#+TITLE: Estimating the end-to-end energy consumption of IoT devices along with their impact on Cloud and telecommunication infrastructures
#+EXPORT_EXCLUDE_TAGS: noexport
#+STARTUP: hideblocks
#+OPTIONS: H:5 author:nil email:nil creator:nil timestamp:nil skip:nil toc:nil ^:nil
#+LATEX_CLASS: IEEEtran
#+LATEX_HEADER: \usepackage{hyperref}
#+LATEX_HEADER: \usepackage{booktabs}
#+LATEX_HEADER: \usepackage{subfigure}
#+LATEX_HEADER: \usepackage{graphicx}
#+LATEX_HEADER: \IEEEoverridecommandlockouts
#+LATEX_HEADER: \author{\IEEEauthorblockN{1\textsuperscript{st} Anne-Cécile Orgerie}
#+LATEX_HEADER: \IEEEauthorblockA{\textit{Univ Rennes, Inria, CNRS, IRISA, Rennes, France} \\
#+LATEX_HEADER: Rennes, France \\
#+LATEX_HEADER: anne-cecile.orgerie@irisa.fr}
#+LATEX_HEADER: \and
#+LATEX_HEADER: \IEEEauthorblockN{2\textsuperscript{nd} Loic Guegan}
#+LATEX_HEADER: \IEEEauthorblockA{\textit{Univ Rennes, Inria, CNRS, IRISA, Rennes, France} \\
#+LATEX_HEADER: Rennes, France \\
#+LATEX_HEADER: loic.guegan@irisa.fr}
#+LATEX_HEADER: }
#+BEGIN_EXPORT latex
\begin{abstract}
Information and Communication Technology takes a growing part in the worldwide energy consumption. One of the root causes of this increase lies in the multiplication of connected devices. Each object of the Internet-of-Things often does not consume much energy by itself. Yet, their number and the infrastructures they require to properly work have leverage. In this paper, we combine simulations and real measurements to study the energy impact of IoT devices. In particular, we analyze the energy consumption of Cloud and telecommunication infrastructures induced by the utilization of connected devices, and we propose an end-to-end energy consumption model for these devices.
\end{abstract}
\begin{IEEEkeywords}
component, formatting, style, styling, insert
\end{IEEEkeywords}
#+END_EXPORT
* Introduction [2 col]
* Related Work [1 col]
* Use-Case [1 col]
** Application Characteristic
#+BEGIN_COMMENT
The IoT part is composed of an Access Point (AP), connected to several sensors using WIFI. In the
system, the IoT part is considered as the part where the system data are created. In fact, the
data life cycle start when the sensors records their respective local temperature at a frequency
$f$ and the local timestamp. Then, these data are transmitted through the network along with an
arbitrary sensor id of 128 bits. Finally, the AP is in charge to transmit the data to the cloud
using the network part.
The network part is considered as the medium that link the IoT part to the cloud. It is composed
of several network switches and router and it is considered to be a wired network.
#+END_COMMENT
** Cloud Infrastructure
* System Model [2 col]
The system model is divided in two parts. First, the IoT and the Network part are models through
simulations. Then, the Cloud part is model using real servers connected to watt-meters. In this way,
it is possible to evaluate the end-to-end energy consumption of the system.
** IoT Part
In the first place, the IoT part is composed of several sensors connected to an AP which forms a
cell. It is model using the ns-3 network simulator. Thus, we setup between 5 and 15 sensors
connected to the AP using WIFI 5GHz 802.11n. The node are placed randomly in a rectangle of 400m2
around the AP which correspond to a typical real use case. All the nodes of the cell are setup
with the default WIFI energy model provided by ns-3. The different energy values used by the
energy model are provided on Table \ref{tab:wifi-energy}. These energy were extracted from
previous work\cite{halperin_demystifying_nodate,li_end--end_2018} on 802.11n. Note that we
suppose that the energy source of the cell nodes are unlimited and thus every nodes can
communicate until the end of all the simulations.
As a scenario, sensors send to the AP packets of 192 bits that include: \textbf{1)} A 128 bits
sensors id \textbf{2)} A 32 bits integer representing the temperature \textbf{3)} An integer
timestamp representing the temperature sensing time. The data are transmitted immediately at each
sensing interval $I$ varied from 1s to 60s. Finally, the AP is in charge of relaying data to the
cloud using the network part.
