Merge work

1 files changed, 100 insertions, 42 deletions

diff --git a/2019-ICA3PP.org b/2019-ICA3PP.org
index b0a1bfb..75704b3 100644
--- a/2019-ICA3PP.org
+++ b/2019-ICA3PP.org
@@ -299,7 +299,6 @@ and transmission technologies.
 
 
 * Experimental setup 
-  \hl{Ajouter \% de bande passante utilisé par les applis low-rate}
   #+Latex: \label{sec:model}
   In this section, we describe the experimental setup employed to
   acquire energy measurements for each of the three parts of our
@@ -316,7 +315,7 @@ and transmission technologies.
    randomly in a rectangle of $400m^2$ around the AP which corresponds
    to a typical use case for a home environment. All
    the cell nodes employ the default WIFI energy model provided by ns3. The different
-   energy values used by the energy model are provided on Table \ref{tab:wifi-energy}. These parameters
+   energy values used by the energy model are provided in Table 1. These parameters
    were extracted from previous work\cite{halperin_demystifying_nodate,li_end--end_2018} On
    IEEE 802.11n. Besides, we suppose that the energy source of each
    nodes is not limited during the experiments. Thus each node
@@ -333,7 +332,7 @@ and transmission technologies.
      \centering
      \caption{Simulations Energy Parameters}
      \label{tab:wifi-energy}
-     \subtable[Wifi]{
+     \subtable[IoT part]{
        \begin{tabular}{@{}lr@{}}
          Parameter      & Value  \\ \midrule
          Supply Voltage & 3.3V   \\
@@ -342,11 +341,11 @@ and transmission technologies.
          Idle           & 0.273A \\ \bottomrule
      \end{tabular}}
      \hspace{0.3cm}
-     \subtable[Network]{
+     \subtable[Network part]{
        \label{tab:net-energy}
        \begin{tabular}{@{}lr@{}}
          Parameter  & Value \\ \midrule
-         Idle       & 1W           \\ 
+         Idle       & 0.00001W           \\ 
          Bytes (Tx/Rx)  & 3.4nJ           \\ 
          Pkt (Tx/Rx)    & 192.0nJ         \\ \bottomrule
        \end{tabular}
@@ -367,7 +366,7 @@ and transmission technologies.
    model for the dynamic energy consumption
    \cite{sivaraman_profiling_2011,Serrano2015}, and it includes also a static energy consumption.
    The different 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
+   network part are shown in left part of Table 1 and come from previous work
    \cite{cornea_studying_2014-1}.
 
 ** Cloud Part
@@ -427,14 +426,14 @@ In this section, we analyze the experimental results.
    % \usepackage{booktabs}
    \begin{table*}[]
    \centering
-   \caption{Sensors transmission interval effects}
+   \caption{Sensors transmission interval effects with 15 sensors}
    \label{tab:sensorsSendIntervalEffects}
    \begin{tabular}{@{}lrrrrr@{}}
    \toprule
-   Sensors Send Interval        & 10s            & 30s            & 50s       & 70s       & 90s      \\ \midrule
-   Sensors Power Consumption    & 13.517\hl{94}W & 13.517\hl{67}W & 13.51767W & 13.51767W & 13.517\hl{61}W \\
-   Network Power Consumption    & 10.441\hl{78}W & 10.441\hl{67}W & 10.44161W & 10.44161W & 10.441\hl{61}W \\
-   End-to-end Application Delay & 0.09951s       & 0.10021s       & 0.10100s  & 0.10203s  & 0.10202s  \\ \bottomrule
+   Transmission Interval        & 10s            & 30s            & 50s       & 70s       & 90s      \\ \midrule
+   Sensor Power     & 13.517\hl{94}W & 13.517\hl{67}W & 13.51767W & 13.51767W & 13.517\hl{61}W \\
+   Network Power     & 0.441\hl{88}W & 0.441\hl{77}W & 0.44171W & 0.44171W & 0.441\hl{71}W \\
+   Application Delay & 0.09951s       & 0.10021s       & 0.10100s  & 0.10203s  & 0.10202s  \\ \bottomrule
    \end{tabular}
    \end{table*}
    #+END_EXPORT
@@ -461,8 +460,8 @@ In our case with small and sporadic network traffic, these results show that wit
     #+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.}
+      \includegraphics[width=0.5\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}
     #+END_EXPORT
@@ -477,6 +476,16 @@ In our case with small and sporadic network traffic, these results show that wit
    the PUE to include the external energy costs like server cooling
    and data center facilities \cite{Ehsan}.
 
