def set_udp_packet_size(self, imix_frame_sizes):
# We should check the gen.cfg to make sure we only send UDP packets
# If only 1 packet size, still using the 'old' way of setting the
# packet sizes in PROX. Otherwise, using the 'new' way which
# automatically sets IP and UDP sizes. We should switch to the new way
# eventually for all cases.
if len(imix_frame_sizes) == 1:
# Frame size = PROX pkt size + 4 bytes CRC
# The set_size function takes the PROX packet size as a parameter
self.socket.set_size(self.machine_params['gencores'], 0,
imix_frame_sizes[0] - 4)
# Writing length in the ip header
self.socket.set_value(
self.machine_params['gencores'], 0, self.ip_length_offset,
imix_frame_sizes[0] - self.frame_size_minus_ip_size, 2)
# Writing length in the udp header
self.socket.set_value(
self.machine_params['gencores'], 0, self.udp_length_offset,
imix_frame_sizes[0] -
self.frame_size_minus_udp_header_and_content, 2)
else:
if self.ipv6:
RapidLog.critical('IMIX not supported for IPV6')
prox_sizes = [frame_size - 4 for frame_size in imix_frame_sizes]
self.socket.set_imix(self.machine_params['gencores'], 0,
prox_sizes)
def lat_stats(self, cores, tasks=[0]):
result = {}
result['lat_min'] = 999999999
result['lat_max'] = result['lat_avg'] = 0
result['buckets'] = [0] * 128
result['mis_ordered'] = 0
result['extent'] = 0
result['duplicate'] = 0
number_tasks_returning_stats = 0
self._send('lat all stats %s %s' %
(','.join(map(str, cores)), ','.join(map(str, tasks))))
for core in cores:
for task in tasks:
stats = self._recv().split(',')
if 'is not measuring' in stats[0]:
continue
if stats[0].startswith('error'):
RapidLog.critical("lat stats error: unexpected reply from PROX\
(potential incompatibility between scripts and PROX)")
raise Exception("lat stats error")
number_tasks_returning_stats += 1
result['lat_min'] = min(int(stats[0]), result['lat_min'])
result['lat_max'] = max(int(stats[1]), result['lat_max'])
result['lat_avg'] += int(stats[2])
#min_since begin = int(stats[3])
#max_since_begin = int(stats[4])
result['lat_tsc'] = int(
stats[5]) # Taking the last tsc as the timestamp since
# PROX will return the same tsc for each
# core/task combination
result['lat_hz'] = int(stats[6])
#coreid = int(stats[7])
#taskid = int(stats[8])
result['mis_ordered'] += int(stats[9])
result['extent'] += int(stats[10])
result['duplicate'] += int(stats[11])
stats = self._recv().split(':')
if stats[0].startswith('error'):
RapidLog.critical("lat stats error: unexpected lat bucket \
reply (potential incompatibility between scripts \
and PROX)")
raise Exception("lat bucket reply error")
result['buckets'][0] = int(stats[1])
for i in range(1, 128):
stats = self._recv().split(':')
result['buckets'][i] = int(stats[1])
result['lat_avg'] = old_div(result['lat_avg'],
number_tasks_returning_stats)
self._send('stats latency(0).used')
used = float(self._recv())
self._send('stats latency(0).total')
total = float(self._recv())
result['lat_used'] = old_div(used, total)
return (result)
def lat_stats(self, cores, tasks=[0]):
min_lat = 999999999
max_lat = avg_lat = 0
number_tasks_returning_stats = 0
buckets = [0] * 128
self._send('lat all stats %s %s' %
(','.join(map(str, cores)), ','.join(map(str, tasks))))
for core in cores:
for task in tasks:
stats = self._recv().split(',')
if 'is not measuring' in stats[0]:
continue
if stats[0].startswith('error'):
RapidLog.critical("lat stats error: unexpected reply from PROX\
(potential incompatibility between scripts and PROX)")
raise Exception("lat stats error")
number_tasks_returning_stats += 1
min_lat = min(int(stats[0]), min_lat)
max_lat = max(int(stats[1]), max_lat)
avg_lat += int(stats[2])
#min_since begin = int(stats[3])
#max_since_begin = int(stats[4])
tsc = int(stats[5]) # Taking the last tsc as the timestamp since
# PROX will return the same tsc for each
# core/task combination
hz = int(stats[6])
#coreid = int(stats[7])
#taskid = int(stats[8])
stats = self._recv().split(':')
if stats[0].startswith('error'):
RapidLog.critical("lat stats error: unexpected lat bucket \
reply (potential incompatibility between scripts \
and PROX)")
raise Exception("lat bucket reply error")
buckets[0] = int(stats[1])
for i in range(1, 128):
stats = self._recv().split(':')
buckets[i] = int(stats[1])
avg_lat = old_div(avg_lat, number_tasks_returning_stats)
self._send('stats latency(0).used')
used = float(self._recv())
self._send('stats latency(0).total')
total = float(self._recv())
return (min_lat, max_lat, avg_lat, (old_div(used,
total)), tsc, hz, buckets)
def multi_port_stats(self, ports=[0]):
rx = tx = port_id = tsc = no_mbufs = errors = 0
self._send('multi port stats %s' % (','.join(map(str, ports))))
result = self._recv().split(';')
if result[0].startswith('error'):
RapidLog.critical("multi port stats error: unexpected invalid \
syntax (potential incompatibility between scripts and \
PROX)")
raise Exception("multi port stats error")
for statistics in result:
stats = statistics.split(',')
port_id = int(stats[0])
rx += int(stats[1])
tx += int(stats[2])
no_mbufs += int(stats[3])
errors += int(stats[4])
tsc = int(stats[5])
return rx, tx, no_mbufs, errors, tsc
def create_key(self):
if os.path.exists(self.key_name):
public_key_file = "{}.pub".format(self.key_name)
if not os.path.exists(public_key_file):
RapidLog.critical('Keypair {}.pub does not exist'.format(
self.key_name))