#+BEGIN_EXPORT latex
\begin{table}[]
\centering
\caption{Simulations Energy Parameters}
\label{tab:wifi-energy}
\subtable[Wifi]{
\begin{tabular}{@{}lr@{}}
Parameter & Value \\ \midrule
Supply Voltage & 3.3V \\
Tx & 0.38A \\
Rx & 0.313A \\
Idle & 0.273A \\ \bottomrule
\end{tabular}}
\hspace{0.3cm}
\subtable[Network]{
\label{tab:net-energy}
\begin{tabular}{@{}lr@{}}
Parameter & Value \\ \midrule
Idle & 1J \\
Bytes (Tx/Rx) & 3.4nJ \\
Pkt (Tx/Rx) & 192.0nJ \\ \bottomrule
\end{tabular}
}
\end{table}
#+END_EXPORT
** Network Part
The network part represents the network starting from the AP to the Cloud excluding the server.
It is also model into ns-3. We consider the server to be 9 hops away from the AP with a typical
round-trip latency of 100ms from the AP to the server. Each node from the AP to the Cloud is
assume to be network switches with static and dynamic network energy consumption. ECOFEN
\cite{orgerie_ecofen:_2011} is used to model the energy consumption of the network part. ECOFEN
is a ns-3 network energy module for ns-3 dedicated to wired network energy estimation. It is
based on an energy-per-bit model including static consumption by assuming a linear relation
between the amount of data sent to the network interface and the power consumption. The different
energy values used to instantiate the ECOFEN energy model for the network part are shown in Table
\ref{tab:net-energy} and come from previous work \cite{cornea_studying_2014-1}.
** Cloud Part
Finally, to measure the energy consumption of the server, we used real server from the
large-scale test-beds Grid5000 (G5K). In fact, G5K has a cluster called Nova composed of several
nodes which are connected to watt-meters. In this way, we can benefit from real energy
measurements. The server used in the experiment is composed of Intel Xeon E5-2620 processor with
64 GB of RAM and 600GB of disk space on a Linux based distribution. This server is configured to
use KVM as virtualization mechanism. We deploy a classical Linux x86_64 distribution on the
Virtual Machines (VM) along with a MySQL database. We different amount of allocated memory for
the VM namely 1024MB/2048MB/4096MB to highlight its effects on the server energy consumption.
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.
* Evaluation [3 col]
** IoT/Network Consumption
In a first place, we start by studying the impact of the sensors position on their energy
consumption. To this end, we run several simulations in ns-3 with different sensors position. The
results provided by Figure \ref{fig:sensorsPos} show that sensors position have a very low impact
on the energy consumption and on the 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.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[width=0.6\linewidth]{./plots/sensorsPosition-delayenergy.png}
\caption{Effects of sensors position on the application delay and the sensors energy consumption in a cell of 9 sensors.}
\label{fig:sensorsPos}
\end{figure}
#+END_EXPORT
Previous work \cite{li_end--end_2018} on similar scenario shows that increasing application
accuracy impact strongly the energy consumption in the context of data stream analysis. However,
in how case, application accuracy is driven by the sensing frequency and thus the transmit
frequency of the sensors. In this way, we vary the transmission frequency of the sensors from 1s
to 60s. Figure \ref{fig:frequency} present the effects of the sensors transmission frequency on
the IoT/Network part energy consumption. In case of small and sporadic network traffic, these
results show that with a reasonable transmission frequency the energy consumption of the
IoT/Network if almost not affected by the variation of this frequency.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[scale=0.45]{./plots/sendFrequency-energy.png}
\caption{Sensors send interval and its influence on the IoT/Network part energy consumption.}
\label{fig:frequency}
\end{figure}
#+END_EXPORT
The number of sensors is the dominant factor that leverage the energy consumption of the
IoT/Network part. Therefore, we varied the number of sensors in the Wifi cell to analyze its
impact. The figure \ref{fig:sensorsNumber} represents the energy consumption of each simulated
part. It is clear that the energy consume by the network is the dominant part. However, since the
number of sensors is increasing the energy consume by the network will become negligible face to
the energy consume by the sensors. In fact, deploying new sensors in the cell do not introduce
much network load. To this end, sensors energy consumption is dominant.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[width=0.6\linewidth]{./plots/numberSensors-WIFINET.png}
\caption{Analysis of the variation of the number of sensors on the IoT/Network part energy consumption.}
\label{fig:sensorsNumber}
\end{figure}
#+END_EXPORT
** Cloud Energy Consumption
In this End-To-End energy consumption study, cloud account for a huge part of the overall energy
consumption. According a report \cite{shehabi_united_2016-1} on United States data center energy
usage, the average Power Usage Effectiveness (PUE) of an hyperscale data center is 1.2. Thus, in
our analysis, all energy measurement on cloud server will account for this PUE.
