diff --git a/2019-ICA3PP.org b/2019-ICA3PP.org index bce8d9d..ecbd534 100644 --- a/2019-ICA3PP.org +++ b/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 diff --git a/2019-ICA3PP.pdf b/2019-ICA3PP.pdf index 9c7edb3..09e2a60 100644 Binary files a/2019-ICA3PP.pdf and b/2019-ICA3PP.pdf differ diff --git a/plots/vmSize-cloud.png b/plots/vmSize-cloud.png index 292cfbf..05ea89e 100644 Binary files a/plots/vmSize-cloud.png and b/plots/vmSize-cloud.png differ diff --git a/src/ns3/nix/simulator/main.cc b/src/ns3/nix/simulator/main.cc index a8ec8cf..0ad0b45 100644 --- a/src/ns3/nix/simulator/main.cc +++ b/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.destinationAddress << " delay = " <second.delaySum.GetSeconds()); + NS_LOG_UNCOND("Flow " < "<< t.destinationAddress << " delay = " <second.delaySum.GetSeconds()); + //NS_LOG_UNCOND("Flow " < "<< t.destinationAddress << " delay = " <second.delaySum.GetSeconds()/flow->second.rxPackets); } diff --git a/src/ns3/nix/simulator/simulator b/src/ns3/nix/simulator/simulator index 295bdbb..a59dbeb 100755 Binary files a/src/ns3/nix/simulator/simulator and b/src/ns3/nix/simulator/simulator differ