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Merge pull request #32 from status-im/measure-progress
Measure and save progress and traffic statistics.
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Leo
GitHub
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57b0ee89f7
@ -1,5 +1,6 @@
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#!/bin/python3
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import numpy as np
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from DAS.block import *
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class Observer:
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@ -44,9 +45,55 @@ class Observer:
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"""It checks the status of how many expected and arrived samples globally."""
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arrived = 0
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expected = 0
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ready = 0
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validated = 0
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for val in validators:
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if val.amIproposer == 0:
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(a, e) = val.checkStatus()
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arrived += a
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expected += e
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return (arrived, expected)
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if a == e:
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ready += 1
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validated += val.vpn
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return (arrived, expected, ready, validated)
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def getProgress(self, validators):
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"""Calculate current simulation progress with different metrics.
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Returns:
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- missingSamples: overall number of sample instances missing in nodes.
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Sample are counted on both rows and columns, so intersections of interest are counted twice.
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- sampleProgress: previous expressed as progress ratio
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- nodeProgress: ratio of nodes having all segments interested in
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- validatorProgress: same as above, but vpn weighted average. I.e. it counts per validator,
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but counts a validator only if its support node's all validators see all interesting segments
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TODO: add real per validator progress counter
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"""
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arrived, expected, ready, validated = self.checkStatus(validators)
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missingSamples = expected - arrived
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sampleProgress = arrived / expected
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nodeProgress = ready / (len(validators)-1)
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validatorCnt = sum([v.vpn for v in validators[1:]])
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validatorProgress = validated / validatorCnt
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return missingSamples, sampleProgress, nodeProgress, validatorProgress
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def getTrafficStats(self, validators):
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"""Summary statistics of traffic measurements in a timestep."""
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def maxOrNan(l):
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return np.max(l) if l else np.NaN
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def meanOrNan(l):
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return np.mean(l) if l else np.NaN
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trafficStats = {}
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for cl in range(0,3):
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Tx = [v.statsTxInSlot for v in validators if v.nodeClass == cl]
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Rx = [v.statsRxInSlot for v in validators if v.nodeClass == cl]
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RxDup = [v.statsRxDupInSlot for v in validators if v.nodeClass == cl]
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trafficStats[cl] = {
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"Tx": {"mean": meanOrNan(Tx), "max": maxOrNan(Tx)},
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"Rx": {"mean": meanOrNan(Rx), "max": maxOrNan(Rx)},
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"RxDup": {"mean": meanOrNan(RxDup), "max": maxOrNan(RxDup)},
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}
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return trafficStats
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@ -13,6 +13,7 @@ class Result:
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self.blockAvailable = -1
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self.tta = -1
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self.missingVector = []
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self.metrics = {}
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def populate(self, shape, missingVector):
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"""It populates part of the result data inside a vector."""
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@ -26,6 +27,10 @@ class Result:
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self.blockAvailable = 0
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self.tta = -1
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def addMetric(self, name, metric):
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"""Generic function to add a metric to the results."""
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self.metrics[name] = metric
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def dump(self, execID):
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"""It dumps the results of the simulation in an XML file."""
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if not os.path.exists("results"):
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@ -2,9 +2,9 @@
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import networkx as nx
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import logging, random
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import pandas as pd
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from functools import partial, partialmethod
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from datetime import datetime
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from statistics import mean
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from DAS.tools import *
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from DAS.results import *
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from DAS.observer import *
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@ -166,9 +166,11 @@ class Simulator:
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"""It runs the main simulation until the block is available or it gets stucked."""