In a first place, we analyse the impact of the VM allocated memory on the server energy
consumption. Figure \ref{fig:vmSize} depict the server energy consumption according to the VM
allocated memory for 20 sensors sending data every 10s. Note that red horizontal line represent
the average energy consumption for sample of energy value. We can see that at each sensing
interval, server face to peaks of energy consumption. However, VM allocated memory do not
influence energy consumption. In fact, simple database requests do not need any particular huge
memory access. Thus, remaining experiments are based on VM allocated memory of 1024MB.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[width=0.8\linewidth]{./plots/vmSize-cloud.png}
\caption{VM size impact on the server energy consumption using 20 sensors sending data every 10s}
\label{fig:vmSize}
\end{figure}
#+END_EXPORT
Next, we study the effects of increasing the number of sensors on the server energy consumption.
Figure \ref{fig:sensorsNumber-server} present the results of the average server energy
consumption when varying the number of sensors from 20 to 500 while Figure
\ref{fig:sensorsNumber-WPS} present the average server energy cost per sensors according to the
number of sensors. These shows a clear linear relation between the number of sensors and the
server energy consumption. Moreover, we can see that the more sensors we have per server, the
more energy we can save. In fact, since the idle server energy consumption is high, it is more
energy efficient to maximze the number of sensors per server. As showed on Figure
\ref{fig:sensorsNumber-WPS}, a significant amount of energy can be save when passing from 20
sensors to 300 per server.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\subfigure[Average server energy consumption]{
\includegraphics[width=0.4\linewidth]{./plots/sensorsNumberLine-cloud.png}
\label{fig:sensorsNumber-server}
}
\hspace{0.5cm}
\subfigure[Average sensors energy cost on server]{
\includegraphics[width=0.4\linewidth]{./plots/WPS-cloud.png}
\label{fig:sensorsNumber-WPS}
}
\caption{Server energy consumption for sensors sending data every 10s}
\label{fig:sensorsNumber-cloud}
\end{figure}
#+END_EXPORT
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.
#+BEGIN_EXPORT latex
\begin{figure}
\caption{Freq}
\end{figure}
#+END_EXPORT
** End-To-End Consumption
* Discussion [1 col]
* Conclusion [1 col]
* References [1 col]
\bibliographystyle{IEEEtran}
\bibliography{references}
* Data Provenance :noexport:
:PROPERTIES:
:header-args: :eval never-export
:END:
** Data Analysis (R Scripts)
*** Utils
**** R
RUtils is intended to load logs (data.csv) and providing
simple plot function for them.
#+NAME: RUtils
#+BEGIN_SRC R :eval never
library("tidyverse")
# Fell free to update the following
labels=c(time="Time (s)",sensorsSendInterval="Sensors Send Interval (s)", sensorsNumber="Number of sensors")
PUE=1.2
ns3SimTime=1800
g5kSimTime=300
loadData=function(path){
data=read_csv(path)
if("sensorsEnergy"%in%colnames(data)){ # If it is ns3 logs
data=data%>%mutate(sensorsEnergy=sensorsEnergy/ns3SimTime) # Convert to watts
data=data%>%mutate(networkEnergy=networkEnergy/ns3SimTime)
data=data%>%mutate(totalEnergy=totalEnergy/ns3SimTime)
}
else{ # Log from g5k
data=data%>%mutate(energy=energy*PUE) # Take into account PUE
data=data%>%filter(time<=g5kSimTime) # Remove extrats values (theorical sim time != real sim time)
}
}
# Get label according to varName
getLabel=function(varName){
if(is.na(labels[varName])){
return(varName)
}
return(labels[varName])
}
applyTheme=function(plot,...){
palette<- c("#00AFBB", "#E7B800", "#FC4E07","#0abb00")
plot=plot+
theme_bw(...)+
scale_fill_manual(values=palette)+
scale_colour_manual(values=palette)
return(plot)
}
#+END_SRC
**** Bash
***** Plots -> PDF
Merge all plots in plots/ folder into a pdf file.
