_state.now
bp.append(count_bp.get_mean())
hw.append(half_width)
i += 1
# print and return results
print("BATCH SIZE: 100; ALPHA: 10%; TOTAL SIMULATION TIME (SECONDS): " +
str(results[0] / 1000) + "; Blocking Probability Mean: " +
str(bp[0]) + "; Half width: " + str(hw[0]))
print("BATCH SIZE: 100; ALPHA: 5%; TOTAL SIMULATION TIME (SECONDS): " +
str(results[1] / 1000) + "; Blocking Probability Mean: " +
str(bp[1]) + "; Half width: " + str(hw[1]))
print("BATCH SIZE: 1000; ALPHA: 10%; TOTAL SIMULATION TIME (SECONDS): " +
str(results[2] / 1000) + "; Blocking Probability Mean: " +
str(bp[2]) + "; Half width: " + str(hw[2]))
print("BATCH SIZE: 1000; ALPHA: 5%; TOTAL SIMULATION TIME (SECONDS): " +
str(results[3] / 1000) + "; Blocking Probability Mean: " +
str(bp[3]) + "; Half width: " + str(hw[3]))
return results
def __init__(self, sim):
"""
Initialize the counter collection.
:param sim: the simulation, the CounterCollection belongs to.
"""
self.sim = sim
# waiting time
self.cnt_wt = TimeIndependentCounter()
self.hist_wt = TimeIndependentHistogram(self.sim, "w")
# queue length
self.cnt_ql = TimeDependentCounter(self.sim)
self.hist_ql = TimeDependentHistogram(self.sim, "q")
# system utilization
self.cnt_sys_util = TimeDependentCounter(self.sim)
"""
def task_5_2_1():
"""
Run task 5.2.1. Make multiple runs until the blocking probability distribution reaches
a confidence level alpha. Simulation is performed for 100s and 1000s and for alpha = 90% and 95%.
"""
results = [None, None, None, None]
# TODO Task 5.2.1: Your code goes here
bp = []
hw = []
sim_param = SimParam()
sim = Simulation(sim_param)
sim.sim_param.S = 4
sim.sim_param.RHO = .9
count_bp = TimeIndependentCounter()
err = .0015
i = 0
for sim_time in [100000, 1000000]:
sim.sim_param.SIM_TIME = sim_time
for alpha in [.1, .05]:
count_bp.reset()
while 1:
sim.reset()
blocking_pro = sim.do_simulation().blocking_probability
count_bp.count(blocking_pro)
half_width = count_bp.report_confidence_interval(alpha=alpha)
if half_width < err:
break
results[i] = len(count_bp.values)
bp.append(count_bp.get_mean())
hw.append(half_width)
i += 1
# print and return results
print("SIM TIME: 100s; ALPHA: 10%; NUMBER OF RUNS: " + str(results[0]) +
"; TOTAL SIMULATION TIME (SECONDS): " + str(results[0] * 100) +
"; Blocking Probability Mean: " + str(bp[0]) + "; Half width: " +
str(hw[0]))
print("SIM TIME: 100s; ALPHA: 5%; NUMBER OF RUNS: " + str(results[1]) +
"; TOTAL SIMULATION TIME (SECONDS): " + str(results[1] * 100) +
"; Blocking Probability Mean: " + str(bp[1]) + "; Half width: " +
str(hw[1]))
print("SIM TIME: 1000s; ALPHA: 10%; NUMBER OF RUNS: " + str(results[2]) +
"; TOTAL SIMULATION TIME (SECONDS): " + str(results[2] * 1000) +
"; Blocking Probability Mean: " + str(bp[2]) + "; Half width: " +
str(hw[2]))
print("SIM TIME: 1000s; ALPHA: 5%; NUMBER OF RUNS: " + str(results[3]) +
"; TOTAL SIMULATION TIME (SECONDS): " + str(results[3] * 1000) +
"; Blocking Probability Mean: " + str(bp[3]) + "; Half width: " +
str(hw[3]))
return results
def task_5_2_1():
"""
Run task 5.2.1. Make multiple runs until the blocking probability distribution reaches
a significance level alpha. Simulation is performed for 100s and 1000s and for alpha = 10% and 5%.
"""
sim = Simulation()
# set parameters
sim.sim_param.RHO = .9
sim.reset()
sim.sim_param.EPSILON = .0015
sim.sim_param.S = 4
# simulate
results = []
for sim_time in [100, 1000]:
sim.sim_param.SIM_TIME = sim_time * 1000
for alpha in [.1, .05]:
sim.sim_param.ALPHA = alpha
counter = TimeIndependentCounter("Blocking Probability")
counter.reset()
tmp = 1.0
while len(counter.values) < 5 or tmp > sim.sim_param.EPSILON:
sim.reset()
sim_result = sim.do_simulation()
bp = sim_result.blocking_probability
counter.count(bp)
tmp = counter.report_confidence_interval(
alpha=sim.sim_param.ALPHA, print_report=False)
results.append(len(counter.values))
counter.report_confidence_interval(alpha=sim.sim_param.ALPHA,
print_report=True)
# print and return results
print('SIM TIME: 100s; ALPHA: 10%; NUMBER OF RUNS: ' + str(results[0]) +
'; TOTAL SIMULATION TIME (SECONDS): ' + str(results[0] * 100))
print('SIM TIME: 100s; ALPHA: 5%; NUMBER OF RUNS: ' + str(results[1]) +
'; TOTAL SIMULATION TIME (SECONDS): ' + str(results[1] * 100))
print('SIM TIME: 1000s; ALPHA: 10%; NUMBER OF RUNS: ' + str(results[2]) +
'; TOTAL SIMULATION TIME (SECONDS): ' + str(results[2] * 1000))
print('SIM TIME: 1000s; ALPHA: 5%; NUMBER OF RUNS: ' + str(results[3]) +
'; TOTAL SIMULATION TIME (SECONDS): ' + str(results[3] * 1000))
return results
def __init__(self, server):
"""
Initialize the counter collection.
:param sim: the simulation, the CounterCollection belongs to.
"""
self.server = server
#self.sim = server.slicesim
# waiting time
#self.cnt_wt = TimeIndependentCounter(self.server)
#self.hist_wt = TimeIndependentHistogram(self.server, "w")
#self.acnt_wt = TimeIndependentAutocorrelationCounter("waiting time with lags 1 to 20", max_lag=20)
# system time(delay)
self.cnt_syst = TimeIndependentCounter(self.server)
self.hist_syst = TimeIndependentHistogram(self.server, "s")
# queue length
self.cnt_ql = TimeDependentCounter(self.server)
self.hist_ql = TimeDependentHistogram(self.server, "q")
# throughput
self.cnt_tp = TimeDependentCounter(self.server, 'tp')
self.cnt_tp2 = TimeDependentCounter(self.server, 'tp2')
def task_5_2_2():
"""
Run simulation in batches. Start the simulation with running until a customer count of n=100 or (n=1000) and
continue to increase the number of customers by dn=n.
