def getBwDifferential(self):
self.iat_length_tuples = [l for l in self.iat_length_tuples if len(l) > 0]
if time.time() - self.session.startTime > 0.2 and len(self.iat_length_tuples) > 1:
lengths0 = [t[1] for t in self.iat_length_tuples[-2]]
lengths1 = [t[1] for t in self.iat_length_tuples[-1]]
bw0 = sum(lengths0) / self._period
bw1 = sum(lengths1) / self._period
bw_diff = (bw1 - bw0) / self._period
self.session.bw_diffs.append(bw_diff)
log.debug("[bwdiff %s] - bw diffs: %s", self.end, self.session.bw_diffs)
log.debug("[bwdiff %s] - abs bwdiff (%s) > threshold (%s)",
self.end, abs(bw_diff), self._threshold)
if abs(bw_diff) > self._threshold:
# we should sample uniformly from the passed iats
# convert self.iat_length_tuples to distribution
# and pass it to wfpad iat distribution as dict.
iats = gu.get_iats([t[0] for t in gu.flatten_list(self.iat_length_tuples)])
h = {iat: 1 for iat in iats}
self._burstHistoProbdist['snd'] = hs.new(h)
self._gapHistoProbdist['snd'] = hs.new(h)
else:
self._burstHistoProbdist['snd'] = hs.uniform(const.INF_LABEL)
self._gapHistoProbdist['snd'] = hs.uniform(const.INF_LABEL)
self.iat_length_tuples.append([])
log.debug("[bwdiff %s] A period has passed: %s", self.end, self.iat_length_tuples[-3:-1])
if self.isVisiting():
log.debug("[bwdiff %s] Calling next period (visiting = %s, padding = %s)",
self.end, self.isVisiting(), self.session.is_padding)
cm.deferLater(self._period, self.getBwDifferential)
def getAverageTs(self):
avg_ts = mu.median(
gu.get_iats(
[t[0] for t in gu.flatten_list(self.iat_length_tuples)]))
log.debug("[bwdiff - %s] Average ts in the session is: %s.", self.end,
avg_ts)
return avg_ts
def estimate_rho(self, rho_star):
"""Estimate new value of rho based on past network performance."""
#log.debug("[cs-buflo] rho stats: %s" % self._rho_stats)
time_intervals = gu.flatten_list([gu.apply_consecutive_elements(burst_list, lambda x, y: (y - x) * const.SCALE)
for burst_list in self._rho_stats])
#log.debug("[cs-buflo] Time intervals = %s", time_intervals)
if len(time_intervals) == 0:
return rho_star
else:
return math.pow(2, math.floor(math.log(mu.median(time_intervals), 2)))
def estimate_rho(self, rho_star):
"""Estimate new value of rho based on past network performance."""
#log.debug("[cs-buflo] rho stats: %s" % self._rho_stats)
time_intervals = gu.flatten_list([
gu.apply_consecutive_elements(burst_list, lambda x, y:
(y - x) * const.SCALE)
for burst_list in self._rho_stats
])
#log.debug("[cs-buflo] Time intervals = %s", time_intervals)
if len(time_intervals) == 0:
return rho_star
else:
return math.pow(2,
math.floor(math.log(mu.median(time_intervals), 2)))
def getBwDifferential(self):
self.iat_length_tuples = [
l for l in self.iat_length_tuples if len(l) > 0
]
if time.time() - self.session.startTime > 0.2 and len(
self.iat_length_tuples) > 1:
lengths0 = [t[1] for t in self.iat_length_tuples[-2]]
lengths1 = [t[1] for t in self.iat_length_tuples[-1]]
bw0 = sum(lengths0) / self._period
bw1 = sum(lengths1) / self._period
bw_diff = (bw1 - bw0) / self._period
self.session.bw_diffs.append(bw_diff)
log.debug("[bwdiff %s] - bw diffs: %s", self.end,
self.session.bw_diffs)
log.debug("[bwdiff %s] - abs bwdiff (%s) > threshold (%s)",
self.end, abs(bw_diff), self._threshold)
if abs(bw_diff) > self._threshold:
# we should sample uniformly from the passed iats
# convert self.iat_length_tuples to distribution
# and pass it to wfpad iat distribution as dict.
iats = gu.get_iats(
[t[0] for t in gu.flatten_list(self.iat_length_tuples)])
h = {iat: 1 for iat in iats}
self._burstHistoProbdist['snd'] = hs.new(h)
self._gapHistoProbdist['snd'] = hs.new(h)
else:
self._burstHistoProbdist['snd'] = hs.uniform(const.INF_LABEL)
self._gapHistoProbdist['snd'] = hs.uniform(const.INF_LABEL)
self.iat_length_tuples.append([])
log.debug("[bwdiff %s] A period has passed: %s", self.end,
self.iat_length_tuples[-3:-1])
if self.isVisiting():
log.debug(
"[bwdiff %s] Calling next period (visiting = %s, padding = %s)",
self.end, self.isVisiting(), self.session.is_padding)
cm.deferLater(self._period, self.getBwDifferential)
def getAverageTs(self):
avg_ts = mu.median(gu.get_iats([t[0] for t in gu.flatten_list(self.iat_length_tuples)]))
log.debug("[bwdiff - %s] Average ts in the session is: %s.",
self.end, avg_ts)
return avg_ts