#+NAME: plotToPDF
#+BEGIN_SRC bash :results output :noweb yes
orgFile="plots/plots.org"
<<singleRun>> # To get all default arguments
# Write helper function
function write {
echo "$1" >> $orgFile
}
echo "#+TITLE: Analysis" > $orgFile
write "#+LATEX_HEADER: \usepackage{fullpage}"
write "#+OPTIONS: toc:nil"
# Default arguments
write '\begin{center}'
write '\begin{tabular}{lr}'
write 'Parameters & Values\\'
write '\hline'
write "sensorsPktSize & ${sensorsPktSize} bytes\\\\"
write "sensorsSendInterval & ${sensorsSendInterval}s\\\\"
write "sensorsNumber & ${sensorsNumber}\\\\"
write "nbHop & ${nbHop}\\\\"
write "linksBandwidth & ${linksBandwidth}Mbps\\\\"
write "linksLatency & ${linksLatency}ms\\\\"
write '\end{tabular}'
write '\newline'
write '\end{center}'
for plot in $(find plots/ -type f -name "*.png")
do
write "\includegraphics[width=0.5\linewidth]{$(basename ${plot})}"
done
# Export to pdf
emacs $orgFile --batch -f org-latex-export-to-pdf --kill
#+END_SRC
***** CSVs -> data.csv (G5K)
Merge all energy file into one (and add additional fields).
#+NAME: G5K-mergeCSV
#+BEGIN_SRC sh
#!/bin/bash
whichLog="last"
logsLocation="logs/g5k"
whichLog="${logsLocation}/${whichLog}"
logFile="$(dirname $(readlink -f $0))"/$whichLog/simLogs.txt
dataFile=$(dirname "$logFile")/data.csv
getValue () {
line=$(echo "$1" | grep "Simulation para"|sed "s/Simulation parameters: //g")
key=$2
echo "$line"|awk 'BEGIN{RS=" ";FS=":"}"'$key'"==$1{gsub("\n","",$0);print $2}'
}
##### Add extract info to energy #####
IFS=$'\n'
for cmd in $(cat $logFile|grep "Simulation parameters")
do
nodeName=$(getValue $cmd serverNodeName)
from=$(getValue $cmd simStart)
to=$(getValue $cmd simEnd)
vmSize=$(getValue $cmd vmSize)
nbSensors=$(getValue $cmd nbSensors)
simKey=$(getValue $cmd simKey)
sendInterval=$(getValue $cmd sensorsSendInterval)
csvFile="$whichLog/${simKey}_${vmSize}VMSIZE_${nbSensors}NBSENSORS_${from}${to}.csv"
csvFileIDLE="$whichLog/${simKey}_${vmSize}VMSIZE_${nbSensors}NBSENSORS_${from}${to}_IDLE.csv"
tmpFile=${csvFile}_tmp
echo ts,energy,simKey,vmSize,nbSensors,time,state,sendInterval > $tmpFile
minTs=$(tail -n+2 $csvFile|awk -F"," 'BEGIN{min=0}$1<min||min==0{min=$1}END{print(min)}') # To compute ts field
minTsIDLE=$(tail -n+2 $csvFileIDLE|awk -F"," 'BEGIN{min=0}$1<min||min==0{min=$1}END{print(min)}') # To compute ts field
tail -n+2 ${csvFile} | awk -F"," '{print $0",'$simKey','$vmSize','$nbSensors',"$1-'$minTs'",sim,"'$sendInterval'}' >> $tmpFile
tail -n+2 ${csvFileIDLE} | awk -F"," '{print $0",'$simKey','$vmSize','$nbSensors',"$1-'$minTsIDLE'",IDLE,"'$sendInterval'}' >> $tmpFile
done
##### Fill dataFile #####
echo ts,energy,simKey,vmSize,nbSensors,time,state,sendInterval > $dataFile
for tmpFile in $(find ${whichLog}/*_tmp -type f)
do
tail -n+2 $tmpFile >> $dataFile
rm $tmpFile # Pay attention to this line :D
done
#+END_SRC
#+RESULTS: G5K-mergeCSV
#+RESULTS: mergeCSV
***** Log -> data.csv (ns3)
logToCSV extract usefull data from logs and put them into logs/data.csv.
