diff --git a/DAS/observer.py b/DAS/observer.py index 18c0461..235ed60 100644 --- a/DAS/observer.py +++ b/DAS/observer.py @@ -58,6 +58,17 @@ class Observer: return (arrived, expected, ready, validated) def getProgress(self, validators): + """Calculate current simulation progress with different metrics. + + Returns: + - missingSamples: overall number of sample instances missing in nodes. + Sample are counted on both rows and columns, so intersections of interest are counted twice. + - sampleProgress: previous expressed as progress ratio + - nodeProgress: ratio of nodes having all segments interested in + - validatorProgress: same as above, but vpn weighted average. I.e. it counts per validator, + but counts a validator only if its support node's all validators see all interesting segments + TODO: add real per validator progress counter + """ arrived, expected, ready, validated = self.checkStatus(validators) missingSamples = expected - arrived sampleProgress = arrived / expected @@ -68,6 +79,7 @@ class Observer: return missingSamples, sampleProgress, nodeProgress, validatorProgress def getTrafficStats(self, validators): + """Summary statistics of traffic measurements in a timestep.""" def maxOrNan(l): return np.max(l) if l else np.NaN def meanOrNan(l): diff --git a/DAS/results.py b/DAS/results.py index 61f4f04..48512cd 100644 --- a/DAS/results.py +++ b/DAS/results.py @@ -7,35 +7,37 @@ from dicttoxml import dicttoxml class Result: """This class stores and process/store the results of a simulation.""" - def __init__(self, shape): + def __init__(self, shape, execID): """It initializes the instance with a specific shape.""" self.shape = shape + self.execID = execID self.blockAvailable = -1 self.tta = -1 self.missingVector = [] self.metrics = {} - def populate(self, shape, missingVector): + def populate(self, shape, config, missingVector): """It populates part of the result data inside a vector.""" self.shape = shape self.missingVector = missingVector missingSamples = missingVector[-1] if missingSamples == 0: self.blockAvailable = 1 - self.tta = len(missingVector) + self.tta = len(missingVector) * (1000/config.stepDuration) else: self.blockAvailable = 0 self.tta = -1 def addMetric(self, name, metric): + """Generic function to add a metric to the results.""" self.metrics[name] = metric - def dump(self, execID): + def dump(self): """It dumps the results of the simulation in an XML file.""" if not os.path.exists("results"): os.makedirs("results") - if not os.path.exists("results/"+execID): - os.makedirs("results/"+execID) + if not os.path.exists("results/"+self.execID): + os.makedirs("results/"+self.execID) resd1 = self.shape.__dict__ resd2 = self.__dict__.copy() resd2.pop("shape") @@ -43,6 +45,6 @@ class Result: resXml = dicttoxml(resd1) xmlstr = minidom.parseString(resXml) xmlPretty = xmlstr.toprettyxml() - filePath = "results/"+execID+"/"+str(self.shape)+".xml" + filePath = "results/"+self.execID+"/"+str(self.shape)+".xml" with open(filePath, "w") as f: f.write(xmlPretty) diff --git a/DAS/simulator.py b/DAS/simulator.py index b7b7dff..e699218 100644 --- a/DAS/simulator.py +++ b/DAS/simulator.py @@ -19,7 +19,8 @@ class Simulator: self.shape = shape self.config = config self.format = {"entity": "Simulator"} - self.result = Result(self.shape) + self.execID = execID + self.result = Result(self.shape, self.execID) self.validators = [] self.logger = [] self.logLevel = config.logLevel @@ -279,7 +280,7 @@ class Simulator: 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) + self.logger.debug("The entire block is available at step %d, with failure rate %d !" % (steps, self.shape.failureRate), extra=self.format) missingVector.append(missingSamples) break else: @@ -288,17 +289,6 @@ class Simulator: progress = pd.DataFrame(progressVector) if self.config.saveProgress: self.result.addMetric("progress", progress.to_dict(orient='list')) - if self.config.plotProgress: - progress.plot.line(subplots = [[cnS, cnN, cnV], [cnT0], [cnT1, cnR1, cnD1], [cnT2, cnR2, cnD2]], - title = str(self.shape)) - if not os.path.exists("results"): - os.makedirs("results") - if not os.path.exists("results/"+self.execID): - os.makedirs("results/"+self.execID) - filePath = "results/"+self.execID+"/"+str(self.shape)+".png" - matplotlib.pyplot.savefig(filePath) - matplotlib.pyplot.close() - - self.result.populate(self.shape, missingVector) + self.result.populate(self.shape, self.config, missingVector) return self.result diff --git a/DAS/visualizer.py b/DAS/visualizer.py index 8afd006..db4b2d5 100644 --- a/DAS/visualizer.py +++ b/DAS/visualizer.py @@ -36,7 +36,7 @@ class Visualizer: bwUplinkProd = int(root.find('bwUplinkProd').text) bwUplink1 = int(root.find('bwUplink1').text) bwUplink2 = int(root.find('bwUplink2').text) - tta = int(root.find('tta').text) + tta = float(root.find('tta').text) # Loop over all possible combinations of of the parameters minus two for combination in combinations(self.parameters, len(self.parameters)-2): @@ -120,7 +120,7 @@ class Visualizer: hist, xedges, yedges = np.histogram2d(data[key][labels[0]], data[key][labels[1]], bins=(len(xlabels), len(ylabels)), weights=data[key]['ttas']) hist = hist.T fig, ax = plt.subplots(figsize=(10, 6)) - sns.heatmap(hist, xticklabels=xlabels, yticklabels=ylabels, cmap='Purples', cbar_kws={'label': 'Time to block availability'}, linecolor='black', linewidths=0.3, annot=True, fmt=".2f", ax=ax) + sns.heatmap(hist, xticklabels=xlabels, yticklabels=ylabels, cmap='Purples', cbar_kws={'label': 'Time to block availability (ms)'}, linecolor='black', linewidths=0.3, annot=True, fmt=".2f", ax=ax) plt.xlabel(self.formatLabel(labels[0])) plt.ylabel(self.formatLabel(labels[1])) filename = "" @@ -131,6 +131,8 @@ class Visualizer: filename += f"{key[paramValueCnt]}" formattedTitle = self.formatTitle(key[paramValueCnt]) title += formattedTitle + if (paramValueCnt+1) % 5 == 0: + title += "\n" paramValueCnt += 1 title_obj = plt.title(title) font_size = 16 * fig.get_size_inches()[0] / 10 diff --git a/config_example.py b/config_example.py index f799c20..2187108 100644 --- a/config_example.py +++ b/config_example.py @@ -31,14 +31,14 @@ logLevel = logging.INFO # number of parallel workers. -1: all cores; 1: sequential # for more details, see joblib.Parallel -numJobs = 3 +numJobs = -1 # distribute rows/columns evenly between validators (True) # or generate it using local randomness (False) evenLineDistribution = True # Number of simulation runs with the same parameters for statistical relevance -runs = range(10) +runs = range(2) # Number of validators numberNodes = range(256, 513, 128) @@ -53,14 +53,14 @@ blockSizes = range(32,65,16) netDegrees = range(6, 9, 2) # number of rows and columns a validator is interested in -chis = range(1, 5, 2) +chis = range(2, 5, 2) # ratio of class1 nodes (see below for parameters per class) -class1ratios = np.arange(0, 1, .2) +class1ratios = [0.8, 0.9] # Number of validators per beacon node validatorsPerNode1 = [1] -validatorsPerNode2 = [2, 4, 8, 16, 32] +validatorsPerNode2 = [500] # Set uplink bandwidth. In segments (~560 bytes) per timestep (50ms?) # 1 Mbps ~= 1e6 / 20 / 8 / 560 ~= 11 @@ -68,8 +68,11 @@ bwUplinksProd = [2200] bwUplinks1 = [110] bwUplinks2 = [2200] +# Step duration in miliseconds (Classic RTT is about 100ms) +stepDuration = 50 + # Set to True if you want your run to be deterministic, False if not -deterministic = False +deterministic = True # If your run is deterministic you can decide the random seed. This is ignore otherwise. randomSeed = "DAS" diff --git a/study.py b/study.py index aa27a5f..74dba63 100644 --- a/study.py +++ b/study.py @@ -36,7 +36,7 @@ def runOnce(config, shape, execID): sim.logger.info("Shape: %s ... Block Available: %d in %d steps" % (str(sim.shape.__dict__), result.blockAvailable, len(result.missingVector)), extra=sim.format) if config.dumpXML: - result.dump(execID) + result.dump() return result