with open(public_key_file, mode='rb') as public_file:
public_key = public_file.read()
else:
public_key = None
keypair = self.nova_client.keypairs.create(name=self.key_name,
public_key=public_key)
# Create a file for writing that can only be read and written by owner
if not os.path.exists(self.key_name):
fp = os.open(self.key_name, os.O_WRONLY | os.O_CREAT, 0o600)
with os.fdopen(fp, 'w') as f:
f.write(keypair.private_key)
RapidLog.info('Keypair {} created'.format(self.key_name))
def core_stats(self, cores, tasks=[0]):
rx = tx = drop = tsc = hz = rx_non_dp = tx_non_dp = tx_fail = 0
self._send('dp core stats %s %s' %
(','.join(map(str, cores)), ','.join(map(str, tasks))))
for core in cores:
for task in tasks:
stats = self._recv().split(',')
if stats[0].startswith('error'):
if stats[0].startswith('error: invalid syntax'):
RapidLog.critical("dp core stats error: unexpected \
invalid syntax (potential incompatibility \
between scripts and PROX)")
raise Exception("dp core stats error")
continue
rx += int(stats[0])
tx += int(stats[1])
rx_non_dp += int(stats[2])
tx_non_dp += int(stats[3])
drop += int(stats[4])
tx_fail += int(stats[5])
tsc = int(stats[6])
hz = int(stats[7])
return rx, rx_non_dp, tx, tx_non_dp, drop, tx_fail, tsc, hz
def run_iteration(self, requested_duration, flow_number, size, speed):
BUCKET_SIZE_EXP = self.gen_machine.bucket_size_exp
LAT_PERCENTILE = self.test['lat_percentile']
r = 0
sleep_time = 2
while (r < self.test['maxr']):
time.sleep(sleep_time)
# Sleep_time is needed to be able to do accurate measurements to check for packet loss. We need to make this time large enough so that we do not take the first measurement while some packets from the previous tests migth still be in flight
t1_rx, t1_non_dp_rx, t1_tx, t1_non_dp_tx, t1_drop, t1_tx_fail, t1_tsc, abs_tsc_hz = self.gen_machine.core_stats(
)
t1_dp_rx = t1_rx - t1_non_dp_rx
t1_dp_tx = t1_tx - t1_non_dp_tx
self.gen_machine.set_generator_speed(0)
self.gen_machine.start_gen_cores()
if self.background_machines:
self.set_background_speed(self.background_machines, 0)
self.start_background_traffic(self.background_machines)
if 'ramp_step' in self.test.keys():
ramp_speed = self.test['ramp_step']
else:
ramp_speed = speed
while ramp_speed < speed:
self.gen_machine.set_generator_speed(ramp_speed)
if self.background_machines:
self.set_background_speed(self.background_machines,
ramp_speed)
time.sleep(2)
ramp_speed = ramp_speed + self.test['ramp_step']
self.gen_machine.set_generator_speed(speed)
if self.background_machines:
self.set_background_speed(self.background_machines, speed)
time.sleep(
2
) ## Needs to be 2 seconds since this 1 sec is the time that PROX uses to refresh the stats. Note that this can be changed in PROX!! Don't do it.
start_bg_gen_stats = []
for bg_gen_machine in self.background_machines:
bg_rx, bg_non_dp_rx, bg_tx, bg_non_dp_tx, _, _, bg_tsc, _ = bg_gen_machine.core_stats(
)
bg_gen_stat = {
"bg_dp_rx": bg_rx - bg_non_dp_rx,
"bg_dp_tx": bg_tx - bg_non_dp_tx,
"bg_tsc": bg_tsc
}
start_bg_gen_stats.append(dict(bg_gen_stat))
if self.sut_machine != None:
t2_sut_rx, t2_sut_non_dp_rx, t2_sut_tx, t2_sut_non_dp_tx, t2_sut_drop, t2_sut_tx_fail, t2_sut_tsc, sut_tsc_hz = self.sut_machine.core_stats(
)
t2_rx, t2_non_dp_rx, t2_tx, t2_non_dp_tx, t2_drop, t2_tx_fail, t2_tsc, tsc_hz = self.gen_machine.core_stats(
)
tx = t2_tx - t1_tx
dp_tx = tx - (t2_non_dp_tx - t1_non_dp_tx)
dp_rx = t2_rx - t1_rx - (t2_non_dp_rx - t1_non_dp_rx)
tot_dp_drop = dp_tx - dp_rx
if tx == 0:
RapidLog.critical(
"TX = 0. Test interrupted since no packet has been sent.")
if dp_tx == 0:
RapidLog.critical(
"Only non-dataplane packets (e.g. ARP) sent. Test interrupted since no packet has been sent."
)
# Ask PROX to calibrate the bucket size once we have a PROX function to do this.
# Measure latency statistics per second
lat_min, lat_max, lat_avg, used_avg, t2_lat_tsc, lat_hz, buckets = self.gen_machine.lat_stats(
)
lat_samples = sum(buckets)
sample_count = 0
for sample_percentile, bucket in enumerate(buckets, start=1):
sample_count += bucket
if sample_count > (lat_samples * LAT_PERCENTILE):
break
percentile_max = (sample_percentile == len(buckets))
sample_percentile = sample_percentile * float(
2**BUCKET_SIZE_EXP) / (old_div(float(lat_hz), float(10**6)))
if self.test['test'] == 'fixed_rate':
RapidLog.info(
self.report_result(flow_number, size, speed, None, None,
None, None, lat_avg, sample_percentile,
percentile_max, lat_max, dp_tx, dp_rx,
None, None))
tot_rx = tot_non_dp_rx = tot_tx = tot_non_dp_tx = tot_drop = 0
lat_avg = used_avg = 0
buckets_total = buckets
tot_lat_samples = sum(buckets)
tot_lat_measurement_duration = float(0)
tot_core_measurement_duration = float(0)
tot_sut_core_measurement_duration = float(0)
tot_sut_rx = tot_sut_non_dp_rx = tot_sut_tx = tot_sut_non_dp_tx = tot_sut_drop = tot_sut_tx_fail = tot_sut_tsc = 0
lat_avail = core_avail = sut_avail = False
while (tot_core_measurement_duration - float(requested_duration) <=
0.1) or (tot_lat_measurement_duration -
float(requested_duration) <= 0.1):
time.sleep(0.5)
lat_min_sample, lat_max_sample, lat_avg_sample, used_sample, t3_lat_tsc, lat_hz, buckets = self.gen_machine.lat_stats(
)
# Get statistics after some execution time
if t3_lat_tsc != t2_lat_tsc:
single_lat_measurement_duration = (
t3_lat_tsc - t2_lat_tsc
) * 1.0 / lat_hz # time difference between the 2 measurements, expressed in seconds.