#+NAME: NS3-logToCSV
#+BEGIN_SRC bash :results output
logsFolder="./logs/ns3/last/"
csvOutput="$logsFolder/data.csv"
# First save csv header line
aLog=$(find $logsFolder -type f -name "*.org"|head -n 1)
metrics=$(cat $aLog|grep "\-METRICSLINE\-"|sed "s/-METRICSLINE-//g")
echo $metrics | awk '{for(i=1;i<=NF;i++){split($i,elem,":");printf(elem[1]);if(i<NF)printf(",");else{print("")}}}' > $csvOutput
# Second save all values
for logFile in $(find $logsFolder -type f -name "*.org")
do
metrics=$(cat $logFile|grep "\-METRICSLINE\-"|sed "s/-METRICSLINE-//g")
echo $metrics | awk '{for(i=1;i<=NF;i++){split($i,elem,":");printf(elem[2]);if(i<NF)printf(",");else{print("")}}}' >> $csvOutput
done
#+END_SRC
#+RESULTS: NS3-logToCSV
*** Plot Scripts
**** Random R Scripts
Watt per sensor on server
#+BEGIN_SRC R :noweb yes :results output
<<RUtils>>
# Load data
data=loadData("./logs/g5k/last/data.csv")
data=data%>%filter(state=="sim",simKey=="nbSensors")%>%ungroup()
data=data%>%group_by(nbSensors)%>%mutate(avgEnergy=mean(energy))%>%distinct()%>%ungroup()
data=data%>%distinct(nbSensors,.keep_all=TRUE)
data=data%>%mutate(WPS=(avgEnergy/nbSensors))
print(data%>%select(WPS,nbSensors))
#+END_SRC
Impact of vm size
#+BEGIN_SRC R :results graphics :file plots/vmSizeBar-cloud.png
library("tidyverse")
PUE=1.2
# Load data
data=read_csv("./logs/g5k/last/data.csv")
data=data%>%filter(state=="sim",simKey=="vmSize")%>%mutate(energy=PUE*energy)%>%filter(time<=300)
data=data%>%group_by(vmSize)%>%mutate(energy=mean(energy))%>%slice(1L)%>%ungroup()
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()
ggsave("plots/vmSizeBar-cloud.png",dpi=90,height=3,width=6)
#+END_SRC
#+RESULTS:
[[file:plots/vmSizeBar-cloud.png]]
#+NAME: ssiNet
#+BEGIN_SRC R :noweb yes :results graphics :file plots/sensorsSendInterval-net.png
<<NS3-RUtils>>
# Load Data
data=read_csv("logs/ns3/last/data.csv")
data%>%filter(simKey=="SENDINTERVAL",sensorsNumber==20) %>% ggplot(aes(x=sensorsSendInterval,y=networkEnergy))+xlab(getLabel("sensorsSendInterval"))+ylab(getLabel("networkEnergy"))+
geom_line()+labs(title="For 20 sensors")
ggsave("plots/sensorsSendInterval-net.png",dpi=80)
#+END_SRC
Effect of the number of sensors on the application delay
#+BEGIN_SRC R :noweb yes :results graphics :file plots/delay-nbsensors.png
<<NS3-RUtils>>
# Load Data
data=read_csv("logs/ns3/last/data.csv")
data%>%filter(simKey=="NBSENSORS") %>% ggplot(aes(y=avgDelay,x=sensorsNumber))+xlab(getLabel("sensorsNumber"))+ylab(getLabel("avgDelay"))+
geom_line()+labs(title="For 20 sensors")
ggsave("plots/delay-nbsensors.png",dpi=80)
#+END_SRC
#+RESULTS:
[[file:plots/delay-nbsensors.png]]
#+NAME: ssiWifi
#+BEGIN_SRC R :noweb yes :results graphics :file plots/sensorsSendInterval-wifi.png
<<NS3-RUtils>>
data=read_csv("logs/ns3/last/data.csv")
data%>%filter(simKey=="SENDINTERVAL",sensorsNumber==20) %>% ggplot(aes(x=sensorsSendInterval,y=sensorsEnergy))+xlab(getLabel("sensorsSendInterval"))+ylab(getLabel("sensorsEnergy"))+
geom_line() + geom_line()+labs(title="For 20 sensors")
ggsave("plots/sensorsSendInterval-wifi.png",dpi=80)
#+END_SRC
#+BEGIN_SRC R :results graphics :noweb yes :file plot-final.png :session *R*
<<NS3-RUtils>>
simTime=1800
# Network
data=read_csv("logs/ns3/last/data.csv")
data=data%>%filter(simKey=="NBSENSORS")
