def actuate(self, input):
self._logger.debug('input = %f' % input)
self.cluster.stateLock.acquire()
try:
currPerf = getPerformance(self.cluster.servers[Server.ON])
sat = input / currPerf
if round(sat - self.controller._setPoint, 5) != 0:
newPerf = input / self.controller._setPoint
newPerf = min(getMaxPerformance(self.cluster.servers[Server.ON]), newPerf)
self._logger.debug('allocation = %f' % newPerf)
output = None
try:
output = self.balancePerformance(newPerf, self.cluster.servers[Server.ON])
#output = self.balancePerformanceNaive(newPerf, self.cluster.servers[Server.ON])
except Exception:
self._logger.exception('balancePerformance has failed with args: (%s, %s)' % (newPerf, self.cluster.servers[Server.ON]))
else:
try:
self.applyPerfRegulation(output)
except Exception:
self._logger.exception('applyPerfRegulation has failed! Input was: %s' % output)
finally:
self.cluster.stateLock.release()
def actuate(self, input):
self._logger.debug('input = %f' % input)
self.cluster.stateLock.acquire()
try:
currPerf = getPerformance(self.cluster.servers[Server.ON])
sat = input / currPerf
if round(sat - self.controller._setPoint, 5) != 0:
newPerf = input / self.controller._setPoint
newPerf = min(
getMaxPerformance(self.cluster.servers[Server.ON]),
newPerf)
self._logger.debug('allocation = %f' % newPerf)
output = None
try:
output = self.balancePerformance(
newPerf, self.cluster.servers[Server.ON])
#output = self.balancePerformanceNaive(newPerf, self.cluster.servers[Server.ON])
except Exception:
self._logger.exception(
'balancePerformance has failed with args: (%s, %s)' %
(newPerf, self.cluster.servers[Server.ON]))
else:
try:
self.applyPerfRegulation(output)
except Exception:
self._logger.exception(
'applyPerfRegulation has failed! Input was: %s' %
output)
finally:
self.cluster.stateLock.release()
def main():
filename = '../tmp/config.txt'
c = ConfigReader()
c.parse(filename)
cluster = c.readCluster()
maxPerf = getMaxPerformance(cluster.availableServers)
outline = readOutlineFile('../tmp/outline.txt')
trace = generateTrace(outline, maxPerf, 20)
if Gnuplot is not None:
data = Data(trace, with_='l')
g = Gnuplot()
g('set ytics 0, 100')
g('set xtics 0, 20')
g('set grid')
g.plot(data)
raw_input()
file = open('../tmp/trace_gen.txt','w')
for value in trace:
file.write('%f\n' % value)
file.close()
def testRamp(self):
'''
Will build a ramp to test configurations
All configurations must be able to cope with the load.
'''
trace = range(0, int(getMaxPerformance(self.configurator._efficientServerList)))
reversed = trace[:]
reversed.reverse()
trace.extend(reversed)
config = self.configurator._efficientServerList
for load in trace:
config = self.configurator.getConfiguration(load, config, self.configurator._gamma)
perf = getMaxPerformance(config)
#print 'load =',load,'perf =',perf
if perf < load:
self.fail("Configuration can't cope with load")
def main():
c = ConfigReader()
c.parse('../../tmp/config.txt')
cluster = c.readCluster()
configurator = ConfiguratorBuilder().build(c, cluster, None)
servers = cluster.availableServers * 100000
maxPerf = getMaxPerformance(servers)
cProfile.runctx('configurator.getConfiguration(maxPerf, servers, 1.0)',globals=globals(),locals=locals())
def scale(data, servers, maxUtil=0.90, minPerf=0):
'''
Scales a trace based on the maximum load for the cluster
'''
trace_min, trace_max = min(data), max(data)
maxPerf = getMaxPerformance(servers) * 0.90
#scales the trace file
for i in range(len(data)):
data[i] = (data[i] - trace_min) / (trace_max - trace_min) * (maxPerf - minPerf) + minPerf
def scale(data, servers, maxUtil=0.90, minPerf=0):
'''
Scales a trace based on the maximum load for the cluster
'''
trace_min, trace_max = min(data), max(data)
maxPerf = getMaxPerformance(servers) * 0.90
#scales the trace file
for i in range(len(data)):
data[i] = (data[i] - trace_min) / (trace_max - trace_min) * (
maxPerf - minPerf) + minPerf
def main():
c = ConfigReader()
c.parse('../../tmp/config.txt')
cluster = c.readCluster()
configurator = ConfiguratorBuilder().build(c, cluster, None)
servers = cluster.availableServers * 100000
maxPerf = getMaxPerformance(servers)
cProfile.runctx('configurator.getConfiguration(maxPerf, servers, 1.0)',
globals=globals(),
locals=locals())
def balancePerformanceNaive(self, load, servers):
'''
Will balance the performance according to each server max capacity, granting
proportional throughput among servers.
