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ORGERIE Anne-Cecile 2019-10-18 21:37:35 +02:00
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2019-CloudCom.tex
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@ -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 needed to manufacture and utilize them. Yet, the overall energy bill
of IoT also comprises indirect costs, as it relies on computing and of IoT also comprises indirect costs, as it relies on computing and
networking infrastructures that consume energy to enable smart 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. infrastructures to store, analyze and share their data.
In February 2019, a report by Cisco stated that ``IoT connections will 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 infrastructures. In this paper, we estimate the overall energy
consumption of an IoT device environment by combining simulations and consumption of an IoT device environment by combining simulations and
real measurements. We focus on a given application with low bandwidth 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 device, through telecommunication networks, and up to the Cloud data
center hosting the application. From this analysis, we derive an center hosting the application. From this analysis, we derive an
end-to-end energy consumption model that can be used to assess the 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}. the PUE~\cite{Ehsan}.
\begin{figure*}[htbp] \begin{figure*}[htbp]
\begin{minipage}[t]{0.65\textwidth}
\centering \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} \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} \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*} \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 among the VMs it can host, depending on their VM size (allocated CPU and
RAM). This model is detailed in Section~\ref{sec:discuss}. 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 A last parameter can leverage server energy consumption, namely
sensors transmission interval. In addition 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. occur. Therefore, it leads to an increase of the server energy consumption.
\begin{figure*}[htbp] \begin{figure*}[htbp]
\begin{minipage}[t]{0.65\textwidth}
\centering \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.} \caption{Server energy consumption multiplied by the PUE (= 1.2) for 50 sensors sending requests at different transmission interval.}
\label{fig:sensorsFrequency} \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*} \end{figure*}
In the next section, we use the hints detailed here and extracted from the 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 real and simulated experiments in order to provide an end-to-end energy
model that can be used for low-bandwidth IoT applications. model that can be used for low-bandwidth IoT applications.