Count the blocking proabability for the batch and calculate the confidence interval width of all values, that have
been counted until now.
Do this until the desired confidence level is reached and print out the simulation time as well as the number of
batches.
"""
results = [None, None, None, None]
# TODO Task 5.2.2: Your code goes here
conf_level = .0015
sim = Simulation()
sim.sim_param.RHO = 0.9
sim.sim_param.S = 4
i = 0
tic = TimeIndependentCounter()
for batch_size in [100, 1000]:
for alpha in [0.1, 0.05]:
tic.reset()
check = False
new_batch = False
sim.reset()
while not check:
sim_result = sim.do_simulation_n_limit(batch_size, new_batch)
tic.count(sim_result.blocking_probability)
if len(tic.values) > 5 and tic.report_confidence_interval(
alpha) < conf_level:
check = True
else:
sim.sim_state.num_blocked_packets = 0
sim.sim_state.num_packets = 0
sim.sim_state.stop = False
sim.counter_collection.reset()
new_batch = True
results[i] = sim.sim_state.now
i += 1
# print and return results
print "BATCH SIZE: 100; ALPHA: 10%; TOTAL SIMULATION TIME (SECONDS): " + str(
results[0] / 1000)
print "BATCH SIZE: 100; ALPHA: 5%; TOTAL SIMULATION TIME (SECONDS): " + str(
results[1] / 1000)
print "BATCH SIZE: 1000; ALPHA: 10%; TOTAL SIMULATION TIME (SECONDS): " + str(
results[2] / 1000)
print "BATCH SIZE: 1000; ALPHA: 5%; TOTAL SIMULATION TIME (SECONDS): " + str(
results[3] / 1000)
return results
def task_5_2_1():
"""
Run task 5.2.1. Make multiple runs until the blocking probability distribution reaches
a confidence level alpha. Simulation is performed for 100s and 1000s and for alpha = 90% and 95%.
"""
results = [None, None, None, None]
# TODO Task 5.2.1: Your code goes here
conf_level = .0015
sim = Simulation()
sim.sim_param.RHO = 0.9
sim.sim_param.S = 4
i = 0
tic = TimeIndependentCounter()
for sim_time in [100000, 1000000]:
sim.sim_param.SIM_TIME = sim_time
for alpha in [0.1, 0.05]:
tic.reset()
count = 0
check = False
while not check:
sim.reset()
sim_result = sim.do_simulation()
tic.count(sim_result.blocking_probability)
count += 1
if tic.report_confidence_interval(alpha) < conf_level:
check = True
results[i] = count
i += 1
# print and return results
print "SIM TIME: 100s; ALPHA: 10%; NUMBER OF RUNS: " + str(
results[0]) + "; TOTAL SIMULATION TIME (SECONDS): " + str(
results[0] * 100)
print "SIM TIME: 100s; ALPHA: 5%; NUMBER OF RUNS: " + str(
results[1]) + "; TOTAL SIMULATION TIME (SECONDS): " + str(
results[1] * 100)
print "SIM TIME: 1000s; ALPHA: 10%; NUMBER OF RUNS: " + str(
results[2]) + "; TOTAL SIMULATION TIME (SECONDS): " + str(
results[2] * 1000)
print "SIM TIME: 1000s; ALPHA: 5%; NUMBER OF RUNS: " + str(
results[3]) + "; TOTAL SIMULATION TIME (SECONDS): " + str(
results[3] * 1000)
return results
def task_5_2_4():
"""
Plot confidence interval as described in the task description for task 5.2.4.
We use the function plot_confidence() for the actual plotting and run our simulation several times to get the
samples. Due to the different configurations, we receive eight plots in two figures.
"""
# TODO Task 5.2.4: Your code goes here
sim = Simulation()
sim.sim_param.S = 10000
tic_sys_util = TimeIndependentCounter()
i = 1
pyplot.subplots_adjust(hspace=0.6)
for rho in [.5, .9]:
sim.sim_param.RHO = rho
sim.reset()
for alpha in [.1, .05]:
for sim_time in [100000, 1000000]:
sim.sim_param.SIM_TIME = sim_time
upper_bounds = []
lower_bounds = []
means = []
for _ in range(100):
tic_sys_util.reset()
for _ in range(30):
sim.reset()
sim_result = sim.do_simulation()
tic_sys_util.count(sim_result.system_utilization)
conf_interval = tic_sys_util.report_confidence_interval(
alpha)
sample_mean = tic_sys_util.get_mean()
lower_bounds.append(sample_mean - conf_interval)
upper_bounds.append(sample_mean + conf_interval)
means.append(sample_mean)
pyplot.subplot(4, 2, i)
plot_confidence(sim, range(1, 101), lower_bounds, upper_bounds,
np.mean(means), rho, "Sys Util", alpha)
i += 1
pyplot.show()
def task_5_2_2():
"""
Run simulation in batches. Start the simulation with running until a customer count of n=100 or (n=1000) and
continue to increase the number of customers by dn=n.
Count the blocking probability for the batch and calculate the significance interval width of all values, that have
been counted until now.
Do this until the desired confidence level is reached and print out the simulation time as well as the number of
batches.
"""
sim = Simulation()
# set parameters
sim.sim_param.RHO = .9
sim.sim_param.EPSILON = .0015
sim.sim_param.S = 4
results = []
for batch_packets in [100, 1000]:
for alpha in [.1, .05]:
dn = batch_packets
n = dn
sim.sim_param.ALPHA = alpha
counter = TimeIndependentCounter("Blocking Probability")
counter.reset()
confid_level_reached = False
sim.reset()
# execute simulation
while not confid_level_reached:
r = sim.do_simulation_n_limit(dn, new_batch=(n != dn))
counter.count(r.blocking_probability)
if len(counter.values
) > 5 and counter.report_confidence_interval(
sim.sim_param.ALPHA,
print_report=False) < sim.sim_param.EPSILON:
confid_level_reached = True
else:
n += dn
sim.counter_collection.reset()
sim.sim_state.num_blocked_packets = 0
sim.sim_state.num_packets = 0
sim.sim_state.stop = False
counter.report_confidence_interval(sim.sim_param.ALPHA,
print_report=True)
print('Number of batches (n=' + str(dn) + ' for blocking probability confidence): ' + str(n / dn) + \
'; simulation time: ' + str(int(sim.sim_state.now / 1000)) + 's.')
results.append(sim.sim_state.now)
# print and return results
print('BATCH SIZE: 100; ALPHA: 10%; TOTAL SIMULATION TIME (SECONDS): ' +
str(results[0] / 1000))
print('BATCH SIZE: 100; ALPHA: 5%; TOTAL SIMULATION TIME (SECONDS): ' +
str(results[1] / 1000))
print('BATCH SIZE: 1000; ALPHA: 10%; TOTAL SIMULATION TIME (SECONDS): ' +
str(results[2] / 1000))
print('BATCH SIZE: 1000; ALPHA: 5%; TOTAL SIMULATION TIME (SECONDS): ' +
str(results[3] / 1000))
return results
def test_confidence(self):
"""
Test the basic implementation of the confidence calculation in the time independent counter.