'''
perfList = []
maxLoad = getMaxPerformance(servers)
for server in servers:
serverPerf = ServerPerf(server)
serverPerf.perf = load * (server.energyModel.peakPerf[-1] / maxLoad)
perfList.append(serverPerf)
return perfList
def balancePerformanceNaive(self, load, servers):
'''
Will balance the performance according to each server max capacity, granting
proportional throughput among servers.
'''
perfList = []
maxLoad = getMaxPerformance(servers)
for server in servers:
serverPerf = ServerPerf(server)
serverPerf.perf = load * (server.energyModel.peakPerf[-1] /
maxLoad)
perfList.append(serverPerf)
return perfList
def main():
c = ConfigReader()
c.parse('../../tmp/config.txt')
cluster = c.readCluster()
configurator = ConfiguratorBuilder().build(c, cluster, None)
tests = 1000
maxMultiplier = 200
step = 20
for mult in xrange(1, maxMultiplier, step):
servers = cluster.availableServers * mult
maxPerf = getMaxPerformance(servers)
ti = time.time()
for i in xrange(tests):
configurator.getConfiguration(maxPerf, servers, 1.0)
tf = time.time()
print len(servers), (tf - ti) * 1000.0 / tests
def main():
c = ConfigReader()
c.parse('../../tmp/config.txt')
cluster = c.readCluster()
configurator = ConfiguratorBuilder().build(c, cluster, None)
tests = 1000
maxMultiplier = 200
step = 20
for mult in xrange(1,maxMultiplier,step):
servers = cluster.availableServers * mult
maxPerf = getMaxPerformance(servers)
ti = time.time()
for i in xrange(tests):
configurator.getConfiguration(maxPerf, servers, 1.0)
tf = time.time()
print len(servers), (tf-ti) * 1000.0 / tests
def actuate(self, qos):
assert self._policyServers is not None
self._policyMutex.acquire()
newPerf = self.perfList[self.perfIndex % len(self.perfList)] * getMaxPerformance(self._policyServers)
self.perfIndex += 1
try:
output = None
try:
self._logger.debug('balancing: ' + str(newPerf))
output = self.balancePerformanceNaive(newPerf, self._policyServers)
except Exception:
self._logger.exception('balancePerformance has failed with args: (%s, %s)' % (newPerf, self._policyServers))
else:
try:
self.applyPerfRegulation(output)
except Exception:
self._logger.exception('applyPerfRegulation has failed! Input was: %s' % output)
finally:
self._policyMutex.release()
def main():
traceFilename = '../../tmp/wc98_1.txt'
configFilename = '../../tmp/dynGamma.txt'
cReader = ConfigReader()
cReader.parse(configFilename)
cluster = cReader.readCluster()
predictor = cReader.readPredictor()
lb = cReader.readLoadBalancer(cluster)
cluster.loadBalancer = lb
config = cluster._availableServers[:]
bestConfig = config[:]
predConfig = config[:]
reactiveConfig = config[:]
bestConfigurator = cReader.readConfigurator(cluster, DummyFilter())
bestConfigurator.setEfficientServerList(cluster._availableServers)
bestConfigurator.gamma = 1.0 #do not change!!
