def results(self):
results = Tree({'MEAN': self.mean_stretch/self.sess_count,
'MEAN_REQUEST': self.mean_req_stretch/self.sess_count,
'MEAN_CONTENT': self.mean_cont_stretch/self.sess_count})
if self.cdf:
results['CDF'] = cdf(self.stretch_data)
results['CDF_REQUEST'] = cdf(self.req_stretch_data)
results['CDF_CONTENT'] = cdf(self.cont_stretch_data)
return results
def results(self):
results = Tree({
"MEAN":
self.mean_stretch / self.sess_count,
"MEAN_REQUEST":
self.mean_req_stretch / self.sess_count,
"MEAN_CONTENT":
self.mean_cont_stretch / self.sess_count,
})
if self.cdf:
results["CDF"] = cdf(self.stretch_data)
results["CDF_REQUEST"] = cdf(self.req_stretch_data)
results["CDF_CONTENT"] = cdf(self.cont_stretch_data)
return results
def results(self):
if self.cdf:
results['CDF'] = cdf(self.latency_data)
results = Tree(
{'SATISFACTION': 1.0 * self.n_satisfied / self.sess_count})
per_service_sats = {}
for service in self.service_requests.keys():
per_service_sats[service] = 1.0 * self.service_satisfied[
service] / self.service_requests[service]
results['PER_SERVICE_SATISFACTION'] = per_service_sats
results['PER_SERVICE_REQUESTS'] = self.service_requests
results['PER_SERVICE_SAT_REQUESTS'] = self.service_satisfied
results['SAT_TIMES'] = self.satrate_times
results['IDLE_TIMES'] = self.idle_times
results['NODE_IDLE_TIMES'] = self.node_idle_times
results['LATENCY'] = self.latency_times
results['DEADLINE_METRIC'] = self.deadline_metric_times
results['CLOUD_SAT_TIMES'] = self.cloud_sat_times
results['INSTANTIATION_OVERHEAD'] = self.instantiations_times
#print "Printing Sat. rate times:"
#for key in sorted(self.satrate_times):
# print (repr(key) + " " + repr(self.satrate_times[key]))
#print "Printing Idle times:"
#for key in sorted(self.idle_times):
# print (repr(key) + " " + repr(self.idle_times[key]))
#results['VMS_PER_SERVICE'] = self.vms_per_service
return results
def results(self):
results = Tree({
'MEAN_RSN_ZERO_HOP': np.mean(self.rsn_hit_ratio[0]),
'MEAN_RSN_ONE_HOP': np.mean(self.rsn_hit_ratio[1]),
'MEAN_RSN_TWO_HOP': np.mean(self.rsn_hit_ratio[2]),
'MEAN_RSN_THREE_HOP': np.mean(self.rsn_hit_ratio[3]),
})
results['MEAN_RSN_ALL'] = results['MEAN_RSN_ZERO_HOP'] + \
results['MEAN_RSN_ONE_HOP'] + \
results['MEAN_RSN_TWO_HOP'] + \
results['MEAN_RSN_THREE_HOP']
if self.cdf:
results.update({
'CDF_RSN_ZERO_HOP': cdf(self.rsn_hit_ratio[0]),
'CDF_RSN_ONE_HOP': cdf(self.rsn_hit_ratio[1]),
'CDF_RSN_TWO_HOP': cdf(self.rsn_hit_ratio[2]),
'CDF_RSN_THREE_HOP': cdf(self.rsn_hit_ratio[3]),
})
return results
def test_cdf_all_zeros(self):
data = np.zeros(200)
x, cdf = stats.cdf(data)
exp_x = [0]
exp_cdf = [1.0]
self.assertEqual(len(x), len(exp_x))
self.assertEqual(len(cdf), len(exp_cdf))
for i in range(len(exp_x)):
self.assertAlmostEqual(x[i], exp_x[i])
self.assertAlmostEqual(cdf[i], exp_cdf[i])
def test_cdf_all_zeros(self):
data = np.zeros(200)
x, cdf = stats.cdf(data)
exp_x = [0]
exp_cdf = [1.0]
assert len(x) == len(exp_x)
assert len(cdf) == len(exp_cdf)
for i in range(len(exp_x)):
assert round(abs(x[i] - exp_x[i]), 7) == 0
assert round(abs(cdf[i] - exp_cdf[i]), 7) == 0
def test_cdf_all_zeros(self):
data = np.zeros(200)
x, cdf = stats.cdf(data)
exp_x = [0]
exp_cdf = [1.0]
self.assertEqual(len(x), len(exp_x))
self.assertEqual(len(cdf), len(exp_cdf))
for i in range(len(exp_x)):
self.assertAlmostEqual(x[i], exp_x[i])
self.assertAlmostEqual(cdf[i], exp_cdf[i])
def test_cdf_known_input(self):
data = [-25, -25, 0.5, 1.1, 1.1, 1.1, 1.4, 1.4, 1.4, 1.4]
# CDF(-25) = 0.2
# CDF(0.5) = 0.3
# CDF(1.1) = 0.6
# CDF(1.5) = 1.0
x, cdf = stats.cdf(data)
exp_x = [-25, 0.5, 1.1, 1.4]
