From bc413520932ba82ed9cc2d1b41db9de6b4cc8a4c Mon Sep 17 00:00:00 2001
From: ORGERIE Anne-Cecile <anne-cecile.orgerie@inria.fr>
Date: Fri, 18 Oct 2019 21:37:35 +0200
Subject: save space

---
 2019-CloudCom.tex | 41 +++++++++++++++++++++++------------------
 1 file changed, 23 insertions(+), 18 deletions(-)

(limited to '2019-CloudCom.tex')

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.
-- 
cgit v1.2.3


From b2378fbbd9ca25a7c7426d875836ca169ac3d010 Mon Sep 17 00:00:00 2001
From: ORGERIE Anne-Cecile <anne-cecile.orgerie@inria.fr>
Date: Fri, 18 Oct 2019 21:53:35 +0200
Subject: ajout app charac

---
 2019-CloudCom.tex | 12 ++++++++++++
 1 file changed, 12 insertions(+)

(limited to '2019-CloudCom.tex')

diff --git a/2019-CloudCom.tex b/2019-CloudCom.tex
index f419ede..1cffd3c 100644
--- a/2019-CloudCom.tex
+++ b/2019-CloudCom.tex
@@ -260,6 +260,8 @@ application. While the derived model is more generic, we focus on a
 given application to obtain a precise use-case with accurate power
 consumption measurements.  
 
+\subsection{IoT device side}
+
 The Google Nest Thermostat relies on five sensors: temperature,
 humidity, near-field activity, far-field activity and ambient 
 light~\cite{Nest}. Periodical measurements, sent through wireless
@@ -284,6 +286,8 @@ home. We consider low-bandwidth applications where devices produces
 several network packets during each sensing period. The transmitting
 frequency can vary from one to several packet sent per minute~\cite{Cisco2019}. 
 
+
+\subsection{Cloud server side}
 We consider that the link between the AP and the Cloud is composed of
 several network switches and routers using Ethernet as shown in 
 Figure~\ref{fig:parts}. The number of routers on the path depends on the
@@ -303,6 +307,14 @@ same time.
   \label{fig:parts}
 \end{figure}
 
+The Cloud part of the application gathers the data sent by the IoT
+devices. These data are treated either on the fly (e.g. threshold
+detection) or periodically, and action commands are sent back to the
+device if required. For instance, if the user has set a targeted 
+temperature, the connected thermostat sends the measured
+temperature regularly, and once the target is reached, the Cloud server detects
+it, and sends back to the IoT device the command to pause the heater.
+
 In the following, we describe the experimental setup, the results and
 the derived end-to-end model. For all these steps, we decompose the overall
 IoT architecture into three parts: the IoT device part, the networking
-- 
cgit v1.2.3


From a01a0c67a785ecc1d51a21074e52986072b32a98 Mon Sep 17 00:00:00 2001
From: ORGERIE Anne-Cecile <anne-cecile.orgerie@inria.fr>
Date: Fri, 18 Oct 2019 22:11:18 +0200
Subject: simulated and real experiments

---
 2019-CloudCom.tex | 20 ++++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

(limited to '2019-CloudCom.tex')

diff --git a/2019-CloudCom.tex b/2019-CloudCom.tex
index 1cffd3c..6c7438c 100644
--- a/2019-CloudCom.tex
+++ b/2019-CloudCom.tex
@@ -444,9 +444,17 @@ if they are known, or estimated from specific energy models.
 \label{sec:org8201f68}
 \label{sec:eval}
 
+In this section, we analyze the experimental results. All the experiments
+concerning IoT devices and network parts (Table~\ref{tab:sensorsSendIntervalEffects} 
+and Figure~\ref{fig:sensorsNumber}) 
+are based on simulations using ns3, 
+while all the experiments on Cloud servers (Figures~\ref{fig:vmSize}, \ref{fig:sensorsNumber-server}, \ref{fig:sensorsFrequency}, 
+and~\ref{fig:sensorsNumber-WPS})
+are real measurements performed on 
+the Grid'5000 experimental platform.
+
 \subsection{IoT and Network Power Consumption}
 \label{sec:org1d05c1b}
-In this section, we analyze the experimental results.
    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 15 sensors using
    different transmission frequencies. The results provided by 
@@ -506,7 +514,7 @@ Consequently, sensors energy consumption is dominant, as each sensor adds its ow
 
 \begin{figure}[htbp]
   \centering
-  \includegraphics[width=0.65\linewidth]{./plots/numberSensors-WIFINET.png}
+  \includegraphics[width=0.75\linewidth]{./plots/numberSensors-WIFINET.png}
   \caption{Analysis of the variation of the number of sensors on the IoT/Network part energy consumption for a transmission interval of 10s.}
   \label{fig:sensorsNumber}
 \end{figure}
@@ -596,7 +604,7 @@ occur. Therefore, it leads to an increase of the server energy consumption.
 \begin{minipage}[t]{0.65\textwidth}
   \centering
   \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.}
+  \caption{Server power consumption multiplied by the PUE (= 1.2) for 50 sensors sending requests at different transmission interval.}
   \label{fig:sensorsFrequency}
 \end{minipage}
 \hspace{0.5cm}
@@ -621,7 +629,9 @@ To have an overview of the energy consumed by the overall system, it is importan
 end-to-end energy consumption.
 We detail here the model used to attribute the energy
 consumption of our application for each part of the
-architecture. For a given IoT device, we have:
+architecture. 
+
+For a given IoT device, we have:
 \begin{enumerate}
 \item For the IoT part, the entire consumption of the IoT device
 belongs to the system's accounted consumption.
@@ -635,12 +645,14 @@ server belongs to a data center and takes part in the overall
 energy drawn to cool the server room.
 \end{enumerate}
 
+
 Concerning the IoT part, we include the entire IoT device power
 consumption. Indeed, in our targeted low-bandwidth IoT application,
 the sensor is dedicated to this application. From Table~\ref{tab:params}, one can
 derive that the static power 
 consumption of one IoT sensor is around 0.9 Watts. Its dynamic part
 depends on the transmission frequency. So the power consumption of an IoT device:
+
 \begin{footnotesize}
 \begin{align*}
 P^{IoTdevice} & = P_{static}^{IoT} + P_{dynamic}^{IoT}\\
-- 
cgit v1.2.3

