From bc413520932ba82ed9cc2d1b41db9de6b4cc8a4c Mon Sep 17 00:00:00 2001 From: ORGERIE Anne-Cecile Date: Fri, 18 Oct 2019 21:37:35 +0200 Subject: [PATCH 1/3] save space --- 2019-CloudCom.tex | 41 +++++++++++++++++++++++------------------ 1 file 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. From b2378fbbd9ca25a7c7426d875836ca169ac3d010 Mon Sep 17 00:00:00 2001 From: ORGERIE Anne-Cecile Date: Fri, 18 Oct 2019 21:53:35 +0200 Subject: [PATCH 2/3] ajout app charac --- 2019-CloudCom.tex | 12 ++++++++++++ 1 file changed, 12 insertions(+) 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 From a01a0c67a785ecc1d51a21074e52986072b32a98 Mon Sep 17 00:00:00 2001 From: ORGERIE Anne-Cecile Date: Fri, 18 Oct 2019 22:11:18 +0200 Subject: [PATCH 3/3] simulated and real experiments --- 2019-CloudCom.tex | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) 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}\\