Update figure 8

2019-ICA3PP.org

@@ -1101,7 +1101,7 @@ applicability of our model.
       data100=data%>%filter(nbSensors==100)%>%mutate(energy=mean(energy)) %>% slice(1L)
       data300=data%>%filter(nbSensors==300)%>%mutate(energy=mean(energy)) %>% slice(1L)
       dataCloud=rbind(data20,data100,data300)%>%mutate(sensorsNumber=nbSensors)%>%mutate(type="Cloud")%>%select(sensorsNumber,energy,type)
-
+      dataCloud=dataCloud%>%mutate(energy=energy/7) # Divide by 7 because 14 core so 1 machine can host 14 vm but we use redundancy (2VM for 1app)
 
       # Network
       data=loadData("./logs/ns3/last/data.csv")
@@ -1126,7 +1126,6 @@ applicability of our model.
       fakeData$type=factor(fakeData$type,ordered=TRUE,levels=c("Sensors","Network","Cloud"))
 
       # Plot
-      fakeData=fakeData%>%mutate(energy=energy/7) # Divide by 7 because 14 core so 1 machine can host 14 vm but we use redundancy (2VM for 1app)
       p=ggplot(fakeData)+geom_bar(position="dodge2",colour="black",aes(x=sensorsNumber,y=energy,fill=type),stat="identity")+ 
       xlab("Sensors Number")+ylab("Power Consumption (W)")+guides(fill=guide_legend(title="System Part"))
       p=applyTheme(p)+theme(text = element_text(size=16))