dataC5=data%>%filter(sensorsNumber==5)%>% mutate(energy=networkEnergy/simTime) %>%select(energy,sensorsNumber)
dataC10=data%>%filter(sensorsNumber==10)%>%mutate(energy=networkEnergy/simTime) %>%select(energy,sensorsNumber)
dataNet=rbind(dataC5,dataC10)%>%mutate(type="Network")
# Network
dataS5=data%>%filter(sensorsNumber==5)%>% mutate(energy=sensorsEnergy/simTime) %>%select(energy,sensorsNumber)
dataS10=data%>%filter(sensorsNumber==10)%>%mutate(energy=sensorsEnergy/simTime) %>%select(energy,sensorsNumber)
dataS=rbind(dataS5,dataS10)%>%mutate(type="Sensors")
data=rbind(dataNet,dataS)%>%mutate(sensorsNumber=as.character(sensorsNumber))
ggplot(data)+geom_bar(aes(x=sensorsNumber,y=energy,fill=type),stat="identity")+xlab("Sensors Number")+ylab("Power Consumption (W)")+guides(fill=guide_legend(title="Part"))
ggsave("plot-final.png",dpi=80)
#+END_SRC
**** Plot In Paper
Figure
#+BEGIN_SRC R :noweb yes :results graphics :file plots/sendFrequency-energy.png
<<RUtils>>
data=loadData("logs/ns3/last/data.csv")
data=data%>%filter(simKey=="SENDINTERVAL",sensorsNumber==15)
p=ggplot(data,aes(y=totalEnergy,x=sensorsSendInterval))+
xlab(getLabel("sensorsSendInterval"))+ylab(getLabel("Sensors And Network\nEnergy Consumption (W)"))+
geom_line()+geom_point()+expand_limits(y=c(0,50))+
guides(color=guide_legend(title="Curves"))+
theme(axis.title.y.right = element_text(margin = margin(t = 0, r = -13, b = 0, l = 7)))
p=applyTheme(p)
ggsave("plots/sendFrequency-energy.png",dpi=100, width=3, height=2.8)
#+END_SRC
#+RESULTS:
[[file:plots/sendFrequency-energy.png]]
Figure Sensors Position ~ Energy/Delay
#+BEGIN_SRC R :noweb yes :results graphics :file plots/sensorsPosition-delayenergy.png
<<RUtils>>
tr=171 # Offset to center delay plot
data=loadData("logs/ns3/last/data.csv")
data=data%>%filter(simKey=="SENSORSPOS",sensorsNumber==9)
p=ggplot(data,aes(y=sensorsEnergy,x=positionSeed,color="Energy"))+xlab(getLabel("Sensors Position Seed"))+ylab(getLabel("Sensors Energy Consumption (W)"))+
geom_line()+geom_point()+geom_line(aes(y=(avgDelay-tr),color="Delay"))+geom_point(aes(y=(avgDelay-tr),color="Delay"))+expand_limits(y=c(0,15))+
scale_y_continuous(sec.axis = sec_axis(~.+tr, name = "Application Delay (s)")) +
guides(color=guide_legend(title="Curves"))
p=applyTheme(p)
p=p+theme(axis.title.y.right = element_text(margin = margin(t = 0, r = -8, b = 0, l = 10)))
ggsave("plots/sensorsPosition-delayenergy.png",dpi=80, width=4, height=3.2)
#+END_SRC
#+RESULTS:
[[file:plots/sensorsPosition-delayenergy.png]]
#+BEGIN_SRC R :noweb yes :results graphics :file plots/numberSensors-WIFINET.png :session *R*
<<RUtils>>
data=loadData("logs/ns3/last/data.csv")
data=data%>%filter(simKey=="NBSENSORS")
dataW=data%>%mutate(energy=sensorsEnergy)%>% mutate(type="Sensors") %>% select(sensorsNumber,energy,type)
dataN=data%>%mutate(energy=networkEnergy)%>% mutate(type="Network") %>% select(sensorsNumber,energy,type)
data=rbind(dataN,dataW)
data=data%>%mutate(sensorsNumber=as.character(sensorsNumber))
data=data%>%mutate(sensorsNumber=fct_reorder(sensorsNumber,as.numeric(sensorsNumber)))
data=data%>%filter(sensorsNumber%in%c(2,4,6,8,10))
p=ggplot(data)+geom_bar(aes(x=sensorsNumber,y=energy,fill=type),position="identity",stat="identity")+
xlab(getLabel("sensorsNumber"))+ ylab("Energy Consumption (W)") + guides(fill=guide_legend(title="")) +coord_flip()
