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das-research
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Adding XML dump of results

This commit is contained in:
Leonardo Bautista-Gomez 2023-01-25 21:51:59 +01:00
parent fc7339dc91
commit 66824aedc6
6 changed files with 71 additions and 27 deletions
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1
DAS/configuration.py
View File

@ -33,6 +33,7 @@ class Configuration:
self.numberRuns = int(config.get("Advanced", "numberRuns"))
self.deterministic = config.get("Advanced", "deterministic")
self.dumpXML = config.get("Advanced", "dumpXML")
if self.nvStop < (self.blockSizeStart*4):
print("ERROR: The number of validators cannot be lower than the block size * 4")

43
DAS/results.py
View File

@ -1,17 +1,50 @@
#!/bin/python3
import os
from xml.dom import minidom
from dicttoxml import dicttoxml
class Result:
config = []
shape = []
missingVector = []
blockAvailable = -1
tta = -1
def __init__(self, config):
self.config = config
def __init__(self, shape):
self.shape = shape
self.blockAvailable = -1
self.tta = -1
self.missingVector = []
def addMissing(self, missingVector):
def populate(self, shape, missingVector):
self.shape = shape
self.missingVector = missingVector
missingSamples = missingVector[-1]
if missingSamples == 0:
self.blockAvailable = 1
self.tta = len(missingVector)
else:
self.blockAvailable = 0
self.tta = -1
def dump(self, execID):
if not os.path.exists("results"):
os.makedirs("results")
if not os.path.exists("results/"+execID):
os.makedirs("results/"+execID)
resd1 = self.shape.__dict__
resd2 = self.__dict__.copy()
resd2.pop("shape")
resd1.update(resd2)
resXml = dicttoxml(resd1)
xmlstr = minidom.parseString(resXml)
xmlPretty = xmlstr.toprettyxml()
filePath = "results/"+execID+"/nbv-"+str(self.shape.numberValidators)+\
"-bs-"+str(self.shape.blockSize)+\
"-nd-"+str(self.shape.netDegree)+\
"-fr-"+str(self.shape.failureRate)+\
"-chi-"+str(self.shape.chi)+\
"-r-"+str(self.shape.run)+".xml"
with open(filePath, "w") as f:
f.write(xmlPretty)

8
DAS/shape.py
View File

@ -1,16 +1,18 @@
#!/bin/python3
class Shape:
run = 0
numberValidators = 0
failureRate = 0
blockSize = 0
failureRate = 0
netDegree = 0
chi = 0
def __init__(self, blockSize, numberValidators, failureRate, chi, netDegree):
def __init__(self, blockSize, numberValidators, failureRate, chi, netDegree, run):
self.run = run
self.numberValidators = numberValidators
self.failureRate = failureRate
self.blockSize = blockSize
self.failureRate = failureRate
self.netDegree = netDegree
self.chi = chi

16
DAS/simulator.py
View File

@ -87,6 +87,7 @@ class Simulator:
def resetShape(self, shape):
self.shape = shape
self.result = Result(self.shape)
for val in self.validators:
val.shape.failureRate = shape.failureRate
val.shape.chi = shape.chi
@ -117,19 +118,16 @@ class Simulator:
missingRate = missingSamples*100/expected
self.logger.debug("step %d, missing %d of %d (%0.02f %%)" % (steps, missingSamples, expected, missingRate), extra=self.format)
if missingSamples == oldMissingSamples:
#self.logger.info("The block cannot be recovered, failure rate %d!" % self.shape.failureRate, extra=self.format)
missingVector.append(missingSamples)
break
elif missingSamples == 0:
#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)
break
else:
steps += 1
self.result.addMissing(missingVector)
if missingSamples == 0:
self.result.blockAvailable = 1
self.logger.debug("The entire block is available at step %d, with failure rate %d !" % (steps, self.shape.failureRate), extra=self.format)
return self.result
else:
self.result.blockAvailable = 0
self.logger.debug("The block cannot be recovered, failure rate %d!" % self.shape.failureRate, extra=self.format)
return self.result
self.result.populate(self.shape, missingVector)
return self.result

1
config.das
View File

@ -25,3 +25,4 @@ chiStep = 2
deterministic = 0
numberRuns = 2
dumpXML = 1

29
study.py
View File

@ -1,6 +1,6 @@
#! /bin/python3
import time, sys
import time, sys, random, copy
from DAS import *
@ -10,36 +10,45 @@ def study():
exit(1)
config = Configuration(sys.argv[1])
sim = Simulator(config)
shape = Shape(0, 0, 0, 0, 0, 0)
sim = Simulator(shape)
sim.initLogger()
results = []
simCnt = 0
now = datetime.now()
execID = now.strftime("%Y-%m-%d_%H-%M-%S_")+str(random.randint(100,999))
sim.logger.info("Starting simulations:", extra=sim.format)
start = time.time()
for run in range(config.numberRuns):
for fr in range(config.failureRateStart, config.failureRateStop+1, config.failureRateStep):
for chi in range(config.chiStart, config.chiStop+1, config.chiStep):
for blockSize in range(config.blockSizeStart, config.blockSizeStop+1, config.blockSizeStep):
for nv in range(config.nvStart, config.nvStop+1, config.nvStep):
for netDegree in range(config.netDegreeStart, config.netDegreeStop, config.netDegreeStep):
for nv in range(config.nvStart, config.nvStop+1, config.nvStep):
for blockSize in range(config.blockSizeStart, config.blockSizeStop+1, config.blockSizeStep):
for fr in range(config.failureRateStart, config.failureRateStop+1, config.failureRateStep):
for netDegree in range(config.netDegreeStart, config.netDegreeStop, config.netDegreeStep):
for chi in range(config.chiStart, config.chiStop+1, config.chiStep):
if not config.deterministic:
random.seed(datetime.now())
shape = Shape(blockSize, nv, fr, chi, netDegree)
shape = Shape(blockSize, nv, fr, chi, netDegree, run)
sim.resetShape(shape)
sim.initValidators()
sim.initNetwork()
result = sim.run()
sim.logger.info("Run %d, FR: %d %%, Chi: %d, BlockSize: %d, Nb.Val: %d, netDegree: %d ... Block Available: %d" % (run, fr, chi, blockSize, nv, netDegree, result.blockAvailable), extra=sim.format)
results.append(result)
sim.logger.info("Shape: %s ... Block Available: %d" % (str(sim.shape.__dict__), result.blockAvailable), extra=sim.format)
results.append(copy.deepcopy(result))
simCnt += 1
end = time.time()
sim.logger.info("A total of %d simulations ran in %d seconds" % (simCnt, end-start), extra=sim.format)
if config.dumpXML:
for res in results:
res.dump(execID)
sim.logger.info("Results dumped into results/%s/" % (execID), extra=sim.format)
study()