def penaltyFunction(lamb, mu, numServers, simTime, numReps, maxMIPS, aggressivness):
mySim = Simulator(lam, mu, numServers, simTime, numRepsSim, maxMIPS, aggressivness)
mySim.runSimulation()
totalPenalty = 0
maxTemps = mySim.getMaxTemps()
responseTimes = mySim.getAvgResponseTime()
avgjobsinsys = mySim.getAvgJobsInSimulation()
avgUtilizations = mySim.getAvgServerUtils()
powerConsumedByServers = mySim.getPowerConsumptions()
for rep in powerConsumedByServers:
repIndex = powerConsumedByServers.index(rep)
print '*****************************************************'
print 'Log dump for rep', repIndex+1
print 'Avg avg utils: ', sum(avgUtilizations[repIndex]) / len(avgUtilizations[repIndex])
print 'Avg power consumed', sum(powerConsumedByServers[repIndex]) / len(powerConsumedByServers[repIndex])
print 'Avg Response Time', sum(responseTimes[repIndex]) / len(responseTimes[repIndex])
for i in range(0, len(rep)):
print 'Server', i, '\tUtil:', avgUtilizations[repIndex][i], '\tMax temp:', maxTemps[repIndex][i], '\t\tConsumed:', powerConsumedByServers[repIndex][i], '\tRT:', responseTimes[repIndex][i]
maxTempForRep = max(maxTemps[repIndex])
maxPowerConsForRep = max(powerConsumedByServers[repIndex])
maxUtilForRep = max(avgUtilizations[repIndex])
maxRTForRep = max(responseTimes[repIndex])
# Huge penalties for infeasible solutions
if maxTempForRep > maxTemperature:
totalPenalty += maxTempForRep
if maxUtilForRep > maxUtilization:
totalPenalty += maxUtilForRep
if maxRTForRep > maxResponseTime:
totalPenalty += maxRTForRep
# Calculate norms against the maximums for each list
# No actual use for this, penalties wouldn't make sense since the normalized scores can only be compared relative to the max...
# normMaxTemps = [float(i)/maxTempForRep for i in maxTemps[repIndex]].sort(reverse=True)
# normPowerConsumed = [float(i)/maxPowerConsForRep for i in powerConsumedByServers[repIndex]].sort(reverse=True)
# normAvgUtils = [float(i)/maxUtilForRep for i in avgUtilizations[repIndex]].sort(reverse=True)
# normResponseTimes = [float(i)/maxRTForRep for i in responseTimes[repIndex]].sort(reverse=True)
avgMaxTempForRep = sum(maxTemps[repIndex]) / len(maxTemps[repIndex])
avgPowerConsForRep = sum(powerConsumedByServers[repIndex]) / len(powerConsumedByServers[repIndex])
avgUtilForRep = sum(avgUtilizations[repIndex]) / len(avgUtilizations[repIndex])
avgRTForRep = sum(responseTimes[repIndex]) / len(responseTimes[repIndex])
for item in rep:
itemIndex = rep.index(item)
if maxTemps[repIndex][itemIndex] > avgMaxTempForRep:
totalPenalty += maxTemps[repIndex][itemIndex] - avgMaxTempForRep
if powerConsumedByServers[repIndex][itemIndex] > avgPowerConsForRep:
totalPenalty += powerConsumedByServers[repIndex][itemIndex] - avgPowerConsForRep
if avgUtilizations[repIndex][itemIndex] > avgUtilForRep:
totalPenalty += avgUtilizations[repIndex][itemIndex] - avgUtilForRep
if responseTimes[repIndex][itemIndex] > avgRTForRep:
totalPenalty += responseTimes[repIndex][itemIndex] - avgRTForRep
# ENDFOR
return totalPenalty
c = 30
lamb = desiredUtil * c
st = 100
reps = 1
maxMIPS = 2500000
aggressivness = 0.10
util = lamb / (float)(mu * c)
# def __init__(lamb, mu, c, simTime, reps):
mySim = Simulator(lamb, mu, c, st, reps, maxMIPS, c * aggressivness)
mySim.runSimulation()
throughput = mySim.getThroughput()
avgjobsinsys = mySim.getAvgJobsInSimulation()
powerConsumedByServers = mySim.getPowerConsumptions()
avgUtilizations = mySim.getAvgServerUtils()
maxTemps = mySim.getMaxTemps()
rrs = mySim.getAvgResponseTime()
roundedAvg = [round(elem, accuracy) for elem in avgjobsinsys]
roundedThroughput = [round(elem, accuracy) for elem in throughput]
print '*****************************************************'
print 'Params: (', lamb, ', ', mu, ', ', c, ', ', st, ', ', reps, ', ', maxMIPS, ')'
print 'Utilization: ', round(util, accuracy)
print 'Throughput: ', roundedThroughput