p=applyTheme(p)+theme(text = element_text(size=15))
size=5
ggsave("plots/numberSensors-WIFINET.png",dpi=90,width=size,height=size-1)
#+END_SRC
#+RESULTS:
[[file:plots/numberSensors-WIFINET.png]]
Final plot: Energy cloud, network and sensors
#+BEGIN_SRC R :noweb yes :results graphics :file plots/final.png :session *R*
library("tidyverse")
# Load data
data=loadData("./logs/g5k/last/data.csv")
data=data%>%filter(state=="sim",simKey=="nbSensors")
# Cloud
data10=data%>%filter(nbSensors==20)%>%mutate(energy=mean(energy)) %>% slice(1L)
data100=data%>%filter(nbSensors==100)%>%mutate(energy=mean(energy)) %>% slice(1L)
data300=data%>%filter(nbSensors==300)%>%mutate(energy=mean(energy)) %>% slice(1L)
dataCloud=rbind(data10,data100,data300)%>%mutate(sensorsNumber=nbSensors)%>%mutate(type="Cloud")%>%select(sensorsNumber,energy,type)
approx=function(data1, data2,nbSensors){
x1=data1$sensorsNumber
y1=data1$energy
x2=data2$sensorsNumber
y2=data2$energy
a=((y2-y1)/(x2-x1))
b=y1-a*x1
return(a*nbSensors+b)
}
simTime=1800
# Network
data=read_csv("./logs/ns3/last/data.csv")
data=data%>%filter(simKey=="NBSENSORS")
dataC5=data%>%filter(sensorsNumber==5)%>% mutate(energy=networkEnergy/simTime) %>%select(energy,sensorsNumber)
dataC10=data%>%filter(sensorsNumber==10)%>%mutate(energy=networkEnergy/simTime) %>%select(energy,sensorsNumber)
dataNet=rbind(dataC5,dataC10)%>%mutate(type="Network")
# Sensors
dataS5=data%>%filter(sensorsNumber==5)%>% mutate(energy=sensorsEnergy/simTime) %>%select(energy,sensorsNumber)
dataS10=data%>%filter(sensorsNumber==10)%>%mutate(energy=sensorsEnergy/simTime) %>%select(energy,sensorsNumber)
dataS=rbind(dataS5,dataS10)%>%mutate(type="Sensors")
fakeNetS=tibble(
sensorsNumber=c(20,100,300,20,100,300),
energy=c(dataC10$energy,approx(dataC5,dataC10,100),approx(dataC5,dataC10,300),dataS10$energy,approx(dataS5,dataS10,100),approx(dataS5,dataS10,300)),
type=c("Network","Network","Network","Sensors","Sensors","Sensors")
)
fakeNetS=fakeNetS%>%mutate(sensorsNumber=as.character(sensorsNumber))
dataCloud=dataCloud%>%mutate(sensorsNumber=as.character(sensorsNumber))
data=rbind(fakeNetS,dataCloud)%>%mutate(sensorsNumber=as.character(sensorsNumber))
data=data%>%mutate(sensorsNumber=fct_reorder(sensorsNumber,as.numeric(sensorsNumber)))
p=ggplot(data)+geom_bar(position="dodge2",colour="black",aes(x=sensorsNumber,y=energy,fill=type),stat="identity")+
xlab("Sensors Number")+ylab("Power Consumption (W)")+guides(fill=guide_legend(title="Part"))
p=applyTheme(p)
ggsave("plots/final.png",dpi=80)
#+END_SRC
#+RESULTS:
[[file:plots/final.png]]
Impact of vm size
#+BEGIN_SRC R :noweb yes :results graphics :noweb yes :file plots/vmSize-cloud.png
<<RUtils>>
# Load data
data=loadData("./logs/g5k/last/data.csv")
data=data%>%filter(state=="sim",simKey=="vmSize")%>%filter(time<=300)
data=data%>%mutate(vmSize=paste0(vmSize," MB"))
data=data%>%group_by(vmSize)%>%mutate(avgEnergy=mean(energy))%>%ungroup()
p=ggplot(data,aes(x=time, y=energy)) + geom_line()+facet_wrap(~vmSize)+geom_hline(aes(yintercept=avgEnergy),color="Red",size=1.0)+expand_limits(y=c(0,40))+ylab("Server Energy Consumption (W)")+
xlab("Simulation Time (s)")
p=applyTheme(p)
ggsave("plots/vmSize-cloud.png",dpi=90,height=3,width=6)
#+END_SRC
#+RESULTS:
[[file:plots/vmSize-cloud.png]]
Impact of sensors number