# A second has passed in between to lat_stats requests. Hence we need to process the results
tot_lat_measurement_duration = tot_lat_measurement_duration + single_lat_measurement_duration
if lat_min > lat_min_sample:
lat_min = lat_min_sample
if lat_max < lat_max_sample:
lat_max = lat_max_sample
lat_avg = lat_avg + lat_avg_sample * single_lat_measurement_duration # Sometimes, There is more than 1 second between 2 lat_stats. Hence we will take the latest measurement
used_avg = used_avg + used_sample * single_lat_measurement_duration # and give it more weigth.
lat_samples = sum(buckets)
tot_lat_samples += lat_samples
sample_count = 0
for sample_percentile, bucket in enumerate(buckets,
start=1):
sample_count += bucket
if sample_count > lat_samples * LAT_PERCENTILE:
break
percentile_max = (sample_percentile == len(buckets))
bucket_size = float(2**BUCKET_SIZE_EXP) / (old_div(
float(lat_hz), float(10**6)))
sample_percentile = sample_percentile * bucket_size
buckets_total = [
buckets_total[i] + buckets[i]
for i in range(len(buckets_total))
]
t2_lat_tsc = t3_lat_tsc
lat_avail = True
t3_rx, t3_non_dp_rx, t3_tx, t3_non_dp_tx, t3_drop, t3_tx_fail, t3_tsc, tsc_hz = self.gen_machine.core_stats(
)
if t3_tsc != t2_tsc:
single_core_measurement_duration = (
t3_tsc - t2_tsc
) * 1.0 / tsc_hz # time difference between the 2 measurements, expressed in seconds.
tot_core_measurement_duration = tot_core_measurement_duration + single_core_measurement_duration
delta_rx = t3_rx - t2_rx
tot_rx += delta_rx
delta_non_dp_rx = t3_non_dp_rx - t2_non_dp_rx
tot_non_dp_rx += delta_non_dp_rx
delta_tx = t3_tx - t2_tx
tot_tx += delta_tx
delta_non_dp_tx = t3_non_dp_tx - t2_non_dp_tx
tot_non_dp_tx += delta_non_dp_tx
delta_dp_tx = delta_tx - delta_non_dp_tx
delta_dp_rx = delta_rx - delta_non_dp_rx
delta_dp_drop = delta_dp_tx - delta_dp_rx
tot_dp_drop += delta_dp_drop
delta_drop = t3_drop - t2_drop
tot_drop += delta_drop
t2_rx, t2_non_dp_rx, t2_tx, t2_non_dp_tx, t2_drop, t2_tx_fail, t2_tsc = t3_rx, t3_non_dp_rx, t3_tx, t3_non_dp_tx, t3_drop, t3_tx_fail, t3_tsc
core_avail = True
if self.sut_machine != None:
t3_sut_rx, t3_sut_non_dp_rx, t3_sut_tx, t3_sut_non_dp_tx, t3_sut_drop, t3_sut_tx_fail, t3_sut_tsc, sut_tsc_hz = self.sut_machine.core_stats(
)
if t3_sut_tsc != t2_sut_tsc:
single_sut_core_measurement_duration = (
t3_sut_tsc - t2_sut_tsc
) * 1.0 / tsc_hz # time difference between the 2 measurements, expressed in seconds.
tot_sut_core_measurement_duration = tot_sut_core_measurement_duration + single_sut_core_measurement_duration
tot_sut_rx += t3_sut_rx - t2_sut_rx
tot_sut_non_dp_rx += t3_sut_non_dp_rx - t2_sut_non_dp_rx
delta_sut_tx = t3_sut_tx - t2_sut_tx
tot_sut_tx += delta_sut_tx
delta_sut_non_dp_tx = t3_sut_non_dp_tx - t2_sut_non_dp_tx
tot_sut_non_dp_tx += delta_sut_non_dp_tx
t2_sut_rx, t2_sut_non_dp_rx, t2_sut_tx, t2_sut_non_dp_tx, t2_sut_drop, t2_sut_tx_fail, t2_sut_tsc = t3_sut_rx, t3_sut_non_dp_rx, t3_sut_tx, t3_sut_non_dp_tx, t3_sut_drop, t3_sut_tx_fail, t3_sut_tsc
sut_avail = True
if self.test['test'] == 'fixed_rate':
if lat_avail == core_avail == True:
lat_avail = core_avail = False
pps_req_tx = (
delta_tx + delta_drop - delta_rx
) / single_core_measurement_duration / 1000000
pps_tx = delta_tx / single_core_measurement_duration / 1000000
if self.sut_machine != None and sut_avail:
pps_sut_tx = delta_sut_tx / single_sut_core_measurement_duration / 1000000
sut_avail = False
else:
pps_sut_tx = None
pps_rx = delta_rx / single_core_measurement_duration / 1000000
RapidLog.info(
self.report_result(
flow_number, size, speed, pps_req_tx, pps_tx,
pps_sut_tx, pps_rx, lat_avg_sample,
sample_percentile, percentile_max,
lat_max_sample, delta_dp_tx, delta_dp_rx,
tot_dp_drop, single_core_measurement_duration))
variables = {
'Flows': flow_number,
'Size': size,
'RequestedSpeed': self.get_pps(speed, size),
'CoreGenerated': pps_req_tx,
'SentByNIC': pps_tx,
'FwdBySUT': pps_sut_tx,
'RevByCore': pps_rx,
'AvgLatency': lat_avg_sample,
'PCTLatency': sample_percentile,
'MaxLatency': lat_max_sample,
'PacketsSent': delta_dp_tx,
'PacketsReceived': delta_dp_rx,
'PacketsLost': tot_dp_drop,
'bucket_size': bucket_size,
'buckets': buckets
}
self.post_data('rapid_flowsizetest', variables)
end_bg_gen_stats = []
for bg_gen_machine in self.background_machines:
bg_rx, bg_non_dp_rx, bg_tx, bg_non_dp_tx, _, _, bg_tsc, bg_hz = bg_gen_machine.core_stats(
)
bg_gen_stat = {
"bg_dp_rx": bg_rx - bg_non_dp_rx,
"bg_dp_tx": bg_tx - bg_non_dp_tx,
"bg_tsc": bg_tsc,
"bg_hz": bg_hz
}
end_bg_gen_stats.append(dict(bg_gen_stat))
i = 0
bg_rates = []
while i < len(end_bg_gen_stats):
bg_rates.append(0.000001 *
(end_bg_gen_stats[i]['bg_dp_rx'] -
start_bg_gen_stats[i]['bg_dp_rx']) /
((end_bg_gen_stats[i]['bg_tsc'] -
start_bg_gen_stats[i]['bg_tsc']) * 1.0 /
end_bg_gen_stats[i]['bg_hz']))
i += 1
if len(bg_rates):
avg_bg_rate = sum(bg_rates) / len(bg_rates)
RapidLog.debug(
'Average Background traffic rate: {:>7.3f} Mpps'.format(
avg_bg_rate))
else:
avg_bg_rate = None
#Stop generating
self.gen_machine.stop_gen_cores()
r += 1
lat_avg = old_div(lat_avg, float(tot_lat_measurement_duration))
used_avg = old_div(used_avg, float(tot_lat_measurement_duration))
t4_tsc = t2_tsc
while t4_tsc == t2_tsc:
t4_rx, t4_non_dp_rx, t4_tx, t4_non_dp_tx, t4_drop, t4_tx_fail, t4_tsc, abs_tsc_hz = self.gen_machine.core_stats(
)