predConfigurator = cReader.readConfigurator(cluster, predictor)
predConfigurator.setEfficientServerList(cluster._availableServers)
predConfigurator._gamma = 1.0
reactiveConfigurator = cReader.readConfigurator(cluster, DummyFilter())
reactiveConfigurator.setEfficientServerList(cluster._availableServers)
#reactiveConfigurator._gamma = 0.55
step = predConfigurator.windowSize
#reads the trace file
data = file2data(1, traceFilename)[0]
scale(data, cluster.availableServers)
predCounter, reactiveCounter, bestCounter = 0, 0, 0 #configuration counter
pred_upCounter, pred_downCounter = 0, 0
best_upCounter, best_downCounter = 0, 0
reactive_upCounter, reactive_downCounter = 0, 0
bestPerf = getMaxPerformance(bestConfig)
reactivePerf = getMaxPerformance(bestConfig)
predPerf = getMaxPerformance(bestConfig)
avgs = []
reactivePerfs = []
predPerfs = []
bestPerfs = []
gammas = []
for i in range(len(data) / step):
avgData = 0.0
for j in range(step):
avgData += data[i * step + j]
avgData /= step
avgs.append(avgData)
reactivePerfs.append(reactivePerf)
predPerfs.append(predPerf)
optimumConfig = None
'''
Will use the efficient list to create the optimumConfig
'''
optimumConfig = [bestConfigurator._efficientServerList[0]]
k = 1
while getMaxPerformance(optimumConfig) < avgData:
optimumConfig.append(bestConfigurator._efficientServerList[k])
k += 1
'''
Calculates the appropriate configuration, 'knowing' that avgData is true
'''
if i > 0: #calculates false alarms
tmp = bestConfigurator.getConfiguration(avgData, bestConfig,
bestConfigurator._gamma)
if tmp != bestConfig:
bestCounter += 1
bestConfig = tmp
bestPerf = getMaxPerformance(bestConfig)
bestPerfs.append(bestPerf)
'''
Compares the chosen configuration with a least powerful one
'''
if avgData > bestPerf:
best_downCounter += 1
elif getMaxPerformance(bestConfig) > getMaxPerformance(
optimumConfig):
best_upCounter += 1
if avgData > reactivePerf:
reactive_downCounter += 1
elif getMaxPerformance(reactiveConfig) > getMaxPerformance(
optimumConfig):
reactive_upCounter += 1
if avgData > predPerf:
pred_downCounter += 1
elif getMaxPerformance(predConfig) > getMaxPerformance(
optimumConfig):
pred_upCounter += 1
elif len(bestConfig) < len(predConfig):
pred_upCounter += 1
prediction = predictor.apply(avgData)
#actuation = prediction
actuation = max(prediction, avgData)
tmp = predConfigurator.getConfiguration(actuation, predConfig,
predConfigurator._gamma)
if set(tmp) != set(predConfig):
predCounter += 1
predConfig = tmp
predPerf = getMaxPerformance(predConfig)
if reactiveConfigurator.dynGamma:
idealConfig = bestConfigurator.getConfiguration(
avgData, reactiveConfig, reactiveConfigurator.MAX_GAMMA)
idealPerf = round(getMaxPerformance(idealConfig), 5)
currPerf = round(getMaxPerformance(reactiveConfig), 5)
reactiveConfigurator._gamma = reactiveConfigurator.adjustGamma(
currPerf - idealPerf)
gammas.append(reactiveConfigurator._gamma)
tmp = reactiveConfigurator.getConfiguration(
avgData, reactiveConfig, reactiveConfigurator._gamma)
if set(tmp) != set(reactiveConfig):
reactiveCounter += 1
reactiveConfig = tmp
reactivePerf = getMaxPerformance(reactiveConfig)