#+BEGIN_SRC R :noweb yes :results graphics :file plots/sensorsNumber-cloud.png
<<RUtils>>
# Load data
data=loadData("./logs/g5k/last/data.csv")
data=data%>%filter(state=="sim",simKey=="nbSensors")%>%ungroup()
data=data%>%mutate(nbSensorsSort=nbSensors)
data=data%>%mutate(nbSensors=paste0(nbSensors," Sensors"))
data$nbSensors=fct_reorder(data$nbSensors, data$nbSensorsSort)
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)
p=applyTheme(p)
ggsave("plots/sensorsNumber-cloud.png",dpi=90,height=3,width=6)
#+END_SRC
#+RESULTS:
[[file:plots/sensorsNumber-cloud.png]]
#+BEGIN_SRC R :noweb yes :results graphics :file plots/sensorsNumberLine-cloud.png :session *R:2*
<<RUtils>>
# Load data
data=loadData("./logs/g5k/last/data.csv")
data=data%>%filter(state=="sim",simKey=="nbSensors")%>%ungroup()
data=data%>%group_by(nbSensors)%>%mutate(avgEnergy=mean(energy))%>%distinct()%>%ungroup()
data=data%>%distinct(nbSensors,.keep_all=TRUE)
data=data%>%mutate(WPS=(avgEnergy/nbSensors))
p=ggplot(data,aes(x=nbSensors, y=avgEnergy)) + geom_point() +geom_line()+
xlab(getLabel("sensorsNumber"))+ylab("Average Server Energy (W)")
p=applyTheme(p)+theme(text = element_text(size=14))+ expand_limits(y=108)
ggsave("plots/sensorsNumberLine-cloud.png",dpi=90,height=4,width=4)
#+END_SRC
#+RESULTS:
[[file:plots/sensorsNumberLine-cloud.png]]
#+BEGIN_SRC R :noweb yes :results graphics :file plots/WPS-cloud.png :session *R:2*
<<RUtils>>
# Load data
data=loadData("./logs/g5k/last/data.csv")
data=data%>%filter(state=="sim",simKey=="nbSensors")%>%ungroup()
data=data%>%group_by(nbSensors)%>%mutate(avgEnergy=mean(energy))%>%distinct()%>%ungroup()
data=data%>%distinct(nbSensors,.keep_all=TRUE)
data=data%>%mutate(WPS=(avgEnergy/nbSensors))
oldNb=data$nbSensors
data=data%>%mutate(nbSensors=as.character(nbSensors))
data$nbSensors=fct_reorder(data$nbSensors,oldNb)
p=ggplot(data,aes(x=nbSensors, y=WPS)) + geom_bar(stat="identity")+
xlab(getLabel("sensorsNumber"))+ylab("Server energy cost per sensors (W)")
p=applyTheme(p)+theme(text = element_text(size=14))+ theme(axis.title.y = element_text(margin = margin(t = 0, r = 8, b = 0, l = 0)))
ggsave("plots/WPS-cloud.png",dpi=90,height=4,width=4)
#+END_SRC
#+RESULTS:
[[file:plots/WPS-cloud.png]]
#+BEGIN_SRC R :noweb yes :results graphics :file plots/sendInterval-cloud.png
<<RUtils>>
# Load data
data=loadData("./logs/g5k/last/data.csv")
data=data%>%filter(state=="sim",simKey=="sendInterval")%>%ungroup()
oldSendInterval=data$sendInterval
data=data%>%mutate(sendInterval=paste0(sendInterval,"s"))
data$sendInterval=fct_reorder(data$sendInterval,oldSendInterval)
data=data%>%group_by(sendInterval)%>%mutate(avgEnergy=mean(energy))%>%ungroup()
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)
p=applyTheme(p)
ggsave("plots/sendInterval-cloud.png",dpi=90,height=3,width=6)
#+END_SRC
#+RESULTS:
[[file:plots/sendInterval-cloud.png]]
* Emacs settings :noexport:
# Local Variables:
# eval: (unless (boundp 'org-latex-classes) (setq org-latex-classes nil))
# eval: (add-to-list 'org-latex-classes
# '("IEEEtran" "\\documentclass[conference]{IEEEtran}\n \[NO-DEFAULT-PACKAGES]\n \[EXTRA]\n" ("\\section{%s}" . "\\section*{%s}") ("\\subsection{%s}" . "\\subsection*{%s}") ("\\subsubsection{%s}" . "\\subsubsection*{%s}") ("\\paragraph{%s}" . "\\paragraph*{%s}") ("\\subparagraph{%s}" . "\\subparagraph*{%s}")))
# End: