das-research/DAS/simulator.py

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#!/bin/python
import networkx as nx
import logging, random
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from datetime import datetime
from DAS.tools import *
from DAS.results import *
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from DAS.observer import *
from DAS.validator import *
class Simulator:
"""This class implements the main DAS simulator."""
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def __init__(self, shape, config):
"""It initializes the simulation with a set of parameters (shape)."""
self.shape = shape
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self.format = {"entity": "Simulator"}
self.result = Result(self.shape)
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self.validators = []
self.logger = []
self.logLevel = config.logLevel
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self.proposerID = 0
self.glob = []
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def initValidators(self):
"""It initializes all the validators in the network."""
self.glob = Observer(self.logger, self.shape)
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self.glob.reset()
self.validators = []
rows = list(range(self.shape.blockSize)) * int(self.shape.chi*self.shape.numberValidators/self.shape.blockSize)
columns = list(range(self.shape.blockSize)) * int(self.shape.chi*self.shape.numberValidators/self.shape.blockSize)
random.shuffle(rows)
random.shuffle(columns)
for i in range(self.shape.numberValidators):
val = Validator(i, int(not i!=0), self.logger, self.shape, rows, columns)
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if i == self.proposerID:
val.initBlock()
self.glob.setGoldenData(val.block)
else:
val.logIDs()
self.validators.append(val)
def initNetwork(self):
"""It initializes the simulated network."""
self.shape.netDegree = 6
rowChannels = [[] for i in range(self.shape.blockSize)]
columnChannels = [[] for i in range(self.shape.blockSize)]
for v in self.validators:
for id in v.rowIDs:
rowChannels[id].append(v)
for id in v.columnIDs:
columnChannels[id].append(v)
for id in range(self.shape.blockSize):
# If the number of nodes in a channel is smaller or equal to the
# requested degree, a fully connected graph is used. For n>d, a random
# d-regular graph is set up. (For n=d+1, the two are the same.)
if (len(rowChannels[id]) <= self.shape.netDegree):
self.logger.debug("Graph fully connected with degree %d !" % (len(rowChannels[id]) - 1), extra=self.format)
G = nx.complete_graph(len(rowChannels[id]))
else:
G = nx.random_regular_graph(self.shape.netDegree, len(rowChannels[id]))
if not nx.is_connected(G):
self.logger.error("Graph not connected for row %d !" % id, extra=self.format)
for u, v in G.edges:
val1=rowChannels[id][u]
val2=rowChannels[id][v]
val1.rowNeighbors[id].update({val2.ID : Neighbor(val2, self.shape.blockSize)})
val2.rowNeighbors[id].update({val1.ID : Neighbor(val1, self.shape.blockSize)})
if (len(columnChannels[id]) <= self.shape.netDegree):
self.logger.debug("Graph fully connected with degree %d !" % (len(columnChannels[id]) - 1), extra=self.format)
G = nx.complete_graph(len(columnChannels[id]))
else:
G = nx.random_regular_graph(self.shape.netDegree, len(columnChannels[id]))
if not nx.is_connected(G):
self.logger.error("Graph not connected for column %d !" % id, extra=self.format)
for u, v in G.edges:
val1=columnChannels[id][u]
val2=columnChannels[id][v]
val1.columnNeighbors[id].update({val2.ID : Neighbor(val2, self.shape.blockSize)})
val2.columnNeighbors[id].update({val1.ID : Neighbor(val1, self.shape.blockSize)})
if self.logger.isEnabledFor(logging.DEBUG):
for i in range(0, self.shape.numberValidators):
self.logger.debug("Val %d : rowN %s", i, self.validators[i].rowNeighbors, extra=self.format)
self.logger.debug("Val %d : colN %s", i, self.validators[i].columnNeighbors, extra=self.format)
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def initLogger(self):
"""It initializes the logger."""
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logger = logging.getLogger("DAS")
logger.setLevel(self.logLevel)
ch = logging.StreamHandler()
ch.setLevel(self.logLevel)
ch.setFormatter(CustomFormatter())
logger.addHandler(ch)
self.logger = logger
def resetShape(self, shape):
"""It resets the parameters of the simulation."""
self.shape = shape
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self.result = Result(self.shape)
for val in self.validators:
val.shape.failureRate = shape.failureRate
val.shape.chi = shape.chi
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def run(self):
"""It runs the main simulation until the block is available or it gets stucked."""
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self.glob.checkRowsColumns(self.validators)
self.validators[self.proposerID].broadcastBlock()
arrived, expected = self.glob.checkStatus(self.validators)
missingSamples = expected - arrived
missingVector = []
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steps = 0
while(True):
missingVector.append(missingSamples)
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oldMissingSamples = missingSamples
self.logger.debug("PHASE SEND %d" % steps, extra=self.format)
for i in range(0,self.shape.numberValidators):
self.validators[i].send()
self.logger.debug("PHASE RECEIVE %d" % steps, extra=self.format)
for i in range(1,self.shape.numberValidators):
self.validators[i].receiveRowsColumns()
self.logger.debug("PHASE RESTORE %d" % steps, extra=self.format)
for i in range(1,self.shape.numberValidators):
self.validators[i].restoreRows()
self.validators[i].restoreColumns()
self.logger.debug("PHASE LOG %d" % steps, extra=self.format)
for i in range(0,self.shape.numberValidators):
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self.validators[i].logRows()
self.validators[i].logColumns()
self.validators[i].updateStats()
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arrived, expected = self.glob.checkStatus(self.validators)
missingSamples = expected - arrived
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missingRate = missingSamples*100/expected
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:
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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break
elif missingSamples == 0:
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#self.logger.info("The entire block is available at step %d, with failure rate %d !" % (steps, self.shape.failureRate), extra=self.format)
missingVector.append(missingSamples)
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break
else:
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steps += 1
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self.result.populate(self.shape, missingVector)
return self.result
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