print 'Best - False alarms: up = %d, down = %d' % (best_upCounter,
best_downCounter)
print 'Predictive - False alarms: up = %d, down = %d' % (pred_upCounter,
pred_downCounter)
print 'Reactive - False alarms: up = %d, down = %d' % (
reactive_upCounter, reactive_downCounter)
print 'Best Configs = %d, Predictive Configs = %d, Reactive Configs = %d' %\
(bestCounter, predCounter, reactiveCounter)
g = Gnuplot(debug=0)
g('reset')
#g('set size 0.65,0.65')
g('set encoding iso_8859_1')
g('set terminal postscript eps enhanced monochrome')
g('set output "carlos2.ps"')
g('set key top left')
g('set xlabel "Tempo (s)"')
g('set xrange [0:%d]' % (len(data) / step))
spacing = 15
tics = ','.join([
'"%d" %d' % (x * step * spacing, x * spacing)
for x in range(1,
len(data) / (step * spacing) + 1)
])
g('set xtics (%s)' % tics)
g('set ylabel "Requisições"'.encode('iso_8859_1'))
g('set grid')
#plot incoming requests avg
with_plot = 'steps'
#plot configurations
pi1 = Data(avgs, with_=with_plot, title='Carga')
pi2 = Data(reactivePerfs, with_=with_plot, title='Reativo')
pi_gamma = Data(gammas, with_=with_plot, title='Gamma')
#pi3 = Data(predPerfs, with_=with_plot,title='Preditivo')
#pi4 = Data(bestPerfs, with_=with_plot,title='Perfect')
#g.plot(pi1,pi2)
#g.plot(pi_gamma)
#raw_input()
print 'Done'
def main():
traceFilename = '../../tmp/nasa_3.txt'
configFilename = '../../tmp/probSub.txt'
cReader = ConfigReader()
cReader.parse(configFilename)
cluster = cReader.readCluster()
lb = cReader.readLoadBalancer(cluster)
cluster.loadBalancer = lb
bestConfigurator = cReader.readConfigurator(cluster, DummyFilter())
bestConfigurator.setEfficientServerList(cluster._availableServers)
bestConfigurator.gamma = 1.0 #do not change!!
reactiveConfigurator = cReader.readConfigurator(cluster, DummyFilter())
reactiveConfigurator.setEfficientServerList(cluster._availableServers)
step = bestConfigurator.windowSize
#reads the trace file
data = file2data(1, traceFilename)[0]
scale(data, cluster.availableServers)
gamma = 0.0
while round(gamma,5) <= MAX_GAMMA:
config = cluster._availableServers[:]
bestConfig = config[:]
reactiveConfig = config[:]
reactiveConfigurator._gamma = gamma
reactiveCounter, bestCounter = 0, 0 #configuration counter
best_upCounter, best_downCounter = 0, 0
reactive_upCounter, reactive_downCounter = 0, 0
bestPerf = getMaxPerformance(bestConfig)
reactivePerf = getMaxPerformance(bestConfig)
subestimated = 0.0
for i in range(len(data) / step):
avgData = 0.0
for j in range(step):
avgData += data[i * step + j]
avgData /= step
optimumConfig = None
'''
Will use the efficient list to create the optimumConfig
'''
optimumConfig = [bestConfigurator._efficientServerList[0]]
k = 1
while getMaxPerformance(optimumConfig) < avgData:
optimumConfig.append(bestConfigurator._efficientServerList[k])
k += 1
'''
Calculates the appropriate configuration, 'knowing' that avgData is true
'''
if i > 0: #calculates false alarms
tmp = bestConfigurator.getConfiguration(avgData, bestConfig, bestConfigurator._gamma)
if tmp != bestConfig:
bestCounter += 1
bestConfig = tmp
bestPerf = getMaxPerformance(bestConfig)
'''
Compares the chosen configuration with a least powerful one
'''
if avgData > bestPerf:
