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# Copyright (c) 2016-2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Fixed traffic profile definitions """
from __future__ import absolute_import
import logging
import datetime
import time
from yardstick.network_services.traffic_profile.prox_profile import ProxProfile
from yardstick.network_services import constants
LOG = logging.getLogger(__name__)
class ProxBinSearchProfile(ProxProfile):
"""
This profile adds a single stream at the beginning of the traffic session
"""
def __init__(self, tp_config):
super(ProxBinSearchProfile, self).__init__(tp_config)
self.current_lower = self.lower_bound
self.current_upper = self.upper_bound
@property
def delta(self):
return self.current_upper - self.current_lower
@property
def mid_point(self):
return (self.current_lower + self.current_upper) / 2
def bounds_iterator(self, logger=None):
self.current_lower = self.lower_bound
self.current_upper = self.upper_bound
test_value = self.current_upper
while abs(self.delta) >= self.precision:
if logger:
logger.debug("New interval [%s, %s), precision: %d", self.current_lower,
self.current_upper, self.step_value)
logger.info("Testing with value %s", test_value)
yield test_value
test_value = self.mid_point
def run_test_with_pkt_size(self, traffic_gen, pkt_size, duration):
"""Run the test for a single packet size.
:param traffic_gen: traffic generator instance
:type traffic_gen: TrafficGen
:param pkt_size: The packet size to test with.
:type pkt_size: int
:param duration: The duration for each try.
:type duration: int
"""
LOG.info("Testing with packet size %d", pkt_size)
# Binary search assumes the lower value of the interval is
# successful and the upper value is a failure.
# The first value that is tested, is the maximum value. If that
# succeeds, no more searching is needed. If it fails, a regular
# binary search is performed.
#
# The test_value used for the first iteration of binary search
# is adjusted so that the delta between this test_value and the
# upper bound is a power-of-2 multiple of precision. In the
# optimistic situation where this first test_value results in a
# success, the binary search will complete on an integer multiple
# of the precision, rather than on a fraction of it.
theor_max_thruput = 0
result_samples = {}
# Store one time only value in influxdb
single_samples = {
"test_duration": traffic_gen.scenario_helper.scenario_cfg["runner"]["duration"],
"test_precision": self.params["traffic_profile"]["test_precision"],
"tolerated_loss": self.params["traffic_profile"]["tolerated_loss"],
"duration": duration
}
self.queue.put(single_samples)
self.prev_time = time.time()
# throughput and packet loss from the most recent successful test
successful_pkt_loss = 0.0
line_speed = traffic_gen.scenario_helper.all_options.get(
"interface_speed_gbps", constants.NIC_GBPS_DEFAULT) * constants.ONE_GIGABIT_IN_BITS
for test_value in self.bounds_iterator(LOG):
result, port_samples = self._profile_helper.run_test(pkt_size, duration,
test_value,
self.tolerated_loss,
line_speed)
self.curr_time = time.time()
diff_time = self.curr_time - self.prev_time
self.prev_time = self.curr_time
if result.success:
LOG.debug("Success! Increasing lower bound")
self.current_lower = test_value
successful_pkt_loss = result.pkt_loss
samples = result.get_samples(pkt_size, successful_pkt_loss, port_samples)
# store results with success tag in influxdb
success_samples = {'Success_' + key: value for key, value in samples.items()}
success_samples["Success_rx_total"] = int(result.rx_total / diff_time)
success_samples["Success_tx_total"] = int(result.tx_total / diff_time)
success_samples["Success_can_be_lost"] = int(result.can_be_lost / diff_time)
success_samples["Success_drop_total"] = int(result.drop_total / diff_time)
self.queue.put(success_samples)
# Store Actual throughput for result samples
result_samples["Result_Actual_throughput"] = \
success_samples["Success_RxThroughput"]
else:
LOG.debug("Failure... Decreasing upper bound")
self.current_upper = test_value
samples = result.get_samples(pkt_size, successful_pkt_loss, port_samples)
for k in samples:
tmp = samples[k]
if isinstance(tmp, dict):
for k2 in tmp:
samples[k][k2] = int(samples[k][k2] / diff_time)
if theor_max_thruput < samples["TxThroughput"]:
theor_max_thruput = samples['TxThroughput']
self.queue.put({'theor_max_throughput': theor_max_thruput})
LOG.debug("Collect TG KPIs %s %s", datetime.datetime.now(), samples)
self.queue.put(samples)
result_samples["Result_pktSize"] = pkt_size
result_samples["Result_theor_max_throughput"] = theor_max_thruput/(1000 * 1000)
self.queue.put(result_samples)
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