if self.test['test'] == 'fixed_rate':
t4_lat_tsc = t2_lat_tsc
while t4_lat_tsc == t2_lat_tsc:
lat_min_sample, lat_max_sample, lat_avg_sample, used_sample, t4_lat_tsc, lat_hz, buckets = self.gen_machine.lat_stats(
)
sample_count = 0
lat_samples = sum(buckets)
for percentile, bucket in enumerate(buckets, start=1):
sample_count += bucket
if sample_count > lat_samples * LAT_PERCENTILE:
break
percentile_max = (percentile == len(buckets))
percentile = percentile * bucket_size
lat_max = lat_max_sample
lat_avg = lat_avg_sample
delta_rx = t4_rx - t2_rx
delta_non_dp_rx = t4_non_dp_rx - t2_non_dp_rx
delta_tx = t4_tx - t2_tx
delta_non_dp_tx = t4_non_dp_tx - t2_non_dp_tx
delta_dp_tx = delta_tx - delta_non_dp_tx
delta_dp_rx = delta_rx - delta_non_dp_rx
dp_tx = delta_dp_tx
dp_rx = delta_dp_rx
tot_dp_drop += delta_dp_tx - delta_dp_rx
pps_req_tx = None
pps_tx = None
pps_sut_tx = None
pps_rx = None
drop_rate = 100.0 * (dp_tx - dp_rx) / dp_tx
tot_core_measurement_duration = None
break ## Not really needed since the while loop will stop when evaluating the value of r
else:
sample_count = 0
buckets = buckets_total
for percentile, bucket in enumerate(buckets_total, start=1):
sample_count += bucket
if sample_count > tot_lat_samples * LAT_PERCENTILE:
break
percentile_max = (percentile == len(buckets_total))
percentile = percentile * bucket_size
pps_req_tx = (
tot_tx + tot_drop - tot_rx
) / tot_core_measurement_duration / 1000000.0 # tot_drop is all packets dropped by all tasks. This includes packets dropped at the generator task + packets dropped by the nop task. In steady state, this equals to the number of packets received by this VM
pps_tx = tot_tx / tot_core_measurement_duration / 1000000.0 # tot_tx is all generated packets actually accepted by the interface
pps_rx = tot_rx / tot_core_measurement_duration / 1000000.0 # tot_rx is all packets received by the nop task = all packets received in the gen VM
if self.sut_machine != None and sut_avail:
pps_sut_tx = tot_sut_tx / tot_sut_core_measurement_duration / 1000000.0
else:
pps_sut_tx = None
dp_tx = (t4_tx - t1_tx) - (t4_non_dp_tx - t1_non_dp_tx)
dp_rx = (t4_rx - t1_rx) - (t4_non_dp_rx - t1_non_dp_rx)
tot_dp_drop = dp_tx - dp_rx
drop_rate = 100.0 * tot_dp_drop / dp_tx
if ((drop_rate < self.test['drop_rate_threshold']) or
(tot_dp_drop == self.test['drop_rate_threshold'] == 0)
or (tot_dp_drop > self.test['maxz'])):
break
return (pps_req_tx, pps_tx, pps_sut_tx, pps_rx, lat_avg, percentile,
percentile_max, lat_max, dp_tx, dp_rx, tot_dp_drop,
(t4_tx_fail - t1_tx_fail), drop_rate, lat_min, used_avg, r,
tot_core_measurement_duration, avg_bg_rate, bucket_size,
buckets)
def run_iteration(self, requested_duration, flow_number, size, speed):
BUCKET_SIZE_EXP = self.gen_machine.bucket_size_exp
sleep_time = self.test['sleep_time']
LAT_PERCENTILE = self.test['lat_percentile']
iteration_data = {}
time_loop_data = {}
iteration_data['r'] = 0
while (iteration_data['r'] < self.test['maxr']):
self.gen_machine.start_latency_cores()
time.sleep(sleep_time)
# Sleep_time is needed to be able to do accurate measurements to check for packet loss. We need to make this time large enough so that we do not take the first measurement while some packets from the previous tests migth still be in flight
t1_rx, t1_non_dp_rx, t1_tx, t1_non_dp_tx, t1_drop, t1_tx_fail, t1_tsc, abs_tsc_hz = self.gen_machine.core_stats(
)
t1_dp_rx = t1_rx - t1_non_dp_rx
t1_dp_tx = t1_tx - t1_non_dp_tx
self.gen_machine.set_generator_speed(0)
self.gen_machine.start_gen_cores()
self.set_background_speed(self.background_machines, 0)
self.start_background_traffic(self.background_machines)
if 'ramp_step' in self.test.keys():
ramp_speed = self.test['ramp_step']
else:
ramp_speed = speed
while ramp_speed < speed:
self.gen_machine.set_generator_speed(ramp_speed)
self.set_background_speed(self.background_machines, ramp_speed)
time.sleep(2)
ramp_speed = ramp_speed + self.test['ramp_step']
self.gen_machine.set_generator_speed(speed)
self.set_background_speed(self.background_machines, speed)
iteration_data['speed'] = speed
time_loop_data['speed'] = speed
time.sleep(
2
) ## Needs to be 2 seconds since this 1 sec is the time that PROX uses to refresh the stats. Note that this can be changed in PROX!! Don't do it.
start_bg_gen_stats = []
for bg_gen_machine in self.background_machines:
bg_rx, bg_non_dp_rx, bg_tx, bg_non_dp_tx, _, _, bg_tsc, _ = bg_gen_machine.core_stats(
)
bg_gen_stat = {
"bg_dp_rx": bg_rx - bg_non_dp_rx,
"bg_dp_tx": bg_tx - bg_non_dp_tx,
"bg_tsc": bg_tsc
}
start_bg_gen_stats.append(dict(bg_gen_stat))
if self.sut_machine != None:
t2_sut_rx, t2_sut_non_dp_rx, t2_sut_tx, t2_sut_non_dp_tx, t2_sut_drop, t2_sut_tx_fail, t2_sut_tsc, sut_tsc_hz = self.sut_machine.core_stats(
)
t2_rx, t2_non_dp_rx, t2_tx, t2_non_dp_tx, t2_drop, t2_tx_fail, t2_tsc, tsc_hz = self.gen_machine.core_stats(
)
tx = t2_tx - t1_tx
iteration_data['abs_tx'] = tx - (t2_non_dp_tx - t1_non_dp_tx)
iteration_data['abs_rx'] = t2_rx - t1_rx - (t2_non_dp_rx -
t1_non_dp_rx)
iteration_data['abs_dropped'] = iteration_data[
'abs_tx'] - iteration_data['abs_rx']
if tx == 0:
RapidLog.critical(
"TX = 0. Test interrupted since no packet has been sent.")
if iteration_data['abs_tx'] == 0:
RapidLog.critical(
"Only non-dataplane packets (e.g. ARP) sent. Test interrupted since no packet has been sent."