"""
tic = TimeIndependentCounter()
tic.count(0)
tic.count(3)
tic.count(5)
tic.count(2)
tic.count(5)
tic.count(8)
tic.count(1)
tic.count(2)
tic.count(1)
self.assertAlmostEqual(tic.report_confidence_interval(.05, print_report=True), 1.96, delta=.01,
msg="Error in Confidence interval calculation. Wrong size of half interval returned.")
self.assertAlmostEqual(tic.report_confidence_interval(.1, print_report=False), 1.58, delta=.01,
msg="Error in Confidence interval calculation. Wrong size of half interval returned.")
self.assertAlmostEqual(tic.report_confidence_interval(.2, print_report=False), 1.187, delta=.01,
msg="Error in Confidence interval calculation. Wrong size of half interval returned.")
self.assertEqual(tic.is_in_confidence_interval(4.5, alpha=.05), True,
msg="Error in Confidence interval calculation. Value should be in interval, but isn't.")
self.assertEqual(tic.is_in_confidence_interval(1.3, alpha=.05), True,
msg="Error in Confidence interval calculation. Value should be in interval, but isn't.")
self.assertEqual(tic.is_in_confidence_interval(5.0, alpha=.05), False,
msg="Error in Confidence interval calculation. Value id in interval, but shouldn't.")
self.assertEqual(tic.is_in_confidence_interval(4.5, alpha=.1), True,
msg="Error in Confidence interval calculation. Value should be in interval, but isn't.")
self.assertEqual(tic.is_in_confidence_interval(1.3, alpha=.1), False,
msg="Error in Confidence interval calculation. Value id in interval, but shouldn't.")
self.assertEqual(tic.is_in_confidence_interval(5.0, alpha=.1), False,
msg="Error in Confidence interval calculation. Value id in interval, but shouldn't.")
self.assertEqual(tic.is_in_confidence_interval(4.5, alpha=.2), False,
msg="Error in Confidence interval calculation. Value id in interval, but shouldn't.")
self.assertEqual(tic.is_in_confidence_interval(1.3, alpha=.2), False,
msg="Error in Confidence interval calculation. Value id in interval, but shouldn't.")
self.assertEqual(tic.is_in_confidence_interval(4.0, alpha=.2), True,
msg="Error in Confidence interval calculation. Value should be in interval, but isn't.")
lower, upper = tic.report_bootstrap_confidence_interval(alpha=.05, resample_size=10000)
self.assertAlmostEqual(lower, 1.55556, delta=0.01,
msg="Error in bootstrap confidence interval calculation. Wrong lower boundary.")
self.assertAlmostEqual(upper, 4.66667, delta=0.01,
msg="Error in bootstrap confidence interval calculation. Wrong upper boundary.")
self.assertEqual(tic.is_in_bootstrap_confidence_interval(4, resample_size=5000, alpha=.05), True,
msg="Error in Confidence interval calculation. Value should be in interval, but isn't.")
self.assertEqual(tic.is_in_bootstrap_confidence_interval(1, resample_size=5000, alpha=.05), False,
msg="Error in Confidence interval calculation. Value id in interval, but shouldn't.")
def test_TIC(self):
"""
Test the TimeIndependentCounter
"""
tic = TimeIndependentCounter()
tic.count(3)
tic.count(2)
tic.count(5)
tic.count(0)
self.assertEqual(tic.get_mean(), 2.5,
msg="Error in TimeIndependentCounter. Wrong mean calculation or wrong counting.")
self.assertEqual(tic.get_var(), numpy.var([3, 2, 5, 0], ddof=1),
msg="Error in TimeIndependentCounter. Wrong variance calculation or wrong counting.")
self.assertEqual(tic.get_stddev(), numpy.std([3, 2, 5, 0], ddof=1),
msg="Error in TimeIndependentCounter. Wrong std dev calculation or wrong counting.")
tic.reset()
tic.count(3.)
tic.count(2.)
tic.count(5.)
tic.count(0.)
self.assertEqual(tic.get_mean(), 2.5,
msg="Error in TimeIndependentCounter. Wrong mean calculation or wrong counting.")
self.assertEqual(tic.get_var(), numpy.var([3, 2, 5, 0], ddof=1),
msg="Error in TimeIndependentCounter. Wrong variance calculation or wrong counting.")
self.assertEqual(tic.get_stddev(), numpy.std([3, 2, 5, 0], ddof=1),
msg="Error in TimeIndependentCounter. Wrong std dev calculation or wrong counting.")
def task_5_2_4():
"""
Plot confidence interval as described in the task description for task 5.2.4.
We use the function plot_confidence() for the actual plotting and run our simulation several times to get the
samples. Due to the different configurations, we receive eight plots in two figures.
"""
# TODO Task 5.2.4: Your code goes here
sim_param = SimParam()
sim = Simulation(sim_param)
sim.sim_param.S = 40000000 #infinite M/M/1/inf
err = .0015
plt_no = 1
for rho in [0.5, 0.9]:
sim.sim_param.RHO = rho
for alpha in [0.1, 0.05]:
for sim_time in [100000, 1000000]:
sim.sim_param.SIM_TIME = sim_time
print(" Sim time " + str(sim.sim_param.SIM_TIME / 1000) +
"s " + " Alpha " + str(alpha) + " RHO " + str(rho))
count_util = TimeIndependentCounter()
mean_count = TimeIndependentCounter()
y_low = []
y_high = []
x = []
for repeat in range(100):
count_util.reset()
for sim_run in range(30):
sim.reset()
count_util.count(
sim.do_simulation().system_utilization)
mean = count_util.get_mean()
half_width = count_util.report_confidence_interval(
alpha=alpha)
mean_count.count(mean)
y_low.append(mean - half_width)
y_high.append(mean + half_width)
x.append(repeat + 1)
pyplot.subplot(2, 2, plt_no)
plt_no += 1
plot_confidence(sim, x, y_low, y_high, mean_count.get_mean(),
sim.sim_param.RHO, "Utilization", alpha)
pyplot.show()
plt_no = 1
def task_3_2_2():
"""
Here, we execute task 3.2.2 and print the results to the console.
The first result string keeps the results for 100s, the second one for 1000s simulation time.
"""
sim = Simulation()
cnt = TimeIndependentCounter("sys_util")
sim.sim_param.S = 5
sim.sim_param.SIM_TIME = 100000
print('Results for simulation time of 100s')
for rho in [0.01, 0.5, 0.8, 0.9]:
sim.sim_param.RHO = rho
sim.reset()
cnt.reset()
for _ in range(100):
cnt.count(sim.do_simulation().system_utilization)
print('rho = %s, real system utilization/throughput = %.4f' %
(rho, cnt.get_mean()))
sim.sim_param.SIM_TIME = 1000000
print('\nResults for simulation time of 1000s')
for rho in [0.01, 0.5, 0.8, 0.9]:
sim.sim_param.RHO = rho
sim.reset()
cnt.reset()
for _ in range(100):
cnt.count(sim.do_simulation().system_utilization)
print("rho = %s, real system utilization/throughput = %.4f" %
(rho, cnt.get_mean()))
def do_simulation_study(sim,
print_queue_length=False,
print_waiting_time=True):
"""
This simulation study is different from the one made in assignment 1. It is mainly used to gather and visualize
statistics for different buffer sizes S instead of finding a minimal number of spaces for a desired quality.
For every buffer size S (which ranges from 5 to 7), statistics are printed (depending on the input parameters).
Finally, after all runs, the results are plotted in order to visualize the differences and giving the ability
to compare them. The simulations are run first for 100s, then for 1000s. For each simulation time, two diagrams are
shown: one for the distribution of the mean waiting times and one for the average buffer usage
:param sim: the simulation object to do the simulation
:param print_queue_length: print the statistics for the queue length to the console
:param print_waiting_time: print the statistics for the waiting time to the console
"""
# counters for mean queue length and waiting time
counter_mean_queue_length = TimeIndependentCounter()
hist_mean_queue_length = TimeIndependentHistogram(sim, "q")
counter_mean_waiting_time = TimeIndependentCounter()
hist_mean_waiting_time = TimeIndependentHistogram(sim, "w")
# step through number of buffer spaces...
for S in sim.sim_param.S_VALUES:
sim.sim_param.S = S
counter_mean_queue_length.reset()
hist_mean_queue_length.reset()
counter_mean_waiting_time.reset()
hist_mean_waiting_time.reset()
sim.sim_param.SIM_TIME = 100000
sim.sim_param.NO_OF_RUNS = 1000
# repeat simulation
for run in range(sim.sim_param.NO_OF_RUNS):
# print(run)
sim.reset()
sim.do_simulation()
# add simulation result to counters and histograms (always use the mean)
counter_mean_queue_length.count(
sim.counter_collection.cnt_ql.get_mean())
hist_mean_queue_length.count(
sim.counter_collection.cnt_ql.get_mean())
counter_mean_waiting_time.count(
sim.counter_collection.cnt_wt.get_mean())
hist_mean_waiting_time.count(
sim.counter_collection.cnt_wt.get_mean())
pyplot.subplot(221)
pyplot.xlabel("Mean waiting time [ms] (SIM_TIME = 100.000ms)")
pyplot.ylabel("Distribution over n")
hist_mean_waiting_time.report()
pyplot.subplot(222)
pyplot.xlabel("Mean queue length (SIM_TIME = 100.000ms)")
pyplot.ylabel("Distribution over n")
hist_mean_queue_length.report()
# if desired, print statistics for queue length and waiting time
if print_queue_length:
print('Buffer size: ' + str(sim.sim_param.S) +
', simulation time: ' + str(sim.sim_param.SIM_TIME) +
', Mean buffer content: ' +
str(counter_mean_queue_length.get_mean()) + ' Variance: ' +
str(counter_mean_queue_length.get_var()))
if print_waiting_time:
print('Buffer size: ' + str(sim.sim_param.S) +
', simulation time: ' + str(sim.sim_param.SIM_TIME) +
', Mean waiting time: ' +
str(counter_mean_waiting_time.get_mean()) + ' Variance: ' +
str(counter_mean_waiting_time.get_var()))
counter_mean_queue_length.reset()
hist_mean_queue_length.reset()
counter_mean_waiting_time.reset()
hist_mean_waiting_time.reset()
sim.sim_param.SIM_TIME = 1000000
sim.sim_param.NO_OF_RUNS = 1000
# repeat simulation
for run in range(sim.sim_param.NO_OF_RUNS):
# print(run)
sim.reset()
sim.do_simulation()
# add simulation result to counters and histograms (always use the mean)
counter_mean_queue_length.count(
sim.counter_collection.cnt_ql.get_mean())
hist_mean_queue_length.count(
sim.counter_collection.cnt_ql.get_mean())
counter_mean_waiting_time.count(
sim.counter_collection.cnt_wt.get_mean())
hist_mean_waiting_time.count(
sim.counter_collection.cnt_wt.get_mean())
pyplot.subplot(223)
pyplot.xlabel("Mean waiting time [ms] (SIM_TIME = 1.000.000ms)")
pyplot.ylabel("Distribution over n")
hist_mean_waiting_time.report()
pyplot.subplot(224)
pyplot.xlabel("Mean queue length (SIM_TIME = 1.000.000ms)")
pyplot.ylabel("Distribution over n")
hist_mean_queue_length.report()
# if desired, print statistics for queue length and waiting time
if print_queue_length:
print('Buffer size: ' + str(sim.sim_param.S) +
', simulation time: ' + str(sim.sim_param.SIM_TIME) +
', Mean buffer content: ' +
str(counter_mean_queue_length.get_mean()) + ' Variance: ' +
str(counter_mean_queue_length.get_var()))
if print_waiting_time:
print('Buffer size: ' + str(sim.sim_param.S) +
', simulation time: ' + str(sim.sim_param.SIM_TIME) +
', Mean waiting time: ' +
str(counter_mean_waiting_time.get_mean()) + ' Variance: ' +
str(counter_mean_waiting_time.get_var()))
# set axis ranges for better comparison and display accumulated plot
pyplot.subplot(221)
pyplot.xlim([0, 3500])
pyplot.subplot(223)
pyplot.xlim([0, 3500])
pyplot.subplot(222)
pyplot.xlim([-.5, sim.sim_param.S_MAX + .5])
pyplot.subplot(224)
pyplot.xlim([-.5, sim.sim_param.S_MAX + .5])
pyplot.show()
def test_confidence(self):
"""
Test the basic implementation of the confidence calculation in the time independent counter.
"""
tic = TimeIndependentCounter()
tic.count(0)
tic.count(3)
tic.count(5)
tic.count(2)
tic.count(5)
tic.count(8)
tic.count(1)
tic.count(2)
tic.count(1)
self.assertAlmostEqual(
tic.report_confidence_interval(.05, print_report=False),
1.96,
delta=.01,
msg=
"Error in Confidence interval calculation. Wrong size of half interval returned."
)
self.assertAlmostEqual(
tic.report_confidence_interval(.1, print_report=False),
1.58,
delta=.01,
msg=
"Error in Confidence interval calculation. Wrong size of half interval returned."
)
self.assertAlmostEqual(
tic.report_confidence_interval(.2, print_report=False),
1.187,
delta=.01,
msg=
"Error in Confidence interval calculation. Wrong size of half interval returned."
)
self.assertEqual(
tic.is_in_confidence_interval(4.5, alpha=.05),
True,
msg=
"Error in Confidence interval calculation. Value should be in interval, but isn't."
)
self.assertEqual(
tic.is_in_confidence_interval(1.3, alpha=.05),
True,
msg=
"Error in Confidence interval calculation. Value should be in interval, but isn't."
)
self.assertEqual(
tic.is_in_confidence_interval(5.0, alpha=.05),
False,
msg=
"Error in Confidence interval calculation. Value id in interval, but shouldn't."
)
self.assertEqual(
tic.is_in_confidence_interval(4.5, alpha=.1),
True,
msg=
"Error in Confidence interval calculation. Value should be in interval, but isn't."
)
self.assertEqual(
tic.is_in_confidence_interval(1.3, alpha=.1),
False,
msg=
"Error in Confidence interval calculation. Value id in interval, but shouldn't."
)
self.assertEqual(
tic.is_in_confidence_interval(5.0, alpha=.1),
False,
msg=
"Error in Confidence interval calculation. Value id in interval, but shouldn't."
)
self.assertEqual(
tic.is_in_confidence_interval(4.5, alpha=.2),
False,
msg=
"Error in Confidence interval calculation. Value id in interval, but shouldn't."
)
self.assertEqual(
tic.is_in_confidence_interval(1.3, alpha=.2),
False,
msg=
"Error in Confidence interval calculation. Value id in interval, but shouldn't."
)
self.assertEqual(
tic.is_in_confidence_interval(4.0, alpha=.2),
True,
msg=
"Error in Confidence interval calculation. Value should be in interval, but isn't."
)
def task_3_2_2():
"""
Here, we execute task 3.2.2 and print the results to the console.
The first result string keeps the results for 100s, the second one for 1000s simulation time.
"""
sim = Simulation()
cnt = TimeIndependentCounter("sys_util")
sim.sim_param.S = 5
sim.sim_param.SIM_TIME = 100000
results100 = []
for rho in [0.01, 0.5, 0.8, 0.9]:
sim.sim_param.RHO = rho
sim.reset()
cnt.reset()
for _ in range(100):
cnt.count(sim.do_simulation().system_utilization)
results100.append(cnt.get_mean())
sim.sim_param.SIM_TIME = 1000000
results1000 = []
for rho in [0.01, 0.5, 0.8, 0.9]:
sim.sim_param.RHO = rho
sim.reset()
cnt.reset()
for _ in range(100):
cnt.count(sim.do_simulation().system_utilization)
results1000.append(cnt.get_mean())
print "Results for simulation time of 100s (rho = 0.01, 0.5, 0.8 and 0.9)"
print results100
print "Results for simulation time of 1000s (rho = 0.01, 0.5, 0.8 and 0.9)"
print results1000
class CounterCollection(object):
"""
CounterCollection is a collection of all counters and histograms that are used in the simulations.
It contains several counters and histograms, that are used in the different tasks.
Reporting is done by calling the report function. This function can be adapted, depending on which counters should
report their results and print strings or plot histograms.
"""
def __init__(self, server):
"""
Initialize the counter collection.
:param sim: the simulation, the CounterCollection belongs to.
"""
self.server = server
#self.sim = server.slicesim
# waiting time
#self.cnt_wt = TimeIndependentCounter(self.server)
#self.hist_wt = TimeIndependentHistogram(self.server, "w")
#self.acnt_wt = TimeIndependentAutocorrelationCounter("waiting time with lags 1 to 20", max_lag=20)
# system time(delay)
self.cnt_syst = TimeIndependentCounter(self.server)
self.hist_syst = TimeIndependentHistogram(self.server, "s")
# queue length
self.cnt_ql = TimeDependentCounter(self.server)
self.hist_ql = TimeDependentHistogram(self.server, "q")
# throughput
self.cnt_tp = TimeDependentCounter(self.server, 'tp')
self.cnt_tp2 = TimeDependentCounter(self.server, 'tp2')
# system utilization
#self.cnt_sys_util = TimeDependentCounter(self.server)
# blocking probability
#self.cnt_bp = TimeIndependentCounter(self.server, "bp")
#self.hist_bp = TimeIndependentHistogram(self.server, "bp")
# cross correlations
#self.cnt_iat_wt = TimeIndependentCrosscorrelationCounter("inter-arrival time vs. waiting time")
#self.cnt_iat_st = TimeIndependentCrosscorrelationCounter("inter-arrival time vs. service time")
#self.cnt_iat_syst = TimeIndependentCrosscorrelationCounter("inter-arrival time vs. system time")
#self.cnt_st_syst = TimeIndependentCrosscorrelationCounter("service time vs. system time")
def reset(self):
"""
Resets all counters and histograms.
"""
#self.cnt_wt.reset()
#self.hist_wt.reset()
#self.acnt_wt.reset()
self.cnt_syst.reset()
self.hist_syst.reset()
self.cnt_ql.reset()
self.hist_ql.reset()
self.cnt_tp.reset()
#self.cnt_sys_util.reset()
#self.cnt_bp.reset()
#self.hist_bp.reset()
#self.cnt_iat_wt.reset()
#self.cnt_iat_st.reset()
#self.cnt_iat_syst.reset()
#self.cnt_st_syst.reset()
def report(self, filename=''):
"""
Calls the report function of the counters and histograms.
Can be adapted, such that not all reports are printed
"""
#self.cnt_wt.report(filename)
#self.hist_wt.report(filename)
#self.acnt_wt.report()
self.cnt_syst.report(filename)
self.hist_syst.report(filename)
self.cnt_ql.report(filename)
self.hist_ql.report(filename)
self.cnt_tp.report(filename)
#self.cnt_sys_util.report()
#self.cnt_iat_wt.report()
#self.cnt_iat_st.report()
#self.cnt_iat_syst.report()
#self.cnt_st_syst.report()
def count_throughput(self, throughput):
"""
Count a throughput. Its data is counted by the various counters
tp in kilobits per second
"""
self.cnt_tp.count(throughput)
def count_throughput2(self, throughput):
"""
Count a throughput. Its data is counted by the various counters
tp in kilobits per second
"""
self.cnt_tp2.count(throughput)
def count_packet(self, packet):
"""
Count a packet. Its data is counted by the various counters
"""
#self.cnt_wt.count(packet.get_waiting_time())
#self.hist_wt.count(packet.get_waiting_time())
#self.acnt_wt.count(packet.get_waiting_time())
self.cnt_syst.count(packet.get_system_time())
self.hist_syst.count(packet.get_system_time())
#self.cnt_iat_wt.count(packet.get_interarrival_time(), packet.get_waiting_time())
#self.cnt_iat_st.count(packet.get_interarrival_time(), packet.get_service_time())
#self.cnt_iat_syst.count(packet.get_interarrival_time(), packet.get_system_time())
#self.cnt_st_syst.count(packet.get_service_time(), packet.get_system_time())
def count_queue(self):
"""
Count the number of packets in the buffer and add the values to the corresponding (time dependent) histogram.
This function should be called at least whenever the number of packets in the buffer changes.
The system utilization is counted as well and can be counted from the counter cnt_sys_util.
"""
self.cnt_ql.count(int(self.server.get_queue_length()))
self.hist_ql.count(self.server.get_queue_length())
class CounterCollection(object):
"""
CounterCollection is a collection of all counters and histograms that are used in the simulations.
It contains several counters and histograms, that are used in the different tasks.
Reporting is done by calling the report function. This function can be adapted, depending on which counters should
report their results and print strings or plot histograms.
"""
def __init__(self, sim):
"""
Initialize the counter collection.
:param sim: the simulation, the CounterCollection belongs to.
"""
self.sim = sim
# waiting time
self.cnt_wt = TimeIndependentCounter()
self.hist_wt = TimeIndependentHistogram(self.sim, "w")
# queue length
self.cnt_ql = TimeDependentCounter(self.sim)
self.hist_ql = TimeDependentHistogram(self.sim, "q")
# system utilization
self.cnt_sys_util = TimeDependentCounter(self.sim)
"""
# blocking probability
self.cnt_bp = TimeIndependentCounter("bp")
self.hist_bp = TimeIndependentHistogram(self.sim, "bp")
# correlations
self.cnt_iat_wt = TimeIndependentCrosscorrelationCounter("inter-arrival time vs. waiting time")
self.cnt_iat_st = TimeIndependentCrosscorrelationCounter("inter-arrival time vs. service time")
self.cnt_iat_syst = TimeIndependentCrosscorrelationCounter("inter-arrival time vs. system time")
self.cnt_st_syst = TimeIndependentCrosscorrelationCounter("service time vs. system time")
self.acnt_wt = TimeIndependentAutocorrelationCounter("waiting time with lags 1 to 20", max_lag=20)
"""
def reset(self):
"""
Resets all counters and histograms.
"""
self.cnt_wt.reset()
self.hist_wt.reset()
self.cnt_ql.reset()
self.hist_ql.reset()
self.cnt_sys_util.reset()
"""
self.cnt_bp.reset()
self.hist_bp.reset()
self.cnt_iat_wt.reset()
self.cnt_iat_st.reset()
self.cnt_iat_syst.reset()
self.cnt_st_syst.reset()
self.acnt_wt.reset()
"""
def report(self):
"""
Calls the report function of the counters and histograms.
Can be adapted, such that not all reports are printed
"""
self.cnt_wt.report()
self.hist_wt.report()
self.cnt_ql.report()
self.hist_ql.report()
self.cnt_sys_util.report()
"""
self.cnt_iat_wt.report()
self.cnt_iat_st.report()
self.cnt_iat_syst.report()
self.cnt_st_syst.report()
self.acnt_wt.report()
"""
def count_packet(self, packet):
"""
Count a packet. Its data is counted by the various counters
"""
self.cnt_wt.count(packet.get_waiting_time())
self.hist_wt.count(packet.get_waiting_time())
"""
self.cnt_iat_wt.count(packet.get_interarrival_time(), packet.get_waiting_time())
self.cnt_iat_st.count(packet.get_interarrival_time(), packet.get_service_time())
self.cnt_iat_syst.count(packet.get_interarrival_time(), packet.get_system_time())
self.cnt_st_syst.count(packet.get_service_time(), packet.get_system_time())
self.acnt_wt.count(packet.get_waiting_time())
"""
def count_queue(self):
"""
Count the number of packets in the buffer and add the values to the corresponding (time dependent) histogram.
This function should be called at least whenever the number of packets in the buffer changes.
The system utilization is counted as well and can be counted from the counter cnt_sys_util.
"""
self.cnt_ql.count(self.sim.system_state.get_queue_length())
self.hist_ql.count(self.sim.system_state.get_queue_length())
# TODO Task 2.5.1: Your code goes here
if self.sim.system_state.server_busy:
self.cnt_sys_util.count(1)
else:
self.cnt_sys_util.count(0)
def task_5_2_4():
"""
Plot confidence interval as described in the task description for task 5.2.4.
We use the function plot_confidence() for the actual plotting and run our simulation several times to get the
samples. Due to the different configurations, we receive eight plots in two figures.
"""
sim = Simulation()
sim.sim_param.S = 10000
for sys_util in [.5, .9]:
sim.sim_param.RHO = sys_util
sim.reset()
for alpha in [.1, .05]:
sim.sim_param.ALPHA = alpha
for time in [100, 1000]:
sim.sim_param.SIM_TIME = time * 1000
sys_util_counter = TimeIndependentCounter("su")
mean_counter = TimeIndependentCounter("mc")
y_min = []
y_max = []
x = []
for run in range(100):
sys_util_counter.reset()
for _ in range(30):
sim.reset()
sim_result = sim.do_simulation()
su = sim_result.system_utilization
sys_util_counter.count(su)
h = sys_util_counter.report_confidence_interval(
alpha=sim.sim_param.ALPHA, print_report=False)
m = sys_util_counter.get_mean()
mean_counter.count(m)
y_min.append(m - h)
y_max.append(m + h)
x.append(run + 1)
mean_calc = sim.sim_param.RHO
mean_real = mean_counter.get_mean()
total = len(x)
good = 0
good_real = 0
for i in range(len(x)):
if y_min[i] <= mean_calc <= y_max[i]:
good += 1
if y_min[i] <= mean_real <= y_max[i]:
good_real += 1
print(
str(good) + '/' + str(total) +
' cover theoretical mean, ' + str(good_real) + '/' +
str(total) + ' cover sample mean.')
if alpha == .1:
if time == 100:
pyplot.subplot(221)
else:
pyplot.subplot(223)
else:
if time == 100:
pyplot.subplot(222)
else:
pyplot.subplot(224)
plot_confidence(sim, x, y_min, y_max, mean_counter.get_mean(),
sim.sim_param.RHO, "system utilization")
pyplot.show()
def do_simulation_study(sim,
print_queue_length=False,
print_waiting_time=True):
"""
This simulation study is different from the one made in assignment 1. It is mainly used to gather and visualize
statistics for different buffer sizes S instead of finding a minimal number of spaces for a desired quality.