best_downCounter += 1
elif getMaxPerformance(bestConfig) > getMaxPerformance(optimumConfig):
best_upCounter += 1
if avgData > reactivePerf:
reactive_downCounter += 1
elif getMaxPerformance(reactiveConfig) > getMaxPerformance(optimumConfig):
reactive_upCounter += 1
for j in range(step):
subestimated += max(data[i * step + j] - reactivePerf, 0)
if reactiveConfigurator.dynGamma:
idealConfig = bestConfigurator.getConfiguration(avgData, reactiveConfig, reactiveConfigurator.MAX_GAMMA)
idealPerf = round(getMaxPerformance(idealConfig),5)
currPerf = round(getMaxPerformance(reactiveConfig),5)
reactiveConfigurator._gamma = reactiveConfigurator.adjustGamma(currPerf - idealPerf)
tmp = reactiveConfigurator.getConfiguration(avgData, reactiveConfig, reactiveConfigurator._gamma)
if set(tmp) != set(reactiveConfig):
reactiveCounter += 1
reactiveConfig = tmp
reactivePerf = getMaxPerformance(reactiveConfig)
#print 'Gamma = %f' % gamma
#print 'Best - super = %d, sub = %d' % (best_upCounter, best_downCounter)
#print 'Reactive - super = %d, sub = %d' % (reactive_upCounter, reactive_downCounter)
#print 'Best Configs = %d, Reactive Configs = %d' %\
# (bestCounter, reactiveCounter)
print gamma, 1.0 - reactive_downCounter / (float(len(data) - step) / step), reactiveCounter
gamma += GAMMA_INCREMENT
def main():
traceFilename = '../../tmp/nasa_3.txt'
configFilename = '../../tmp/probSub.txt'
cReader = ConfigReader()
cReader.parse(configFilename)
cluster = cReader.readCluster()
lb = cReader.readLoadBalancer(cluster)
cluster.loadBalancer = lb
bestConfigurator = cReader.readConfigurator(cluster, DummyFilter())
bestConfigurator.setEfficientServerList(cluster._availableServers)
bestConfigurator.gamma = 1.0 #do not change!!
reactiveConfigurator = cReader.readConfigurator(cluster, DummyFilter())
reactiveConfigurator.setEfficientServerList(cluster._availableServers)
step = bestConfigurator.windowSize
#reads the trace file
data = file2data(1, traceFilename)[0]
scale(data, cluster.availableServers)
gamma = 0.0
while round(gamma, 5) <= MAX_GAMMA:
config = cluster._availableServers[:]
bestConfig = config[:]
reactiveConfig = config[:]
reactiveConfigurator._gamma = gamma
reactiveCounter, bestCounter = 0, 0 #configuration counter
best_upCounter, best_downCounter = 0, 0
reactive_upCounter, reactive_downCounter = 0, 0
bestPerf = getMaxPerformance(bestConfig)
reactivePerf = getMaxPerformance(bestConfig)
subestimated = 0.0
for i in range(len(data) / step):
avgData = 0.0
for j in range(step):
avgData += data[i * step + j]
avgData /= step
optimumConfig = None
'''
Will use the efficient list to create the optimumConfig
'''
optimumConfig = [bestConfigurator._efficientServerList[0]]
k = 1
while getMaxPerformance(optimumConfig) < avgData:
optimumConfig.append(bestConfigurator._efficientServerList[k])
k += 1
'''
Calculates the appropriate configuration, 'knowing' that avgData is true
'''
if i > 0: #calculates false alarms
tmp = bestConfigurator.getConfiguration(
avgData, bestConfig, bestConfigurator._gamma)
if tmp != bestConfig:
bestCounter += 1
bestConfig = tmp
bestPerf = getMaxPerformance(bestConfig)
'''
Compares the chosen configuration with a least powerful one