)
# Ask PROX to calibrate the bucket size once we have a PROX function to do this.
# Measure latency statistics per second
iteration_data.update(self.gen_machine.lat_stats())
t2_lat_tsc = iteration_data['lat_tsc']
sample_count = 0
for sample_percentile, bucket in enumerate(
iteration_data['buckets'], start=1):
sample_count += bucket
if sample_count > sum(
iteration_data['buckets']) * LAT_PERCENTILE:
break
iteration_data['lat_perc_max'] = (sample_percentile == len(
iteration_data['buckets']))
iteration_data['bucket_size'] = float(2**BUCKET_SIZE_EXP) / (
old_div(float(iteration_data['lat_hz']), float(10**6)))
time_loop_data['bucket_size'] = iteration_data['bucket_size']
iteration_data[
'lat_perc'] = sample_percentile * iteration_data['bucket_size']
if self.test['test'] == 'fixed_rate':
iteration_data['pps_req_tx'] = None
iteration_data['pps_tx'] = None
iteration_data['pps_sut_tx'] = None
iteration_data['pps_rx'] = None
iteration_data['lat_perc'] = None
iteration_data['actual_duration'] = None
iteration_prefix = {
'speed': '',
'lat_avg': '',
'lat_perc': '',
'lat_max': '',
'abs_drop_rate': '',
'drop_rate': ''
}
RapidLog.info(
self.report_result(flow_number, size, iteration_data,
iteration_prefix))
tot_rx = tot_non_dp_rx = tot_tx = tot_non_dp_tx = tot_drop = 0
iteration_data['lat_avg'] = iteration_data['lat_used'] = 0
tot_lat_measurement_duration = float(0)
iteration_data['actual_duration'] = float(0)
tot_sut_core_measurement_duration = float(0)
tot_sut_rx = tot_sut_non_dp_rx = tot_sut_tx = tot_sut_non_dp_tx = tot_sut_drop = tot_sut_tx_fail = tot_sut_tsc = 0
lat_avail = core_avail = sut_avail = False
while (iteration_data['actual_duration'] -
float(requested_duration) <= 0.1) or (
tot_lat_measurement_duration - float(requested_duration)
<= 0.1):
time.sleep(0.5)
time_loop_data.update(self.gen_machine.lat_stats())
# Get statistics after some execution time
if time_loop_data['lat_tsc'] != t2_lat_tsc:
single_lat_measurement_duration = (
time_loop_data['lat_tsc'] - t2_lat_tsc
) * 1.0 / time_loop_data[
'lat_hz'] # time difference between the 2 measurements, expressed in seconds.
# A second has passed in between to lat_stats requests. Hence we need to process the results
tot_lat_measurement_duration = tot_lat_measurement_duration + single_lat_measurement_duration
if iteration_data['lat_min'] > time_loop_data['lat_min']:
iteration_data['lat_min'] = time_loop_data['lat_min']
if iteration_data['lat_max'] < time_loop_data['lat_max']:
iteration_data['lat_max'] = time_loop_data['lat_max']
iteration_data['lat_avg'] = iteration_data[
'lat_avg'] + time_loop_data[
'lat_avg'] * single_lat_measurement_duration # Sometimes, There is more than 1 second between 2 lat_stats. Hence we will take the latest measurement
iteration_data['lat_used'] = iteration_data[
'lat_used'] + time_loop_data[
'lat_used'] * single_lat_measurement_duration # and give it more weigth.
sample_count = 0
for sample_percentile, bucket in enumerate(
time_loop_data['buckets'], start=1):
sample_count += bucket
if sample_count > sum(
time_loop_data['buckets']) * LAT_PERCENTILE:
break
time_loop_data['lat_perc_max'] = (sample_percentile == len(
time_loop_data['buckets']))
time_loop_data[
'lat_perc'] = sample_percentile * iteration_data[
'bucket_size']
iteration_data['buckets'] = [
iteration_data['buckets'][i] +
time_loop_data['buckets'][i]
for i in range(len(iteration_data['buckets']))
]
t2_lat_tsc = time_loop_data['lat_tsc']
lat_avail = True
t3_rx, t3_non_dp_rx, t3_tx, t3_non_dp_tx, t3_drop, t3_tx_fail, t3_tsc, tsc_hz = self.gen_machine.core_stats(
)
if t3_tsc != t2_tsc:
time_loop_data['actual_duration'] = (
t3_tsc - t2_tsc
) * 1.0 / tsc_hz # time difference between the 2 measurements, expressed in seconds.
iteration_data['actual_duration'] = iteration_data[
'actual_duration'] + time_loop_data['actual_duration']
delta_rx = t3_rx - t2_rx
tot_rx += delta_rx
delta_non_dp_rx = t3_non_dp_rx - t2_non_dp_rx
tot_non_dp_rx += delta_non_dp_rx
delta_tx = t3_tx - t2_tx
tot_tx += delta_tx
delta_non_dp_tx = t3_non_dp_tx - t2_non_dp_tx
tot_non_dp_tx += delta_non_dp_tx
delta_dp_tx = delta_tx - delta_non_dp_tx
delta_dp_rx = delta_rx - delta_non_dp_rx
time_loop_data['abs_dropped'] = delta_dp_tx - delta_dp_rx
iteration_data['abs_dropped'] += time_loop_data[
'abs_dropped']
delta_drop = t3_drop - t2_drop
tot_drop += delta_drop
t2_rx, t2_non_dp_rx, t2_tx, t2_non_dp_tx, t2_drop, t2_tx_fail, t2_tsc = t3_rx, t3_non_dp_rx, t3_tx, t3_non_dp_tx, t3_drop, t3_tx_fail, t3_tsc
core_avail = True
if self.sut_machine != None:
t3_sut_rx, t3_sut_non_dp_rx, t3_sut_tx, t3_sut_non_dp_tx, t3_sut_drop, t3_sut_tx_fail, t3_sut_tsc, sut_tsc_hz = self.sut_machine.core_stats(
)
if t3_sut_tsc != t2_sut_tsc:
single_sut_core_measurement_duration = (
t3_sut_tsc - t2_sut_tsc
) * 1.0 / sut_tsc_hz # time difference between the 2 measurements, expressed in seconds.