For every buffer size S (which ranges from 5 to 7), statistics are printed (depending on the input parameters).
Finally, after all runs, the results are plotted in order to visualize the differences and giving the ability
to compare them. The simulations are run first for 100s, then for 1000s. For each simulation time, two diagrams are
shown: one for the distribution of the mean waiting times and one for the average buffer usage
:param sim: the simulation object to do the simulation
:param print_queue_length: print the statistics for the queue length to the console
:param print_waiting_time: print the statistics for the waiting time to the console
"""
# TODO Task 2.7.1: Your code goes here
# TODO Task 2.7.2: Your code goes here
for i in sim.sim_param.S_VALUES:
sim.sim_param.S = i
mean_waiting_time_histogram = TimeIndependentHistogram(sim, "bp")
for j in range(sim.sim_param.NO_OF_RUNS):
sim.reset()
sim_result = sim.do_simulation()
mean_waiting_time_histogram.count(
sim.counter_collection.cnt_wt.get_mean())
mean_waiting_time_histogram.report()
mean_queue_length_histogram1 = TimeIndependentCounter("1")
mean_queue_length_histogram2 = TimeIndependentCounter("2")
mean_queue_length_histogram3 = TimeIndependentCounter("3")
width = 0.1
sim.sim_param.S = 5
for j in range(sim.sim_param.NO_OF_RUNS):
sim.reset()
sim_result = sim.do_simulation()
mean_queue_length_histogram1.count(
sim.counter_collection.cnt_ql.get_mean())
sim.sim_param.S = 6
for j in range(sim.sim_param.NO_OF_RUNS):
sim.reset()
sim_result1 = sim.do_simulation()
mean_queue_length_histogram2.count(
sim.counter_collection.cnt_ql.get_mean())
sim.sim_param.S = 7
for j in range(sim.sim_param.NO_OF_RUNS):
sim.reset()
sim_result2 = sim.do_simulation()
mean_queue_length_histogram3.count(
sim.counter_collection.cnt_ql.get_mean())
histogram1, bins1 = numpy.histogram(mean_queue_length_histogram1.values,
25, (0.0, 7.0))
histogram2, bins2 = numpy.histogram(mean_queue_length_histogram2.values,
25, (0.0, 7.0))
histogram3, bins3 = numpy.histogram(mean_queue_length_histogram3.values,
25, (0.0, 7.0))
fig, ax = pyplot.subplots()
bins2 = array(bins2.tolist())
bins2 = bins2 + ones(len(bins2.tolist())) * width
bins3 = array(bins3.tolist())
bins3 = bins3 + ones(len(bins3.tolist())) * 2.0 * width
rects1 = ax.bar(bins1.tolist(),
histogram1.tolist() + [0],
width,
color='r')
rects2 = ax.bar(bins2.tolist(),
histogram2.tolist() + [0],
width,
color='g')
rects3 = ax.bar(bins3.tolist(),
histogram3.tolist() + [0],
width,
color='b')
ax.legend((rects1[0], rects2[0], rects3[0]), ('S5', 'S6', 'S7'))
pyplot.show()
def task_5_2_1():
"""
Run task 5.2.1. Make multiple runs until the blocking probability distribution reaches
a confidence level alpha. Simulation is performed for 100s and 1000s and for alpha = 90% and 95%.
"""
results = [None, None, None, None]
TIC = TimeIndependentCounter("bp")
# TODO Task 5.2.1: Your code goes here
#SIM TIME: 100s; ALPHA: 10%
sim_param = SimParam()
random.seed(sim_param.SEED)
sim = Simulation(sim_param)
sim.sim_param.SIM_TIME = 100000
sim.sim_param.S = 4
sim.sim_param.RHO = 0.9
for i in range(10000):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(sim.do_simulation().blocking_probability)
if TIC.report_confidence_interval(0.1) < 0.0015:
results[0] = i
break
sim.reset()
#SIM TIME: 100s; ALPHA: 5%
TIC.reset()
for i in range(10000):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(sim.do_simulation().blocking_probability)
if TIC.report_confidence_interval(0.05) < 0.0015:
results[1] = i
break
sim.reset()
#SIM TIME: 1000s; ALPHA: 10%
sim.sim_param.SIM_TIME = 1000000
TIC.reset()
for i in range(10000):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(sim.do_simulation().blocking_probability)
if TIC.report_confidence_interval(0.05) < 0.0015:
results[2] = i
break
sim.reset()
#SIM TIME: 1000s; ALPHA: 5%
sim.sim_param.SIM_TIME = 1000000
TIC.reset()
for i in range(10000):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(sim.do_simulation().blocking_probability)
if TIC.report_confidence_interval(0.05) < 0.0015:
results[3] = i
break
sim.reset()
# print and return results
print "SIM TIME: 100s; ALPHA: 10%; NUMBER OF RUNS: " + str(results[0])
print "SIM TIME: 100s; ALPHA: 5%; NUMBER OF RUNS: " + str(results[1])
print "SIM TIME: 1000s; ALPHA: 10%; NUMBER OF RUNS: " + str(results[2])
print "SIM TIME: 1000s; ALPHA: 5%; NUMBER OF RUNS: " + str(results[3])
return results
from simparam import SimParam
from simulation import Simulation
from matplotlib import pyplot
from counter import TimeIndependentCounter
from histogram import TimeIndependentHistogram
"""
This file should be used to keep all necessary code that is used for the simulation study in part 2 of the programming
assignment. It contains the tasks 2.7.1 and 2.7.2.
The function do_simulation_study() should be used to run the simulation routine, that is described in the assignment.
"""
sim_param = SimParam()
random.seed(sim_param.SEED)
sim = Simulation(sim_param)
ql_count = TimeIndependentCounter()
ql_hist = TimeIndependentHistogram(sim, "q")
wt_count = TimeIndependentCounter()
wt_hist = TimeIndependentHistogram(sim, "w")
def task_2_7_1():
"""
Here, you should execute task 2.7.1 (and 2.7.2, if you want).
"""
# TODO Task 2.7.1: Your code goes here
return do_simulation_study(sim)
def task_2_7_2():
def task_5_2_2():
"""
Run simulation in batches. Start the simulation with running until a customer count of n=100 or (n=1000) and
continue to increase the number of customers by dn=n.
Count the blocking proabability for the batch and calculate the confidence interval width of all values, that have
been counted until now.
Do this until the desired confidence level is reached and print out the simulation time as well as the number of
batches.
