Commit 090507ee authored by juga's avatar juga
Browse files

Implement method to scale as Torflow

parent 5a9be7c5
...@@ -481,9 +481,196 @@ class V3BWFile(object): ...@@ -481,9 +481,196 @@ class V3BWFile(object):
'allowed', (1 - accuracy_ratio) * 100, margin * 100) 'allowed', (1 - accuracy_ratio) * 100, margin * 100)
@staticmethod @staticmethod
def bw_lines_torflow(bw_lines, desc_obs_bws=TORFLOW_OBS_LAST, def bw_torflow_scale(bw_lines, desc_obs_bws=TORFLOW_OBS_LAST,
cap=TORFLOW_BW_MARGIN, reverse=False): cap=TORFLOW_BW_MARGIN, reverse=False):
pass """
Obtain final bandwidth measurements applying Torflow's scaling
From Torflow's README.spec.txt (section 2.2)::
In this way, the resulting network status consensus bandwidth values # NOQA
are effectively re-weighted proportional to how much faster the node # NOQA
was as compared to the rest of the network.
The variables and steps used in Torflow:
The strm_bw field is the average (mean) of all the streams for the relay # NOQA
identified by the fingerprint field.
strm_bw = sum(bw stream x)/|n stream|
The filt_bw field is computed similarly, but only the streams equal to # NOQA
or greater than the strm_bw are counted in order to filter very slow # NOQA
streams due to slow node pairings.
**filt_sbw and strm_sbw**::
for rs in RouterStats.query.filter(stats_clause).\
options(eagerload_all('router.streams.circuit.routers')).all(): # NOQA
tot_sbw = 0
sbw_cnt = 0
for s in rs.router.streams:
if isinstance(s, ClosedStream):
skip = False
#for br in badrouters:
# if br != rs:
# if br.router in s.circuit.routers:
# skip = True
if not skip:
# Throw out outliers < mean
# (too much variance for stddev to filter much)
if rs.strm_closed == 1 or s.bandwidth() >= rs.sbw:
tot_sbw += s.bandwidth()
sbw_cnt += 1
if sbw_cnt: rs.filt_sbw = tot_sbw/sbw_cnt
else: rs.filt_sbw = None
**filt_avg, and strm_avg**::
Once we have determined the most recent measurements for each node, we # NOQA
compute an average of the filt_bw fields over all nodes we have measured. # NOQA
filt_avg = sum(map(lambda n: n.filt_bw, nodes.itervalues()))/float(len(nodes)) # NOQA
strm_avg = sum(map(lambda n: n.strm_bw, nodes.itervalues()))/float(len(nodes)) # NOQA
**true_filt_avg and true_strm_avg**::
for cl in ["Guard+Exit", "Guard", "Exit", "Middle"]:
true_filt_avg[cl] = filt_avg
true_strm_avg[cl] = strm_avg
In the non-pid case, all types of nodes get the same avg
**n.fbw_ratio and n.fsw_ratio**::
for n in nodes.itervalues():
n.fbw_ratio = n.filt_bw/true_filt_avg[n.node_class()]
n.sbw_ratio = n.strm_bw/true_strm_avg[n.node_class()]
These averages are used to produce ratios for each node by dividing the # NOQA
measured value for that node by the network average.
# Choose the larger between sbw and fbw
if n.sbw_ratio > n.fbw_ratio:
n.ratio = n.sbw_ratio
n.ratio = n.fbw_ratio
It is the ``observed bandwidth`` in the descriptor, NOT the ``average
return Router(ns.idhex, ns.nickname, bw_observed, dead, exitpolicy,
ns.flags, ip, version, os, uptime, published, contact, rate_limited, # NOQA
ns.orhash, ns.bandwidth, extra_info_digest, ns.unmeasured)
self.desc_bw = max(bw,1) # Avoid div by 0
These ratios are then multiplied by the most recent observed descriptor # NOQA
bandwidth we have available for each node, to produce a new value for # NOQA
the network status consensus process.
n.new_bw = n.desc_bw*n.ratio
The descriptor observed bandwidth is multiplied by the ratio.
With empirical results this ratio is ~[0.9, 8.9]
**Limit the bandwidth to a maximum**::
NODE_CAP = 0.05
if n.new_bw > tot_net_bw*NODE_CAP:
plog("INFO", "Clipping extremely fast "+n.node_class()+" node "+n.idhex+"="+n.nick+ # NOQA
" at "+str(100*NODE_CAP)+"% of network capacity ("+
str(n.new_bw)+"->"+str(int(tot_net_bw*NODE_CAP))+") "+
" pid_error="+str(n.pid_error)+
" pid_error_sum="+str(n.pid_error_sum))
n.new_bw = int(tot_net_bw*NODE_CAP)
However, tot_net_bw does not seems to be updated when not using pid.
This clipping would make faster relays to all have the same value.
All of that can be expressed as:
.. math::
bwnew_i &=
min \\left(
bw_i \\times \\mu
min \\left(bw_i,
bw_i \\times \\mu
\\times bwdescobs_i \\
min \\left(
bw_i \\times \\frac{\\sum_{i=1}^{n}bw_i}{n}
min \\left(bw_i,
bw_i \\times \\frac{\\sum_{i=1}^{n}bw_i}{n}
\\times bwdescobs_i
""""Calculating relays' bandwidth using Torflow method.")
bw_lines_tf = copy.deepcopy(bw_lines)
# mean (Torflow's strm_avg)
mu = mean([l.bw_bs_mean for l in bw_lines])
# filtered mean (Torflow's filt_avg)
muf = mean([min(l.bw_bs_mean, mu) for l in bw_lines])
# bw sum (Torflow's tot_net_bw or tot_sbw)
sum_bw = sum([l.bw_bs_mean for l in bw_lines])
# Torflow's clipping, not being applied
# hlimit = sum_bw * TORFLOW_BW_MARGIN
log.debug('sum %s', sum_bw)
log.debug('mu %s', mu)
log.debug('muf %s', muf)
# log.debug('hlimit %s', hlimit)
for l in bw_lines_tf:
if desc_obs_bws == TORFLOW_OBS_LAST:
desc_obs_bw = l.desc_obs_bw_bs_last
elif desc_obs_bws == TORFLOW_OBS_MEAN:
desc_obs_bw = l.desc_obs_bw_bs_mean
# just applying the formula above: = max(round(
# ratio
l.bw_bs_mean / mu,
# ratio filtered
min(l.bw_bs_mean, mu) / muf
) * desc_obs_bw
/ 1000), 1)
return sorted(bw_lines_tf, key=lambda x:, reverse=reverse)
@property @property
def sum_bw(self): def sum_bw(self):
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