tot_sut_core_measurement_duration = tot_sut_core_measurement_duration + single_sut_core_measurement_duration
tot_sut_rx += t3_sut_rx - t2_sut_rx
tot_sut_non_dp_rx += t3_sut_non_dp_rx - t2_sut_non_dp_rx
delta_sut_tx = t3_sut_tx - t2_sut_tx
tot_sut_tx += delta_sut_tx
delta_sut_non_dp_tx = t3_sut_non_dp_tx - t2_sut_non_dp_tx
tot_sut_non_dp_tx += delta_sut_non_dp_tx
t2_sut_rx, t2_sut_non_dp_rx, t2_sut_tx, t2_sut_non_dp_tx, t2_sut_drop, t2_sut_tx_fail, t2_sut_tsc = t3_sut_rx, t3_sut_non_dp_rx, t3_sut_tx, t3_sut_non_dp_tx, t3_sut_drop, t3_sut_tx_fail, t3_sut_tsc
sut_avail = True
if self.test['test'] == 'fixed_rate':
if lat_avail == core_avail == True:
lat_avail = core_avail = False
time_loop_data['pps_req_tx'] = (
delta_tx + delta_drop - delta_rx
) / time_loop_data['actual_duration'] / 1000000
time_loop_data['pps_tx'] = delta_tx / time_loop_data[
'actual_duration'] / 1000000
if self.sut_machine != None and sut_avail:
time_loop_data[
'pps_sut_tx'] = delta_sut_tx / single_sut_core_measurement_duration / 1000000
sut_avail = False
else:
time_loop_data['pps_sut_tx'] = None
time_loop_data['pps_rx'] = delta_rx / time_loop_data[
'actual_duration'] / 1000000
time_loop_data['abs_tx'] = delta_dp_tx
time_loop_data['abs_rx'] = delta_dp_rx
time_loop_prefix = {
'speed': '',
'lat_avg': '',
'lat_perc': '',
'lat_max': '',
'abs_drop_rate': '',
'drop_rate': ''
}
RapidLog.info(
self.report_result(flow_number, size,
time_loop_data,
time_loop_prefix))
time_loop_data['test'] = self.test['testname']
time_loop_data['environment_file'] = self.test[
'environment_file']
time_loop_data['Flows'] = flow_number
time_loop_data['Size'] = size
time_loop_data['RequestedSpeed'] = RapidTest.get_pps(
speed, size)
_ = self.post_data(time_loop_data)
end_bg_gen_stats = []
for bg_gen_machine in self.background_machines:
bg_rx, bg_non_dp_rx, bg_tx, bg_non_dp_tx, _, _, bg_tsc, bg_hz = bg_gen_machine.core_stats(
)
bg_gen_stat = {
"bg_dp_rx": bg_rx - bg_non_dp_rx,
"bg_dp_tx": bg_tx - bg_non_dp_tx,
"bg_tsc": bg_tsc,
"bg_hz": bg_hz
}
end_bg_gen_stats.append(dict(bg_gen_stat))
self.stop_background_traffic(self.background_machines)
i = 0
bg_rates = []
while i < len(end_bg_gen_stats):
bg_rates.append(0.000001 *
(end_bg_gen_stats[i]['bg_dp_rx'] -
start_bg_gen_stats[i]['bg_dp_rx']) /
((end_bg_gen_stats[i]['bg_tsc'] -
start_bg_gen_stats[i]['bg_tsc']) * 1.0 /
end_bg_gen_stats[i]['bg_hz']))
i += 1
if len(bg_rates):
iteration_data['avg_bg_rate'] = sum(bg_rates) / len(bg_rates)
RapidLog.debug(
'Average Background traffic rate: {:>7.3f} Mpps'.format(
iteration_data['avg_bg_rate']))
else:
iteration_data['avg_bg_rate'] = None
#Stop generating
self.gen_machine.stop_gen_cores()
time.sleep(3.5)
self.gen_machine.stop_latency_cores()
iteration_data['r'] += 1
iteration_data['lat_avg'] = old_div(
iteration_data['lat_avg'], float(tot_lat_measurement_duration))
iteration_data['lat_used'] = old_div(
iteration_data['lat_used'],
float(tot_lat_measurement_duration))
t4_tsc = t2_tsc
while t4_tsc == t2_tsc:
t4_rx, t4_non_dp_rx, t4_tx, t4_non_dp_tx, t4_drop, t4_tx_fail, t4_tsc, abs_tsc_hz = self.gen_machine.core_stats(
)
if self.test['test'] == 'fixed_rate':
iteration_data['lat_tsc'] = t2_lat_tsc
while iteration_data['lat_tsc'] == t2_lat_tsc:
iteration_data.update(self.gen_machine.lat_stats())
sample_count = 0
for percentile, bucket in enumerate(iteration_data['buckets'],
start=1):
sample_count += bucket
if sample_count > sum(
iteration_data['buckets']) * LAT_PERCENTILE:
break
iteration_data['lat_perc_max'] = (percentile == len(
iteration_data['buckets']))
iteration_data[
'lat_perc'] = percentile * iteration_data['bucket_size']
delta_rx = t4_rx - t2_rx
delta_non_dp_rx = t4_non_dp_rx - t2_non_dp_rx
delta_tx = t4_tx - t2_tx
delta_non_dp_tx = t4_non_dp_tx - t2_non_dp_tx
delta_dp_tx = delta_tx - delta_non_dp_tx
delta_dp_rx = delta_rx - delta_non_dp_rx
iteration_data['abs_tx'] = delta_dp_tx
iteration_data['abs_rx'] = delta_dp_rx
iteration_data['abs_dropped'] += delta_dp_tx - delta_dp_rx
iteration_data['pps_req_tx'] = None
iteration_data['pps_tx'] = None
iteration_data['pps_sut_tx'] = None
iteration_data['drop_rate'] = 100.0 * (
iteration_data['abs_tx'] -
iteration_data['abs_rx']) / iteration_data['abs_tx']
iteration_data['actual_duration'] = None
break ## Not really needed since the while loop will stop when evaluating the value of r
else:
sample_count = 0
for percentile, bucket in enumerate(iteration_data['buckets'],
start=1):
sample_count += bucket
if sample_count > sum(
iteration_data['buckets']) * LAT_PERCENTILE:
break
iteration_data['lat_perc_max'] = (percentile == len(
iteration_data['buckets']))
iteration_data[
'lat_perc'] = percentile * iteration_data['bucket_size']
iteration_data['pps_req_tx'] = (
tot_tx + tot_drop - tot_rx
) / iteration_data[
'actual_duration'] / 1000000.0 # tot_drop is all packets dropped by all tasks. This includes packets dropped at the generator task + packets dropped by the nop task. In steady state, this equals to the number of packets received by this VM
iteration_data['pps_tx'] = tot_tx / iteration_data[
'actual_duration'] / 1000000.0 # tot_tx is all generated packets actually accepted by the interface
iteration_data['pps_rx'] = tot_rx / iteration_data[
'actual_duration'] / 1000000.0 # tot_rx is all packets received by the nop task = all packets received in the gen VM
if self.sut_machine != None and sut_avail:
iteration_data[
'pps_sut_tx'] = tot_sut_tx / tot_sut_core_measurement_duration / 1000000.0
else:
iteration_data['pps_sut_tx'] = None
iteration_data['abs_tx'] = (t4_tx - t1_tx) - (t4_non_dp_tx -
t1_non_dp_tx)
iteration_data['abs_rx'] = (t4_rx - t1_rx) - (t4_non_dp_rx -
t1_non_dp_rx)
iteration_data['abs_dropped'] = iteration_data[
'abs_tx'] - iteration_data['abs_rx']
iteration_data['drop_rate'] = 100.0 * iteration_data[
'abs_dropped'] / iteration_data['abs_tx']
if ((iteration_data['drop_rate'] <
self.test['drop_rate_threshold'])
or (iteration_data['abs_dropped'] ==
self.test['drop_rate_threshold'] == 0) or
(iteration_data['abs_dropped'] > self.test['maxz'])):
break
self.gen_machine.stop_latency_cores()
iteration_data['abs_tx_fail'] = t4_tx_fail - t1_tx_fail
return (iteration_data)
def run_iteration(self, requested_duration, flow_number, size, speed):
BUCKET_SIZE_EXP = self.gen_machine.bucket_size_exp
LAT_PERCENTILE = self.test['lat_percentile']
r = 0
sleep_time = 2
while (r < self.test['maxr']):
time.sleep(sleep_time)
# Sleep_time is needed to be able to do accurate measurements to check for packet loss. We need to make this time large enough so that we do not take the first measurement while some packets from the previous tests migth still be in flight
t1_rx, t1_non_dp_rx, t1_tx, t1_non_dp_tx, t1_drop, t1_tx_fail, t1_tsc, abs_tsc_hz = self.gen_machine.core_stats(
)
t1_dp_rx = t1_rx - t1_non_dp_rx
t1_dp_tx = t1_tx - t1_non_dp_tx
self.gen_machine.start_gen_cores()
time.sleep(
2
) ## Needs to be 2 seconds since this 1 sec is the time that PROX uses to refresh the stats. Note that this can be changed in PROX!! Don't do it.