'''
if avgData > bestPerf:
best_downCounter += 1
elif getMaxPerformance(bestConfig) > getMaxPerformance(
optimumConfig):
best_upCounter += 1
if avgData > reactivePerf:
reactive_downCounter += 1
elif getMaxPerformance(reactiveConfig) > getMaxPerformance(
optimumConfig):
reactive_upCounter += 1
for j in range(step):
subestimated += max(data[i * step + j] - reactivePerf, 0)
if reactiveConfigurator.dynGamma:
idealConfig = bestConfigurator.getConfiguration(
avgData, reactiveConfig, reactiveConfigurator.MAX_GAMMA)
idealPerf = round(getMaxPerformance(idealConfig), 5)
currPerf = round(getMaxPerformance(reactiveConfig), 5)
reactiveConfigurator._gamma = reactiveConfigurator.adjustGamma(
currPerf - idealPerf)
tmp = reactiveConfigurator.getConfiguration(
avgData, reactiveConfig, reactiveConfigurator._gamma)
if set(tmp) != set(reactiveConfig):
reactiveCounter += 1
reactiveConfig = tmp
reactivePerf = getMaxPerformance(reactiveConfig)
#print 'Gamma = %f' % gamma
#print 'Best - super = %d, sub = %d' % (best_upCounter, best_downCounter)
#print 'Reactive - super = %d, sub = %d' % (reactive_upCounter, reactive_downCounter)
#print 'Best Configs = %d, Reactive Configs = %d' %\
# (bestCounter, reactiveCounter)
print gamma, 1.0 - reactive_downCounter / (float(len(data) - step) /
step), reactiveCounter
gamma += GAMMA_INCREMENT
def main():
traceFilename = '../../tmp/wc98_1.txt'
configFilename = '../../tmp/dynGamma.txt'
cReader = ConfigReader()
cReader.parse(configFilename)
cluster = cReader.readCluster()
predictor = cReader.readPredictor()
lb = cReader.readLoadBalancer(cluster)
cluster.loadBalancer = lb
config = cluster._availableServers[:]
bestConfig = config[:]
predConfig = config[:]
reactiveConfig = config[:]
bestConfigurator = cReader.readConfigurator(cluster, DummyFilter())
bestConfigurator.setEfficientServerList(cluster._availableServers)
bestConfigurator.gamma = 1.0 #do not change!!
predConfigurator = cReader.readConfigurator(cluster, predictor)
predConfigurator.setEfficientServerList(cluster._availableServers)
predConfigurator._gamma = 1.0
reactiveConfigurator = cReader.readConfigurator(cluster, DummyFilter())
reactiveConfigurator.setEfficientServerList(cluster._availableServers)
#reactiveConfigurator._gamma = 0.55
step = predConfigurator.windowSize
#reads the trace file
data = file2data(1, traceFilename)[0]
scale(data, cluster.availableServers)
predCounter, reactiveCounter, bestCounter = 0, 0, 0 #configuration counter
pred_upCounter, pred_downCounter = 0, 0
best_upCounter, best_downCounter = 0, 0
reactive_upCounter, reactive_downCounter = 0, 0
bestPerf = getMaxPerformance(bestConfig)
reactivePerf = getMaxPerformance(bestConfig)
predPerf = getMaxPerformance(bestConfig)
avgs = []
reactivePerfs = []
predPerfs = []
bestPerfs = []
gammas = []
for i in range(len(data) / step):
avgData = 0.0
for j in range(step):
avgData += data[i * step + j]
avgData /= step
avgs.append(avgData)
reactivePerfs.append(reactivePerf)
predPerfs.append(predPerf)
optimumConfig = None
'''
Will use the efficient list to create the optimumConfig
'''
optimumConfig = [bestConfigurator._efficientServerList[0]]
k = 1
