1 # Copyright (c) 2016-2017 Intel Corporation
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
7 # http://www.apache.org/licenses/LICENSE-2.0
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 """ Fixed traffic profile definitions """
16 from __future__ import absolute_import
22 from yardstick.network_services.traffic_profile.prox_profile import ProxProfile
24 LOG = logging.getLogger(__name__)
27 class ProxBinSearchProfile(ProxProfile):
29 This profile adds a single stream at the beginning of the traffic session
32 def __init__(self, tp_config):
33 super(ProxBinSearchProfile, self).__init__(tp_config)
34 self.current_lower = self.lower_bound
35 self.current_upper = self.upper_bound
39 return self.current_upper - self.current_lower
43 return (self.current_lower + self.current_upper) / 2
45 def bounds_iterator(self, logger=None):
46 self.current_lower = self.lower_bound
47 self.current_upper = self.upper_bound
49 test_value = self.current_upper
50 while abs(self.delta) >= self.precision:
52 logger.debug("New interval [%s, %s), precision: %d", self.current_lower,
53 self.current_upper, self.step_value)
54 logger.info("Testing with value %s", test_value)
57 test_value = self.mid_point
59 def run_test_with_pkt_size(self, traffic_gen, pkt_size, duration):
60 """Run the test for a single packet size.
62 :param traffic_gen: traffic generator instance
63 :type traffic_gen: TrafficGen
64 :param pkt_size: The packet size to test with.
66 :param duration: The duration for each try.
71 LOG.info("Testing with packet size %d", pkt_size)
73 # Binary search assumes the lower value of the interval is
74 # successful and the upper value is a failure.
75 # The first value that is tested, is the maximum value. If that
76 # succeeds, no more searching is needed. If it fails, a regular
77 # binary search is performed.
79 # The test_value used for the first iteration of binary search
80 # is adjusted so that the delta between this test_value and the
81 # upper bound is a power-of-2 multiple of precision. In the
82 # optimistic situation where this first test_value results in a
83 # success, the binary search will complete on an integer multiple
84 # of the precision, rather than on a fraction of it.
90 # Store one time only value in influxdb
92 "test_duration" : traffic_gen.scenario_helper.scenario_cfg["runner"]["duration"],
93 "test_precision" : self.params["traffic_profile"]["test_precision"],
94 "tolerated_loss" : self.params["traffic_profile"]["tolerated_loss"],
97 self.queue.put(single_samples)
98 self.prev_time = time.time()
100 # throughput and packet loss from the most recent successful test
101 successful_pkt_loss = 0.0
102 for test_value in self.bounds_iterator(LOG):
103 result, port_samples = self._profile_helper.run_test(pkt_size, duration,
104 test_value, self.tolerated_loss)
105 self.curr_time = time.time()
106 diff_time = self.curr_time - self.prev_time
107 self.prev_time = self.curr_time
110 LOG.debug("Success! Increasing lower bound")
111 self.current_lower = test_value
112 successful_pkt_loss = result.pkt_loss
113 samples = result.get_samples(pkt_size, successful_pkt_loss, port_samples)
114 samples["TxThroughput"] = samples["TxThroughput"] * 1000 * 1000
116 # store results with success tag in influxdb
117 success_samples = {'Success_' + key: value for key, value in samples.items()}
119 success_samples["Success_rx_total"] = int(result.rx_total / diff_time)
120 success_samples["Success_tx_total"] = int(result.tx_total / diff_time)
121 success_samples["Success_can_be_lost"] = int(result.can_be_lost / diff_time)
122 success_samples["Success_drop_total"] = int(result.drop_total / diff_time)
123 self.queue.put(success_samples)
125 # Store Actual throughput for result samples
126 result_samples["Result_Actual_throughput"] = \
127 success_samples["Success_RxThroughput"]
129 LOG.debug("Failure... Decreasing upper bound")
130 self.current_upper = test_value
131 samples = result.get_samples(pkt_size, successful_pkt_loss, port_samples)
135 if isinstance(tmp, dict):
137 samples[k][k2] = int(samples[k][k2] / diff_time)
139 if theor_max_thruput < samples["TxThroughput"]:
140 theor_max_thruput = samples['TxThroughput']
141 self.queue.put({'theor_max_throughput': theor_max_thruput})
143 LOG.debug("Collect TG KPIs %s %s", datetime.datetime.now(), samples)
144 self.queue.put(samples)
146 result_samples["Result_pktSize"] = pkt_size
147 result_samples["Result_theor_max_throughput"] = theor_max_thruput/ (1000 * 1000)
148 self.queue.put(result_samples)