if self.sut_machine != None:
t2_sut_rx, t2_sut_non_dp_rx, t2_sut_tx, t2_sut_non_dp_tx, t2_sut_drop, t2_sut_tx_fail, t2_sut_tsc, sut_tsc_hz = self.sut_machine.core_stats(
)
t2_rx, t2_non_dp_rx, t2_tx, t2_non_dp_tx, t2_drop, t2_tx_fail, t2_tsc, tsc_hz = self.gen_machine.core_stats(
)
tx = t2_tx - t1_tx
dp_tx = tx - (t2_non_dp_tx - t1_non_dp_tx)
dp_rx = t2_rx - t1_rx - (t2_non_dp_rx - t1_non_dp_rx)
tot_dp_drop = dp_tx - dp_rx
if tx == 0:
RapidLog.critical(
"TX = 0. Test interrupted since no packet has been sent.")
if dp_tx == 0:
RapidLog.critical(
"Only non-dataplane packets (e.g. ARP) sent. Test interrupted since no packet has been sent."
)
# Ask PROX to calibrate the bucket size once we have a PROX function to do this.
# Measure latency statistics per second
lat_min, lat_max, lat_avg, used_avg, t2_lat_tsc, lat_hz, buckets = self.gen_machine.lat_stats(
)
lat_samples = sum(buckets)
sample_count = 0
for sample_percentile, bucket in enumerate(buckets, start=1):
sample_count += bucket
if sample_count > (lat_samples * LAT_PERCENTILE):
break
percentile_max = (sample_percentile == len(buckets))
sample_percentile = sample_percentile * float(
2**BUCKET_SIZE_EXP) / (old_div(float(lat_hz), float(10**6)))
if self.test['test'] == 'fixed_rate':
RapidLog.info(
self.report_result(flow_number, size, speed, None, None,
None, None, lat_avg, sample_percentile,
percentile_max, lat_max, dp_tx, dp_rx,
None, None))
tot_rx = tot_non_dp_rx = tot_tx = tot_non_dp_tx = tot_drop = 0
lat_avg = used_avg = 0
buckets_total = [0] * 128
tot_lat_samples = 0
tot_lat_measurement_duration = float(0)
tot_core_measurement_duration = float(0)
tot_sut_core_measurement_duration = float(0)
tot_sut_rx = tot_sut_non_dp_rx = tot_sut_tx = tot_sut_non_dp_tx = tot_sut_drop = tot_sut_tx_fail = tot_sut_tsc = 0
lat_avail = core_avail = sut_avail = False
while (tot_core_measurement_duration - float(requested_duration) <=
0.1) or (tot_lat_measurement_duration -
float(requested_duration) <= 0.1):
time.sleep(0.5)
lat_min_sample, lat_max_sample, lat_avg_sample, used_sample, t3_lat_tsc, lat_hz, buckets = self.gen_machine.lat_stats(
)
# Get statistics after some execution time
if t3_lat_tsc != t2_lat_tsc:
single_lat_measurement_duration = (
t3_lat_tsc - t2_lat_tsc
) * 1.0 / lat_hz # time difference between the 2 measurements, expressed in seconds.
# A second has passed in between to lat_stats requests. Hence we need to process the results
tot_lat_measurement_duration = tot_lat_measurement_duration + single_lat_measurement_duration
if lat_min > lat_min_sample:
lat_min = lat_min_sample
if lat_max < lat_max_sample:
lat_max = lat_max_sample
lat_avg = lat_avg + lat_avg_sample * single_lat_measurement_duration # Sometimes, There is more than 1 second between 2 lat_stats. Hence we will take the latest measurement
used_avg = used_avg + used_sample * single_lat_measurement_duration # and give it more weigth.
lat_samples = sum(buckets)
tot_lat_samples += lat_samples
sample_count = 0
for sample_percentile, bucket in enumerate(buckets,
start=1):
sample_count += bucket
if sample_count > lat_samples * LAT_PERCENTILE:
break
percentile_max = (sample_percentile == len(buckets))
sample_percentile = sample_percentile * float(
2**BUCKET_SIZE_EXP) / (old_div(float(lat_hz),
float(10**6)))
buckets_total = [
buckets_total[i] + buckets[i]
for i in range(len(buckets_total))
]
t2_lat_tsc = t3_lat_tsc
lat_avail = True
t3_rx, t3_non_dp_rx, t3_tx, t3_non_dp_tx, t3_drop, t3_tx_fail, t3_tsc, tsc_hz = self.gen_machine.core_stats(
)
if t3_tsc != t2_tsc:
single_core_measurement_duration = (
t3_tsc - t2_tsc
) * 1.0 / tsc_hz # time difference between the 2 measurements, expressed in seconds.
