save space

1 files changed, 23 insertions, 18 deletions

diff --git a/2019-CloudCom.tex b/2019-CloudCom.tex
index 4380b03..f419ede 100644
--- a/2019-CloudCom.tex
+++ b/2019-CloudCom.tex
@@ -66,7 +66,7 @@ This increase in number of devices implies an increase in the energy
 needed to manufacture and utilize them. Yet, the overall energy bill
 of IoT also comprises indirect costs, as it relies on computing and 
 networking infrastructures that consume energy to enable smart
-services. Indeed, IoT devices communicate with Cloud computing
+services. Indeed, IoT devices employ Cloud computing
 infrastructures to store, analyze and share their data.  
 
 In February 2019, a report by Cisco stated that ``IoT connections will
@@ -82,7 +82,7 @@ the iceberg: their use induce energy costs in communication and cloud
 infrastructures. In this paper, we estimate the overall energy
 consumption of an IoT device environment by combining simulations and
 real measurements. We focus on a given application with low bandwidth
-requirement and we evaluate its overall energy consumption: from the
+requirements, and we evaluate its overall energy consumption: from the
 device, through telecommunication networks, and up to the Cloud data
 center hosting the application. From this analysis, we derive an
 end-to-end energy consumption model that can be used to assess the
@@ -512,10 +512,19 @@ It means that the power consumption of the server is multiplied by
 the PUE~\cite{Ehsan}.
 
 \begin{figure*}[htbp]
+\begin{minipage}[t]{0.65\textwidth}
   \centering
-  \includegraphics[width=.6\linewidth]{./plots/vmSize-cloud.png}
+  \includegraphics[width=.9\linewidth]{./plots/vmSize-cloud.png}
   \caption{Server power consumption multiplied by the PUE (= 1.2) using 20 sensors sending data every 10s for various VM memory sizes}
   \label{fig:vmSize}
+\end{minipage}
+\hspace{0.5cm}
+\begin{minipage}[t]{0.27\textwidth}
+  \centering
+    \includegraphics[width=1.\linewidth]{./plots/sensorsNumberLine-cloud.png}
+    \caption{Average server power consumption multiplied by the PUE (= 1.2) for sensors sending data every 10s}
+    \label{fig:sensorsNumber-server}
+\end{minipage}
 \end{figure*}
 
 
@@ -561,20 +570,6 @@ model will in fact share the static power consumption of the server
 among the VMs it can host, depending on their VM size (allocated CPU and
 RAM). This model is detailed in Section~\ref{sec:discuss}.
 
-\begin{figure}[htbp]
-  \centering
-    \includegraphics[width=0.55\linewidth]{./plots/sensorsNumberLine-cloud.png}
-    \caption{Average server power consumption multiplied by the PUE (= 1.2) for sensors sending data every 10s}
-    \label{fig:sensorsNumber-server}
-\end{figure}
-  
-
-\begin{figure}[htbp]
-  \centering
-    \includegraphics[width=0.55\linewidth]{./plots/WPS-cloud.png}
-    \caption{Average sensors power cost on the server hosting only our VM with PUE (= 1.2) for sensors sending data every 10s}
-    \label{fig:sensorsNumber-WPS}
-\end{figure}
 
 A last parameter can leverage server energy consumption, namely
 sensors transmission interval. In addition 
@@ -586,12 +581,22 @@ interval is, the more server energy consumption peaks
 occur. Therefore, it leads to an increase of the server energy consumption.
 
 \begin{figure*}[htbp]
+\begin{minipage}[t]{0.65\textwidth}
   \centering
-  \includegraphics[width=0.6\linewidth]{plots/sendInterval-cloud.png}
+  \includegraphics[width=0.9\linewidth]{plots/sendInterval-cloud.png}
   \caption{Server energy consumption multiplied by the PUE (= 1.2) for 50 sensors sending requests at different transmission interval.}
   \label{fig:sensorsFrequency}
+\end{minipage}
+\hspace{0.5cm}
+\begin{minipage}[t]{0.27\textwidth}
+  \centering
+    \includegraphics[width=1.\linewidth]{./plots/WPS-cloud.png}
+    \caption{Average sensors power cost on the server hosting only our VM with PUE (= 1.2) for sensors sending data every 10s}
+    \label{fig:sensorsNumber-WPS}
+\end{minipage}
 \end{figure*}
 
+
 In the next section, we use the hints detailed here and extracted from the
 real and simulated experiments in order to provide an end-to-end energy
 model that can be used for low-bandwidth IoT applications.