while getMaxPerformance(optimumConfig) < avgData:
optimumConfig.append(bestConfigurator._efficientServerList[k])
k += 1
'''
Calculates the appropriate configuration, 'knowing' that avgData is true
'''
if i > 0: #calculates false alarms
tmp = bestConfigurator.getConfiguration(avgData, bestConfig, bestConfigurator._gamma)
if tmp != bestConfig:
bestCounter += 1
bestConfig = tmp
bestPerf = getMaxPerformance(bestConfig)
bestPerfs.append(bestPerf)
'''
Compares the chosen configuration with a least powerful one
'''
if avgData > bestPerf:
best_downCounter += 1
elif getMaxPerformance(bestConfig) > getMaxPerformance(optimumConfig):
best_upCounter += 1
if avgData > reactivePerf:
reactive_downCounter += 1
elif getMaxPerformance(reactiveConfig) > getMaxPerformance(optimumConfig):
reactive_upCounter += 1
if avgData > predPerf:
pred_downCounter += 1
elif getMaxPerformance(predConfig) > getMaxPerformance(optimumConfig):
pred_upCounter += 1
elif len(bestConfig) < len(predConfig):
pred_upCounter += 1
prediction = predictor.apply(avgData)
#actuation = prediction
actuation = max(prediction, avgData)
tmp = predConfigurator.getConfiguration(actuation, predConfig, predConfigurator._gamma)
if set(tmp) != set(predConfig):
predCounter += 1
predConfig = tmp
predPerf = getMaxPerformance(predConfig)
if reactiveConfigurator.dynGamma:
idealConfig = bestConfigurator.getConfiguration(avgData, reactiveConfig, reactiveConfigurator.MAX_GAMMA)
idealPerf = round(getMaxPerformance(idealConfig),5)
currPerf = round(getMaxPerformance(reactiveConfig),5)
reactiveConfigurator._gamma = reactiveConfigurator.adjustGamma(currPerf - idealPerf)
gammas.append(reactiveConfigurator._gamma)
tmp = reactiveConfigurator.getConfiguration(avgData, reactiveConfig, reactiveConfigurator._gamma)
if set(tmp) != set(reactiveConfig):
reactiveCounter += 1
reactiveConfig = tmp
reactivePerf = getMaxPerformance(reactiveConfig)
print 'Best - False alarms: up = %d, down = %d' % (best_upCounter, best_downCounter)
print 'Predictive - False alarms: up = %d, down = %d' % (pred_upCounter, pred_downCounter)
print 'Reactive - False alarms: up = %d, down = %d' % (reactive_upCounter, reactive_downCounter)
print 'Best Configs = %d, Predictive Configs = %d, Reactive Configs = %d' %\
(bestCounter, predCounter, reactiveCounter)
g = Gnuplot(debug=0)
g('reset')
#g('set size 0.65,0.65')
g('set encoding iso_8859_1')
g('set terminal postscript eps enhanced monochrome')
g('set output "carlos2.ps"')
g('set key top left')
g('set xlabel "Tempo (s)"')
g('set xrange [0:%d]' % (len(data) / step))
spacing = 15
tics = ','.join(['"%d" %d' % (x * step * spacing, x * spacing) for x in range(1, len(data) / (step * spacing) + 1)])
g('set xtics (%s)' % tics)
g('set ylabel "Requisições"'.encode('iso_8859_1'))
g('set grid')
#plot incoming requests avg
with_plot = 'steps'
#plot configurations
pi1 = Data(avgs, with_=with_plot,title='Carga')
pi2 = Data(reactivePerfs, with_=with_plot,title='Reativo')
pi_gamma = Data(gammas, with_=with_plot,title='Gamma')
#pi3 = Data(predPerfs, with_=with_plot,title='Preditivo')
#pi4 = Data(bestPerfs, with_=with_plot,title='Perfect')
#g.plot(pi1,pi2)
#g.plot(pi_gamma)
#raw_input()
print 'Done'