136 lines
5.4 KiB
Python
136 lines
5.4 KiB
Python
#!/bin/python
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import networkx as nx
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import logging, random
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from datetime import datetime
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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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from DAS.validator import *
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class Simulator:
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proposerID = 0
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logLevel = logging.INFO
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validators = []
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glob = []
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result = []
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shape = []
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logger = []
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format = {}
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def __init__(self, shape):
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self.shape = shape
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self.format = {"entity": "Simulator"}
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self.result = Result(self.shape)
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def initValidators(self):
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self.glob = Observer(self.logger, self.shape)
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self.glob.reset()
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self.validators = []
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rows = list(range(self.shape.blockSize)) * int(self.shape.chi*self.shape.numberValidators/self.shape.blockSize)
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columns = list(range(self.shape.blockSize)) * int(self.shape.chi*self.shape.numberValidators/self.shape.blockSize)
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random.shuffle(rows)
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random.shuffle(columns)
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for i in range(self.shape.numberValidators):
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val = Validator(i, int(not i!=0), self.logger, self.shape, rows, columns)
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if i == self.proposerID:
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val.initBlock()
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self.glob.setGoldenData(val.block)
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else:
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val.logIDs()
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self.validators.append(val)
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def initNetwork(self):
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self.shape.netDegree = 6
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rowChannels = [[] for i in range(self.shape.blockSize)]
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columnChannels = [[] for i in range(self.shape.blockSize)]
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for v in self.validators:
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for id in v.rowIDs:
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rowChannels[id].append(v)
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for id in v.columnIDs:
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columnChannels[id].append(v)
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for id in range(self.shape.blockSize):
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if (len(rowChannels[id]) < self.shape.netDegree):
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self.logger.error("Graph degree higher than %d" % len(rowChannels[id]), extra=self.format)
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G = nx.random_regular_graph(self.shape.netDegree, len(rowChannels[id]))
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if not nx.is_connected(G):
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self.logger.error("Graph not connected for row %d !" % id, extra=self.format)
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for u, v in G.edges:
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val1=rowChannels[id][u]
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val2=rowChannels[id][v]
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val1.rowNeighbors[id].update({val2.ID : Neighbor(val2, self.shape.blockSize)})
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val2.rowNeighbors[id].update({val1.ID : Neighbor(val1, self.shape.blockSize)})
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if (len(columnChannels[id]) < self.shape.netDegree):
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self.logger.error("Graph degree higher than %d" % len(columnChannels[id]), extra=self.format)
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G = nx.random_regular_graph(self.shape.netDegree, len(columnChannels[id]))
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if not nx.is_connected(G):
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self.logger.error("Graph not connected for column %d !" % id, extra=self.format)
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for u, v in G.edges:
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val1=columnChannels[id][u]
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val2=columnChannels[id][v]
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val1.columnNeighbors[id].update({val2.ID : Neighbor(val2, self.shape.blockSize)})
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val2.columnNeighbors[id].update({val1.ID : Neighbor(val1, self.shape.blockSize)})
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def initLogger(self):
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logger = logging.getLogger("DAS")
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logger.setLevel(self.logLevel)
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ch = logging.StreamHandler()
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ch.setLevel(self.logLevel)
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ch.setFormatter(CustomFormatter())
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logger.addHandler(ch)
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self.logger = logger
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def resetShape(self, shape):
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self.shape = shape
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for val in self.validators:
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val.shape.failureRate = shape.failureRate
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val.shape.chi = shape.chi
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def run(self):
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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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missingSamples = expected - arrived
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missingVector = []
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steps = 0
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while(missingSamples > 0):
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missingVector.append(missingSamples)
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oldMissingSamples = missingSamples
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for i in range(1,self.shape.numberValidators):
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self.validators[i].receiveRowsColumns()
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for i in range(1,self.shape.numberValidators):
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self.validators[i].restoreRows()
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self.validators[i].restoreColumns()
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self.validators[i].sendRows()
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self.validators[i].sendColumns()
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self.validators[i].logRows()
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self.validators[i].logColumns()
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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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break
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elif missingSamples == 0:
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break
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else:
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steps += 1
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self.result.addMissing(missingVector)
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if missingSamples == 0:
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self.result.blockAvailable = 1
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self.logger.debug("The entire block is available at step %d, with failure rate %d !" % (steps, self.shape.failureRate), extra=self.format)
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return self.result
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else:
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self.result.blockAvailable = 0
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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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return self.result
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