Update paper remarks

2019-ICA3PP.org

@@ -415,15 +415,13 @@ In this section, we analyze the experimental results.
 ** IoT and Network Power Consumption
    In a first place, we start by studying the impact of the sensors'
    transmission frequency on their energy
-   consumption. To this end, we run several simulations in ns3 with different frequencies. The
+   consumption. To this end, we run several simulations in ns3 with 15 sensors using different transmission frequencies. The
    results provided by Table \ref{tab:sensorsSendIntervalEffects} show
    that the transmission frequency has a very low impact
-   on the energy consumption and on the application delay. It has an impact of course, but it is very
+   on the energy consumption and on the cumulative end-to-end application delay. It has an impact of course, but it is very
    limited. This due to the fact that in such a scenario with very small number of communications
    spread over the time, sensors don't have to contend for accessing to the Wifi channel.
  
-\hl{TODO: définir le 'application delay' et le nombre de capteurs utilisés pour l'expérience de la table}
-
    #+BEGIN_EXPORT latex
    % Please add the following required packages to your document preamble:
    % \usepackage{booktabs}
@@ -433,10 +431,10 @@ In this section, we analyze the experimental results.
    \label{tab:sensorsSendIntervalEffects}
    \begin{tabular}{@{}lrrrrr@{}}
    \toprule
-   Sensors Send Interval      & 10s      & 30s      & 50s      & 70s      & 90s      \\ \midrule
-   Sensors Power Consumption  & 13.517\hl{94}W & 13.517\hl{67}W & 13.51767W & 13.51767W & 13.517\hl{61}W \\
-   Network Power Consumption  & 10.441\hl{78}W & 10.441\hl{67}W & 10.44161W & 10.44161W & 10.441\hl{61}W \\
-   Average Application Delay & 17.81360s & 5.91265s  & 3.53509s  & 2.55086s  & 1.93848s  \\ \bottomrule
+   Sensors Send Interval        & 10s      & 30s      & 50s      & 70s      & 90s      \\ \midrule
+   Sensors Power Consumption    & 13.517\hl{94}W & 13.517\hl{67}W & 13.51767W & 13.51767W & 13.517\hl{61}W \\
+   Network Power Consumption    & 10.441\hl{78}W & 10.441\hl{67}W & 10.44161W & 10.44161W & 10.441\hl{61}W \\
+   Cumulative Application Delay & 17.81360s & 5.91265s  & 3.53509s  & 2.55086s  & 1.93848s  \\ \bottomrule
    \end{tabular}
    \end{table*}
    #+END_EXPORT
@@ -515,8 +513,6 @@ In our case with small and sporadic network traffic, these results show that wit
    and are not shared among all the VMs that could be hosted on this
    server.
 
-   \hl{Figure 5 n'inclut pas le PUE non? le Pidle est bien à 97 Watts environ?}
-
    #+BEGIN_EXPORT latex
    \begin{figure}
      \centering
@@ -1083,7 +1079,7 @@ applicability of our model.
       xlab("Experiment Time (s)")
 
       p=applyTheme(p)
-
+      
       ggsave("plots/vmSize-cloud.png",dpi=90,height=3,width=6)  
     #+END_SRC
 

src/ns3/nix/simulator/main.cc

@@ -65,7 +65,8 @@ int main(int argc, char* argv[]){
   for (std::map< FlowId, FlowMonitor::FlowStats>::iterator flow=stats.begin(); flow!=stats.end(); flow++)
     {
       Ipv4FlowClassifier::FiveTuple  t = classifier->FindFlow(flow->first);
-      NS_LOG_UNCOND("Flow " <<t.sourceAddress<<  " -> "<< t.destinationAddress << " delay = " <<flow->second.delaySum.GetSeconds()); 
+      NS_LOG_UNCOND("Flow " <<t.sourceAddress<<  " -> "<< t.destinationAddress << " delay = " <<flow->second.delaySum.GetSeconds());
+      //NS_LOG_UNCOND("Flow " <<t.sourceAddress<<  " -> "<< t.destinationAddress << " delay = " <<flow->second.delaySum.GetSeconds()/flow->second.rxPackets); 
     }