tot_core_measurement_duration = tot_core_measurement_duration + single_core_measurement_duration
delta_rx = t3_rx - t2_rx
tot_rx += delta_rx
delta_non_dp_rx = t3_non_dp_rx - t2_non_dp_rx
tot_non_dp_rx += delta_non_dp_rx
delta_tx = t3_tx - t2_tx
tot_tx += delta_tx
delta_non_dp_tx = t3_non_dp_tx - t2_non_dp_tx
tot_non_dp_tx += delta_non_dp_tx
delta_dp_tx = delta_tx - delta_non_dp_tx
delta_dp_rx = delta_rx - delta_non_dp_rx
delta_dp_drop = delta_dp_tx - delta_dp_rx
tot_dp_drop += delta_dp_drop
delta_drop = t3_drop - t2_drop
tot_drop += delta_drop
t2_rx, t2_non_dp_rx, t2_tx, t2_non_dp_tx, t2_drop, t2_tx_fail, t2_tsc = t3_rx, t3_non_dp_rx, t3_tx, t3_non_dp_tx, t3_drop, t3_tx_fail, t3_tsc
core_avail = True
if self.sut_machine != None:
t3_sut_rx, t3_sut_non_dp_rx, t3_sut_tx, t3_sut_non_dp_tx, t3_sut_drop, t3_sut_tx_fail, t3_sut_tsc, sut_tsc_hz = self.sut_machine.core_stats(
)
if t3_sut_tsc != t2_sut_tsc:
single_sut_core_measurement_duration = (
t3_sut_tsc - t2_sut_tsc
) * 1.0 / tsc_hz # time difference between the 2 measurements, expressed in seconds.
tot_sut_core_measurement_duration = tot_sut_core_measurement_duration + single_sut_core_measurement_duration
tot_sut_rx += t3_sut_rx - t2_sut_rx
tot_sut_non_dp_rx += t3_sut_non_dp_rx - t2_sut_non_dp_rx
delta_sut_tx = t3_sut_tx - t2_sut_tx
tot_sut_tx += delta_sut_tx
delta_sut_non_dp_tx = t3_sut_non_dp_tx - t2_sut_non_dp_tx
tot_sut_non_dp_tx += delta_sut_non_dp_tx
t2_sut_rx, t2_sut_non_dp_rx, t2_sut_tx, t2_sut_non_dp_tx, t2_sut_drop, t2_sut_tx_fail, t2_sut_tsc = t3_sut_rx, t3_sut_non_dp_rx, t3_sut_tx, t3_sut_non_dp_tx, t3_sut_drop, t3_sut_tx_fail, t3_sut_tsc
sut_avail = True
if self.test['test'] == 'fixed_rate':
if lat_avail == core_avail == True:
lat_avail = core_avail = False
pps_req_tx = (
delta_tx + delta_drop - delta_rx
) / single_core_measurement_duration / 1000000
pps_tx = delta_tx / single_core_measurement_duration / 1000000
if self.sut_machine != None and sut_avail:
pps_sut_tx = delta_sut_tx / single_sut_core_measurement_duration / 1000000
sut_avail = False
else:
pps_sut_tx = None
pps_rx = delta_rx / single_core_measurement_duration / 1000000
RapidLog.info(
self.report_result(
flow_number, size, speed, pps_req_tx, pps_tx,
pps_sut_tx, pps_rx, lat_avg_sample,
sample_percentile, percentile_max,
lat_max_sample, delta_dp_tx, delta_dp_rx,
tot_dp_drop, single_core_measurement_duration))
#Stop generating
self.gen_machine.stop_gen_cores()
r += 1
lat_avg = old_div(lat_avg, float(tot_lat_measurement_duration))
used_avg = old_div(used_avg, float(tot_lat_measurement_duration))
t4_tsc = t2_tsc
while t4_tsc == t2_tsc:
t4_rx, t4_non_dp_rx, t4_tx, t4_non_dp_tx, t4_drop, t4_tx_fail, t4_tsc, abs_tsc_hz = self.gen_machine.core_stats(
)
if self.test['test'] == 'fixed_rate':
t4_lat_tsc = t2_lat_tsc
while t4_lat_tsc == t2_lat_tsc:
lat_min_sample, lat_max_sample, lat_avg_sample, used_sample, t4_lat_tsc, lat_hz, buckets = self.gen_machine.lat_stats(
)
sample_count = 0
lat_samples = sum(buckets)
for percentile, bucket in enumerate(buckets, start=1):
sample_count += bucket
if sample_count > lat_samples * LAT_PERCENTILE:
break
percentile_max = (percentile == len(buckets))
percentile = percentile * float(2**BUCKET_SIZE_EXP) / (old_div(
float(lat_hz), float(10**6)))
lat_max = lat_max_sample
lat_avg = lat_avg_sample
delta_rx = t4_rx - t2_rx
delta_non_dp_rx = t4_non_dp_rx - t2_non_dp_rx
delta_tx = t4_tx - t2_tx
delta_non_dp_tx = t4_non_dp_tx - t2_non_dp_tx
delta_dp_tx = delta_tx - delta_non_dp_tx
delta_dp_rx = delta_rx - delta_non_dp_rx
dp_tx = delta_dp_tx
dp_rx = delta_dp_rx
tot_dp_drop += delta_dp_tx - delta_dp_rx
pps_req_tx = None
pps_tx = None
pps_sut_tx = None
pps_rx = None
drop_rate = 100.0 * (dp_tx - dp_rx) / dp_tx
tot_core_measurement_duration = None
break ## Not really needed since the while loop will stop when evaluating the value of r
else:
sample_count = 0
for percentile, bucket in enumerate(buckets_total, start=1):
sample_count += bucket
if sample_count > tot_lat_samples * LAT_PERCENTILE:
break
percentile_max = (percentile == len(buckets_total))
percentile = percentile * float(2**BUCKET_SIZE_EXP) / (old_div(
float(lat_hz), float(10**6)))
pps_req_tx = (
tot_tx + tot_drop - tot_rx
) / tot_core_measurement_duration / 1000000.0 # tot_drop is all packets dropped by all tasks. This includes packets dropped at the generator task + packets dropped by the nop task. In steady state, this equals to the number of packets received by this VM
pps_tx = tot_tx / tot_core_measurement_duration / 1000000.0 # tot_tx is all generated packets actually accepted by the interface
pps_rx = tot_rx / tot_core_measurement_duration / 1000000.0 # tot_rx is all packets received by the nop task = all packets received in the gen VM
if self.sut_machine != None and sut_avail:
pps_sut_tx = tot_sut_tx / tot_sut_core_measurement_duration / 1000000.0
else:
pps_sut_tx = None
dp_tx = (t4_tx - t1_tx) - (t4_non_dp_tx - t1_non_dp_tx)
dp_rx = (t4_rx - t1_rx) - (t4_non_dp_rx - t1_non_dp_rx)
tot_dp_drop = dp_tx - dp_rx
drop_rate = 100.0 * tot_dp_drop / dp_tx
if ((drop_rate < self.test['drop_rate_threshold']) or
(tot_dp_drop == self.test['drop_rate_threshold'] == 0)
or (tot_dp_drop > self.test['maxz'])):
break
return (pps_req_tx, pps_tx, pps_sut_tx, pps_rx, lat_avg, percentile,
percentile_max, lat_max, dp_tx, dp_rx, tot_dp_drop,
(t4_tx_fail - t1_tx_fail), drop_rate, lat_min, used_avg, r,
tot_core_measurement_duration)