def determineTargets(targetList, alphas, deeperList):
totalTraffic = long(sumTraffic(targetList) / numNonZero(targetList))
newTargetList = []
oldTargetList = []
Globals.ASSIGNLOG.write('Threshold: ' + str(totalTraffic * Globals.MINTGTTHRESH) + " < x < " + str(totalTraffic * Globals.MAXTGTTHRESH) + '\n')
for i in range(0, len(targetList)):
if targetList[i]['traffic'] > (totalTraffic * Globals.MAXTGTTHRESH) or Alphas.sumAllAlphaAssign(alphas) < Alphas.sumAllAlphaTarget(alphas):
Globals.ASSIGNLOG.write('\t' + str(targetList[i]['ip']) + " Beyond Threshold " + str(targetList[i]['traffic']) + '\n')
# Beyond Threshold: Need more rules
childList = IPRules.findChildRules(deeperList, targetList[i]['ip'], targetList[i]['wild'])
for j, child in enumerate(childList):
newTargetList.append(child)
if childList != []:
oldTargetList.append(targetList[i])
elif targetList[i]['traffic'] < (totalTraffic * Globals.MINTGTTHRESH):
Globals.ASSIGNLOG.write('\t' + str(targetList[i]['ip']) + " Under Threshold " + str(targetList[i]['traffic']) + '\n')
# Below Theshold: Reduce rules
(siblingIP, siblingWild) = IPRules.findSiblingRule(targetList[i])
siblingRule = IPRules.findMatch(targetList, siblingIP, siblingWild)
if siblingRule != []:
if siblingRule[0]['traffic'] <= (totalTraffic * Globals.MINTGTTHRESH) and \
targetList.index(siblingRule[0]) < i:
parentRule = IPRules.getParentRule(siblingRule[0])
Globals.ASSIGNLOG.write('\t' + str(parentRule['ip']) + '/' + str(parentRule['wild']) + ' combined\n')
newTargetList.append(parentRule)
oldTargetList.append(targetList[i])
oldTargetList.append(siblingRule[0])
else:
Globals.ASSIGNLOG.write('\t' + str(targetList[i]['ip']) + " Fine Threshold " + str(targetList[i]['traffic']) + '\n')
if targetList[i]['replica'] == -1:
newTargetList.append(targetList[i])
oldTargetList.append(targetList[i])
return (oldTargetList, newTargetList)
def updateAlphas():
oldTargetRules = copyTargets(Globals.TARGETRULES)
oldInstallRules = copyTargets(Globals.INSTALLEDRULES)
newAlphas = Alphas.getAlphas(Globals.ALPHAFILE)
Globals.log.info("NEW ALPHAS " + str(newAlphas))
Globals.log.info("OLD ALPHAS" + str(Globals.ALPHAS))
(oldTargetList, newTargetList) = Bins.determineTargets(oldTargetRules, Globals.ALPHAS, oldInstallRules)
(oldTargetList, newTargetList) = Bins.redistributeTargets(oldTargetRules, Globals.ALPHAS, newAlphas, oldTargetList, newTargetList)
# Bins.printTargetList(newTargetList)
totalAssignments = len(newTargetList)
Globals.log.info("Total Assigns: " + str(totalAssignments))
newAlphas = Alphas.distributeAlphas(newAlphas, Globals.ALPHAS, totalAssignments)
Globals.log.info("DISTRIBUTE NEW ALPHAS " + str(newAlphas))
newAlphas = Alphas.distributeEven(newAlphas, totalAssignments)
# newAlphas = Alphas.distributeOld(newAlphas, Globals.ALPHAS, totalAssignments)
Globals.log.info("DISTRIBUTE OLD ALPHAS " + str(newAlphas))
newAlphas = Alphas.distributeRemaining(newAlphas, totalAssignments)
Globals.log.info("REMAINING NEW ALPHAS " + str(newAlphas))
Alphas.updateAlphas(newAlphas, oldTargetList)
newTargetList = IPRules.assignReplicas(newAlphas, newTargetList)
Bins.printTargetList(newTargetList)
newRuleList = IPRules.getNewRules(Globals.TARGETRULES, newTargetList)
IPRules.printRules(newRuleList)
# (oldRules, newRules) = IPRules.redistributeRules(Globals.TARGETRULES, Globals.ALPHAS, newAlphas)
# newRuleList.extend(newRules)
# IPRules.updateAssigns(newRuleList, oldRules)
IPRules.updateAssigns(newRuleList)
Alphas.printAlphas(Globals.ALPHAS)
return newRuleList