"""
num_batches1 = 0
num_batches2 = 0
num_batches3 = 0
num_batches4 = 0
TIC = TimeIndependentCounter("bp")
# TODO Task 5.2.2: Your code goes here
sim_param = SimParam()
random.seed(sim_param.SEED)
sim = Simulation(sim_param)
sim.sim_param.S = 4
sim.sim_param.RHO = 0.9
## n = 100
# ALPHA: 5%
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(sim.do_simulation_n_limit(100).blocking_probability)
for i in range(10000):
sim.sim_result = SimResult(sim)
sim.sim_state.stop = False
sim.sim_state.num_packets = 0
sim.sim_state.num_blocked_packets = 0
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(
sim.do_simulation_n_limit(100, True).blocking_probability)
print TIC.report_confidence_interval(0.05)
if TIC.report_confidence_interval(0.05) < 0.0015:
num_batches1 = i + 1
break
t1 = sim.sim_state.now
# ALPHA: 10%
sim.reset()
TIC.reset()
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(sim.do_simulation_n_limit(100).blocking_probability)
for i in range(10000):
sim.sim_result = SimResult(sim)
sim.sim_state.stop = False
sim.sim_state.num_packets = 0
sim.sim_state.num_blocked_packets = 0
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(
sim.do_simulation_n_limit(100, True).blocking_probability)
print TIC.report_confidence_interval(0.1)
if TIC.report_confidence_interval(0.1) < 0.0015:
num_batches2 = i + 1
break
t2 = sim.sim_state.now
sim.reset()
## n = 1000
# ALPHA: 5%
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(sim.do_simulation_n_limit(100).blocking_probability)
for i in range(10000):
sim.sim_result = SimResult(sim)
sim.sim_state.stop = False
sim.sim_state.num_packets = 0
sim.sim_state.num_blocked_packets = 0
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(
sim.do_simulation_n_limit(1000, True).blocking_probability)
print TIC.report_confidence_interval(0.05)
if TIC.report_confidence_interval(0.05) < 0.0015:
num_batches3 = i + 1
break
t3 = sim.sim_state.now
# ALPHA: 10%
sim.reset()
TIC.reset()
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(sim.do_simulation_n_limit(100).blocking_probability)
for i in range(10000):
sim.sim_result = SimResult(sim)
sim.sim_state.stop = False
sim.sim_state.num_packets = 0
sim.sim_state.num_blocked_packets = 0
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
TIC.count(
sim.do_simulation_n_limit(1000, True).blocking_probability)
print TIC.report_confidence_interval(0.1)
if TIC.report_confidence_interval(0.1) < 0.0015:
num_batches4 = i + 1
break
t4 = sim.sim_state.now
# print and return both results
print "N: 100; ALPHA: 5%; NUMBER OF BATCHES: " + str(
num_batches1) + " and SIM TIME: " + str(t1)
print "N: 100; ALPHA: 10%; NUMBER OF BATCHES: " + str(
num_batches2) + " and SIM TIME: " + str(t2)
print "N: 1000; ALPHA: 5%; NUMBER OF BATCHES: " + str(
num_batches3) + " and SIM TIME: " + str(t3)
print "N: 1000; ALPHA: 10%; NUMBER OF BATCHES: " + str(
num_batches4) + " and SIM TIME: " + str(t4)
return [t1, t2, t3, t4]
def task_3_2_2():
"""
Here, we execute task 3.2.2 and print the results to the console.
The first result string keeps the results for 100s, the second one for 1000s simulation time.
"""
# TODO Task 3.2.2: Your code goes here
sim_param = SimParam()
sim = Simulation(sim_param)
count_sys = TimeIndependentCounter()
sim_param.S = 5
print("S = " + str(sim.sim_param.S))
sim_param.SIM_TIME = 100000 #100s
for rho in [0.01, 0.5, 0.8, 0.9]:
sim.sim_param.RHO = rho
sim.reset()
count_sys.reset()
for k in range(sim.sim_param.NO_OF_RUNS):
r = sim.do_simulation().system_utilization
count_sys.count(r)
print("system_utilization = " + str(count_sys.get_mean()) + " RHO "+str(rho) + " Sim.Time=100s")
sim_param.SIM_TIME = 1000000 #1000s
for rho in [0.01, 0.5, 0.8, 0.9]:
sim.sim_param.RHO = rho
sim.reset()
count_sys.reset()
for k in range(sim.sim_param.NO_OF_RUNS):
r = sim.do_simulation().system_utilization
count_sys.count(r)
print("system_utilization = " + str(count_sys.get_mean()) + " RHO "+str(rho) + " Sim.Time=1000s")
sim_param = SimParam()
sim = Simulation(sim_param)
count_sys = TimeIndependentCounter()
sim_param.S = 100000
print("S = " + str(sim.sim_param.S))
sim_param.SIM_TIME = 100000 #100s
for rho in [0.01, 0.5, 0.8, 0.9]:
sim.sim_param.RHO = rho
sim.reset()
count_sys.reset()
for k in range(sim.sim_param.NO_OF_RUNS):
r = sim.do_simulation().system_utilization
count_sys.count(r)
print("system_utilization = " + str(count_sys.get_mean()) + " RHO "+str(rho) + " Sim.Time=100s")
sim_param.SIM_TIME = 1000000 #1000s
for rho in [0.01, 0.5, 0.8, 0.9]:
sim.sim_param.RHO = rho
sim.reset()
count_sys.reset()
for k in range(sim.sim_param.NO_OF_RUNS):
r = sim.do_simulation().system_utilization
count_sys.count(r)
print("system_utilization = " + str(count_sys.get_mean()) + " RHO "+str(rho) + " Sim.Time=1000s")
sim_param = SimParam()
sim = Simulation(sim_param)
count_sys = TimeIndependentCounter()
sim_param.S = 1
print("S = " + str(sim.sim_param.S))
sim_param.SIM_TIME = 100000 #100s
for rho in [0.01, 0.5, 0.8, 0.9]:
sim.sim_param.RHO = rho
sim.reset()
count_sys.reset()
for k in range(sim.sim_param.NO_OF_RUNS):
r = sim.do_simulation().system_utilization
count_sys.count(r)
print("system_utilization = " + str(count_sys.get_mean()) + " RHO "+str(rho) + " Sim.Time=100s")
sim_param.SIM_TIME = 1000000 #1000s
for rho in [0.01, 0.5, 0.8, 0.9]:
sim.sim_param.RHO = rho
sim.reset()
count_sys.reset()
for k in range(sim.sim_param.NO_OF_RUNS):
r = sim.do_simulation().system_utilization
count_sys.count(r)
print("system_utilization = " + str(count_sys.get_mean()) + " RHO "+str(rho) + " Sim.Time=1000s")