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self.glob.checkRowsColumns(self.validators)
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self.validators[self.proposerID].broadcastBlock()
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arrived, expected = self.glob.checkStatus(self.validators)
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arrived, expected, ready, validated = self.glob.checkStatus(self.validators)
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missingSamples = expected - arrived
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missingVector = []
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progressVector = []
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trafficStatsVector = []
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steps = 0
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while(True):
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missingVector.append(missingSamples)
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@ -189,19 +191,41 @@ class Simulator:
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self.validators[i].logColumns()
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# log TX and RX statistics
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statsTxInSlot = [v.statsTxInSlot for v in self.validators]
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statsRxInSlot = [v.statsRxInSlot for v in self.validators]
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self.logger.debug("step %d: TX_prod=%.1f, RX_prod=%.1f, TX_avg=%.1f, TX_max=%.1f, Rx_avg=%.1f, Rx_max=%.1f" %
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(steps, statsTxInSlot[0], statsRxInSlot[0],
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mean(statsTxInSlot[1:]), max(statsTxInSlot[1:]),
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mean(statsRxInSlot[1:]), max(statsRxInSlot[1:])), extra=self.format)
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trafficStats = self.glob.getTrafficStats(self.validators)
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self.logger.debug("step %d: %s" %
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(steps, trafficStats), extra=self.format)
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for i in range(0,self.shape.numberNodes):
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self.validators[i].updateStats()
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trafficStatsVector.append(trafficStats)
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missingSamples, sampleProgress, nodeProgress, validatorProgress = self.glob.getProgress(self.validators)
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self.logger.debug("step %d, arrived %0.02f %%, ready %0.02f %%, validated %0.02f %%"
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% (steps, sampleProgress*100, nodeProgress*100, validatorProgress*100), extra=self.format)
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cnS = "samples received"
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cnN = "nodes ready"
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cnV = "validators ready"
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cnT0 = "TX builder mean"
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cnT1 = "TX class1 mean"
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cnT2 = "TX class2 mean"
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cnR1 = "RX class1 mean"
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cnR2 = "RX class2 mean"
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cnD1 = "Dup class1 mean"
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cnD2 = "Dup class2 mean"
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progressVector.append({
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cnS:sampleProgress,
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cnN:nodeProgress,
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cnV:validatorProgress,
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cnT0: trafficStats[0]["Tx"]["mean"],
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cnT1: trafficStats[1]["Tx"]["mean"],
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cnT2: trafficStats[2]["Tx"]["mean"],
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cnR1: trafficStats[1]["Rx"]["mean"],
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cnR2: trafficStats[2]["Rx"]["mean"],
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cnD1: trafficStats[1]["RxDup"]["mean"],
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cnD2: trafficStats[2]["RxDup"]["mean"],
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})
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arrived, expected = self.glob.checkStatus(self.validators)
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missingSamples = expected - arrived
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missingRate = missingSamples*100/expected
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self.logger.debug("step %d, missing %d of %d (%0.02f %%)" % (steps, missingSamples, expected, missingRate), extra=self.format)
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if missingSamples == oldMissingSamples:
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self.logger.debug("The block cannot be recovered, failure rate %d!" % self.shape.failureRate, extra=self.format)
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missingVector.append(missingSamples)
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@ -213,6 +237,9 @@ class Simulator:
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else:
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steps += 1
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progress = pd.DataFrame(progressVector)
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if self.config.saveProgress:
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self.result.addMetric("progress", progress.to_dict(orient='list'))
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self.result.populate(self.shape, missingVector)
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return self.result
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@ -79,6 +79,8 @@ class Validator:
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self.statsTxPerSlot = []
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self.statsRxInSlot = 0
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self.statsRxPerSlot = []
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self.statsRxDupInSlot = 0
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self.statsRxDupPerSlot = []
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# Set uplink bandwidth. In segments (~560 bytes) per timestep (50ms?)
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# 1 Mbps ~= 1e6 / 20 / 8 / 560 ~= 11
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@ -163,7 +165,7 @@ class Validator:
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self.receivedQueue.append((rID, cID))
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else:
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self.logger.trace("Recv DUP: %d->%d: %d,%d", src, self.ID, rID, cID, extra=self.format)
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# self.statsRxDuplicateInSlot += 1
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self.statsRxDupInSlot += 1
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self.statsRxInSlot += 1
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def addToSendQueue(self, rID, cID):
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@ -205,8 +207,10 @@ class Validator:
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"""It updates the stats related to sent and received data."""
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self.logger.debug("Stats: tx %d, rx %d", self.statsTxInSlot, self.statsRxInSlot, extra=self.format)
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self.statsRxPerSlot.append(self.statsRxInSlot)
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self.statsRxDupPerSlot.append(self.statsRxDupInSlot)
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self.statsTxPerSlot.append(self.statsTxInSlot)
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self.statsRxInSlot = 0
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self.statsRxDupInSlot = 0
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self.statsTxInSlot = 0
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def checkSegmentToNeigh(self, rID, cID, neigh):
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@ -19,6 +19,9 @@ import numpy as np
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from DAS.shape import Shape
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dumpXML = 1
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# save progress vectors to XML
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saveProgress = 1
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visualization = 1
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logLevel = logging.INFO
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