exp_cdf = [0.2, 0.3, 0.6, 1.0]
self.assertEqual(len(x), len(exp_x))
self.assertEqual(len(cdf), len(exp_cdf))
for i in range(len(exp_x)):
self.assertAlmostEqual(x[i], exp_x[i])
self.assertAlmostEqual(cdf[i], exp_cdf[i])
def test_cdf_known_input(self):
data = [-25, -25, 0.5, 1.1, 1.1, 1.1, 1.4, 1.4, 1.4, 1.4]
# CDF(-25) = 0.2
# CDF(0.5) = 0.3
# CDF(1.1) = 0.6
# CDF(1.5) = 1.0
x, cdf = stats.cdf(data)
exp_x = [-25, 0.5, 1.1, 1.4]
exp_cdf = [0.2, 0.3, 0.6, 1.0]
assert len(x) == len(exp_x)
assert len(cdf) == len(exp_cdf)
for i in range(len(exp_x)):
assert round(abs(x[i] - exp_x[i]), 7) == 0
assert round(abs(cdf[i] - exp_cdf[i]), 7) == 0
def test_cdf_known_input(self):
data = [-25, -25, 0.5, 1.1, 1.1, 1.1, 1.4, 1.4, 1.4, 1.4]
# CDF(-25) = 0.2
# CDF(0.5) = 0.3
# CDF(1.1) = 0.6
# CDF(1.5) = 1.0
x, cdf = stats.cdf(data)
exp_x = [-25, 0.5, 1.1, 1.4]
exp_cdf = [0.2, 0.3, 0.6, 1.0]
self.assertEqual(len(x), len(exp_x))
self.assertEqual(len(cdf), len(exp_cdf))
for i in range(len(exp_x)):
self.assertAlmostEqual(x[i], exp_x[i])
self.assertAlmostEqual(cdf[i], exp_cdf[i])
def results(self):
# Idle times
num_nodes = len(self.css.keys())
node_idle_times = [0.0 for x in range(0, num_nodes)]
node_vms = [0 for x in range(0, num_nodes)]
for flow in self.flow_start.keys():
duration = self.flow_end[flow] - self.flow_start[flow]
self.latency += duration
for node, cs in self.css.items():
num_of_vms = cs.numOfVMs
node_vms[node] = num_of_vms
for indx in range(0, num_of_vms):
node_idle_times[node] += cs.getIdleTime(indx, self.last_timestamp)
node_idle_times[node] /= num_of_vms
# Idle times
if self.cdf:
results['CDF'] = cdf(self.latency_data)
results = Tree({'LATENCY' : 1.0*self.latency/self.sess_count})
per_service_sats = {}
for service in self.service_requests.keys():
per_service_sats[service] = 1.0*self.service_satisfied[service]/self.service_requests[service]
results['PER_SERVICE_REQUESTS'] = self.service_requests
results['IDLE_TIMES'] = node_idle_times
results['NUM_OF_VMS'] = node_vms
#print "Printing Idle times:"
#node = 0
#for idle_time in node_idle_times:
# print (repr(node) + " " + repr(node_idle_times[node]))
# node += 1
#print ("Flow starts:\n")
#for flow in self.flow_start.keys():
# print (repr(flow) + " " + repr(self.flow_start[flow]))
#print ("Flow ends:\n")
#for flow in self.flow_end.keys():
# print (repr(flow) + " " + repr(self.flow_end[flow]))
return results
def test_cdf_array_input(self):
data = np.array(list(range(2000)))
x, cdf = stats.cdf(data)
self.assertEqual(len(x), 2000)
self.assertAlmostEqual(cdf[-1], 1.0)
def results(self):
results = Tree({'MEAN': self.latency/self.sess_count})
if self.cdf:
results['CDF'] = cdf(self.latency_data)
return results
def test_cdf_array_input(self):
data = np.array(list(range(2000)))
x, cdf = stats.cdf(data)
self.assertEqual(len(x), 2000)
self.assertAlmostEqual(cdf[-1], 1.0)
def test_cdf_deque_input(self):
data = collections.deque(range(2000))
x, cdf = stats.cdf(data)
self.assertEqual(len(x), 2000)
self.assertAlmostEqual(cdf[-1], 1.0)
def results(self):
results = Tree({"MEAN": self.latency / self.sess_count})
if self.cdf:
results["CDF"] = cdf(self.latency_data)
return results
def test_cdf_deque_input(self):
data = collections.deque(range(2000))
x, cdf = stats.cdf(data)
self.assertEqual(len(x), 2000)
self.assertAlmostEqual(cdf[-1], 1.0)
def test_cdf_array_input(self):
data = np.array(list(range(2000)))
x, cdf = stats.cdf(data)
assert len(x) == 2000
assert round(abs(cdf[-1] - 1.0), 7) == 0
def test_cdf_deque_input(self):
data = collections.deque(range(2000))
x, cdf = stats.cdf(data)
assert len(x) == 2000
assert round(abs(cdf[-1] - 1.0), 7) == 0
