Merge branch 'master' of https://gitlab.inria.fr/afahs/voila-orch_sim

voila.py

@@ -135,12 +135,14 @@ def CalculateFitness(Current,Previous,NodeObjects,Epsilon,Cs,Ca,fitnessAlpha):
 	#We want to also have the cost from 0 to 100
 	nbDedicated=len(GetNodesOfType("D",NodeObjects))
 	nbSpare=len(GetNodesOfType("S",NodeObjects))
-	minCost=Cd
+	#minCost=Cd
 	maxCost=(nbDedicated-1)*(Cd+Cr)+Cd+nbSpare*(Cs+Ca+Cr)
-	percentCost=((Cost-minCost)/(maxCost-minCost))*100
-	
+	#percentCost=((Cost-minCost)/(maxCost-minCost))*100
 	#alpha=0.5 #Fitness parameter
-	fitness=fitnessAlpha*(100-Epsilon)+(1-fitnessAlpha)*percentCost
+	#fitness=fitnessAlpha*(100-Epsilon)+(1-fitnessAlpha)*percentCost
+	percentCost=(Cost/maxCost)
+	fitness=fitnessAlpha*Epsilon+(1-fitnessAlpha)*percentCost
+
 	
 	return fitness
 
@@ -372,6 +374,12 @@ def GetBestWithCost(Cases,SaturationPerPod,VProximity,Ep0, Previous, NodeObjects
 		#Also get all the suitable cases (i.e. below the threshold)
 		if case.Ep < Ep0:
 			SuitableCases.append(case)
+	
+	if SuitableCases==[]: 
+		for case in Cases:
+			if case.Ep < MinEp+0.1: 
+				SuitableCases.append(Case)
+
 	#If we have suitable cases, we want to return the one with minimum cost as the best
 	#Otherwise, we return the current best
 	if SuitableCases != []:
@@ -385,10 +393,12 @@ def GetBestWithCost(Cases,SaturationPerPod,VProximity,Ep0, Previous, NodeObjects
 				MinCost=case.Cost
 				MinCostCase=case
 		BestCase=MinCostCase
+
 		
 	return BestCase
 	
 def GetBestWithFitness(Cases,SaturationPerPod,VProximity,Ep0, Previous, NodeObjects,Cs,Ca,fitnessAlpha):
+	assert fitnessAlpha>=0 and fitnessAlpha<=1; "The value of alpha "+fitnessAlpha+" is out of range" 
 	MinEp=100
 	BestCase=Cases[0]
 	SuitableCases=[]
@@ -408,12 +418,16 @@ def GetBestWithFitness(Cases,SaturationPerPod,VProximity,Ep0, Previous, NodeObje
 			SuitableCases.append(case)
 	#If we have suitable cases, we want to return the one with minimum cost as the best
 	#Otherwise, we return the current best
+	if SuitableCases==[]: 
+		for case in Cases:
+			if case.Ep < MinEp+0.1: 
+				SuitableCases.append(Case)
 	if SuitableCases != []:
 		MaxFitness=CalculateFitness(SuitableCases[0],Previous,NodeObjects,SuitableCases[0].Ep,Cs,Ca,fitnessAlpha)
 		MaxFitnessCase=SuitableCases[0]
 		for case in SuitableCases:
 			case.Fitness=CalculateFitness(case,Previous,NodeObjects,case.Ep,Cs,Ca,fitnessAlpha)
-			if case.Fitness>MaxFitness: 
+			if case.Fitness<MaxFitness: 
 				MaxFitness=case.Fitness
 				MaxFitnessCase=case
 		BestCase=MaxFitnessCase
@@ -958,6 +972,8 @@ def FixSaturationQoSFitness(Nodes,NodeObjects,SelectedNodes,start,TempCase,FullT
 			if number!=0:  # number=0 should return the solution, in this case else will be skipped and the function will go to the if number==0 directly
 				print("debug 16")
 				return Cases
+			else: 
+				return Best
 		else: # No scale up with one D solution was found, we have to try S
 			NewCases,Number,Condition=AutoScale(VProximity,Ep0,SaturationPerPod,
 									Timeout,TempCase,ProbMat,TestMat,NodeObjects,
@@ -1039,6 +1055,8 @@ def FixProximityQoSFitness(Nodes,NodeObjects,SelectedNodes,start,TempCase,FullTe
 			if number!=0:  # number=0 should return the solution, in this case else will be skipped and the function will go to the if number==0 directly
 				print("debug 19")
 				return Cases
+			else: 
+				return Best
 
 		else: 
 			NewCases,Number,Condition=AutoScale(VProximity,Ep0,SaturationPerPod,