diff options
Diffstat (limited to '2019-Mascots.org')
| -rw-r--r-- | 2019-Mascots.org | 25 |
1 files changed, 15 insertions, 10 deletions
diff --git a/2019-Mascots.org b/2019-Mascots.org index 4d2044b..5c248c3 100644 --- a/2019-Mascots.org +++ b/2019-Mascots.org @@ -128,7 +128,7 @@ component, formatting, style, styling, insert The sensors requests are simulated using another server. This server is in charge to send hundred of requests to the VM in order to fill the database. Consequently, it is easy to vary the different requests characteristics namely: \textbf{1)} The number request, to virtually - add/remove sensors \textbf{2)} The requests frequency. + add/remove sensors \textbf{2)} The requests interval. * Evaluation [3 col] ** IoT/Network Consumption @@ -239,13 +239,17 @@ component, formatting, style, styling, insert A last parameter can leverage server energy consumption namely sensors send frequency. In addition to increasing the application accuracy, sensors send frequency increase network traffic - and database accesses. + and database accesses. Figure \ref{fig:sensorsFrequency} present the impact on the server energy + consumption of changing the send interval of 50 sensors to 1s, 10s and 30s. We can see that, the + more sensors send interval is low, the more server energy consumption peaks occurs. Therefore, it + leads to an increase in the server energy consumption. #+BEGIN_EXPORT latex \begin{figure} - - \caption{Freq} - + \centering + \includegraphics[scale=0.5]{plots/sendInterval-cloud.png} + \label{fig:sensorsFrequency} + \caption{Server energy consumption for 50 sensors sending request at different interval.} \end{figure} #+END_EXPORT @@ -472,7 +476,7 @@ component, formatting, style, styling, insert data=data%>%mutate(vmSize=as.character(vmSize)) ggplot(data) + geom_bar(aes(x=vmSize,y=energy),stat="identity")+expand_limits(y=c(75,100))+ylab("Server Energy Consumption (W)")+ - xlab("Simulation Time (s)")+scale_y_log10() + xlab("Experiment Time (s)")+scale_y_log10() ggsave("plots/vmSizeBar-cloud.png",dpi=90,height=3,width=6) @@ -703,7 +707,7 @@ component, formatting, style, styling, insert data=data%>%mutate(vmSize=paste0(vmSize," MB")) data=data%>%group_by(vmSize)%>%mutate(avgEnergy=mean(energy))%>%ungroup() p=ggplot(data,aes(x=time, y=energy)) + geom_line()+facet_wrap(~vmSize)+geom_hline(aes(yintercept=avgEnergy),color="Red",size=1.0)+expand_limits(y=c(0,40))+ylab("Server Energy Consumption (W)")+ - xlab("Simulation Time (s)") + xlab("Experiment Time (s)") p=applyTheme(p) @@ -728,7 +732,7 @@ component, formatting, style, styling, insert data=data%>%group_by(nbSensors)%>%mutate(avgEnergy=mean(energy))%>%ungroup() p=ggplot(data,aes(x=time, y=energy)) + geom_line()+facet_wrap(~nbSensors)+expand_limits(y=c(0,40))+ylab("Server Energy Consumption (W)")+ - xlab("Simulation Time (s)")+geom_hline(aes(yintercept=avgEnergy),color="Red",size=1.0) + xlab("Experiment Time (s)")+geom_hline(aes(yintercept=avgEnergy),color="Red",size=1.0) p=applyTheme(p) ggsave("plots/sensorsNumber-cloud.png",dpi=90,height=3,width=6) @@ -804,11 +808,12 @@ component, formatting, style, styling, insert data$sendInterval=fct_reorder(data$sendInterval,oldSendInterval) data=data%>%group_by(sendInterval)%>%mutate(avgEnergy=mean(energy))%>%ungroup() + print(data) p=ggplot(data,aes(x=time, y=energy)) + geom_line()+facet_wrap(~sendInterval)+expand_limits(y=c(0,40))+ylab("Server Energy Consumption (W)")+ - xlab("Simulation Time (s)")+geom_hline(aes(yintercept=avgEnergy),color="Red",size=1.0) + xlab("Experiment Time (s)")+geom_hline(aes(yintercept=avgEnergy),color="Red",size=1.0) p=applyTheme(p) - ggsave("plots/sendInterval-cloud.png",dpi=90,height=3,width=6) + ggsave("plots/sendInterval-cloud.png",dpi=120,height=3,width=6) #+END_SRC #+RESULTS: |