+   #+BEGIN_EXPORT latex
+   \begin{figure}
+     \centering
+     \includegraphics[width=0.8\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{figure}
+   #+END_EXPORT
+
+
    Firstly, we analyze the impact of the VM allocated memory on the server energy
    consumption. Figure \ref{fig:vmSize} depicts the server energy consumption according to the VM
    allocated memory for 20 sensors sending data every 10s. Note that
@@ -489,15 +498,6 @@ In our case with small and sporadic network traffic, these results show that wit
    heavy memory accesses and processing time. Thus, remaining experiments are based on VM with 1024MB
    of allocated memory.
 
-   #+BEGIN_EXPORT latex
-   \begin{figure}
-     \centering
-     \includegraphics[width=0.8\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{figure}
-   #+END_EXPORT
-
    Next, we study the effects of increasing the number of sensors on the server energy consumption.
    Figure \ref{fig:sensorsNumber-server} presents the results of the average server energy
    consumption when varying the number of sensors from 20 to 500, while Figure
@@ -567,7 +567,14 @@ In our case with small and sporadic network traffic, these results show that wit
       other IoT devices belonging to the same application and the
       server hosting the VM also hosts other VMs. Furthermore, the
       server belongs to a data center and takes part in the overall
-      energy drawn to cool the server room. 
+      energy drawn to cool the server room.
+
+   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 1, 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.
    
    Concerning the sharing of the network costs, for each router, we
    consider its aggregate bandwidth (on all the ports), its average
@@ -582,10 +589,12 @@ In our case with small and sporadic network traffic, these results show that wit
   
    where $P_{static}^{device}$ is the static power consumption of the
    network device (switch fabrics for instance or gateway),
-   $Bandwidth^{application }$ is the bandwidth used by our IoT application,
+   $Bandwidth^{application }$ Is the bandwidth used by our IoT application,
    $AggregateBandwidth^{device }$ is the overall aggregated bandwidth of the
    network device on all its ports, and $LinkUtilization^{device}$ is the
-   effective link utilization percentage. The formula includes the
+   effective link utilization percentage. The $Bandwidth^{application }$
+   depends on the transmission frequency in our use-case.
+   The formula includes the
    link utilization in order to charge for the effective energy cost
    per trafic and not for the theoretical upper bound which is the
    link bandwidth. Indeed, using such an upper bound leads to greatly
@@ -595,7 +604,30 @@ In our case with small and sporadic network traffic, these results show that wit
    Similarly, for each network port, we take the share attributable to
    our application: the ratio of our bandwidth utilization over the
    port bandwidth multiplied by the link utilization and the overall
-   static power consumption of the port.
+   static power consumption of the port. Table \ref{tab:netbidules}
+   summarizes the parameters used in our model, they are taken from
+   \cite{mahadevan_power_2009,Hassidim2013}. These are the parameters
+   used in our formula to compute the values that we used in the
+   simulations and that are presented in left part of Table 1. 
+
+
+   
+   #+BEGIN_EXPORT latex
+   \begin{table}[]
+     \centering
+     \caption{Network Devices Parameters}
+     \label{tab:netbidules}
+       \begin{tabular}{l|l}
+         Device      & ~Parameters  \\ \midrule
+         Gateway & ~Static power = 8.3 Watts, Bandwidth = 54Mbps, Utilization = 10\%   \\
+         Core router & ~Static power = 555 Watts, 48 ports of 1 Gbps, Utilization = 25\% \\
+         Edge switch~ & ~Static power = 150 Watts, 48 ports of 1 Gbps, Utilization = 25\% \\
+         \bottomrule
+     \end{tabular}}
+   \end{table}
+   #+END_EXPORT
+
+
 
    For the sharing of the Cloud costs, we take into account the number
    of VMs that a server can host, the CPU utilization of a VM and the
@@ -623,28 +655,49 @@ In our case with small and sporadic network traffic, these results show that wit
 
    The Figure \ref{fig:end-to-end} represents the end-to-end system
    energy consumption using the model described above while varying
-   the number of sensors. The values are extracted from the
-   experiments presented in the previous section.
-
-   Note that, for small-scale systems, the server energy consumption
-   is dominant compared to the energy consumed by the 
-   sensors. However, since we are using a single server, large-scale sensors deployment lead to an
-   increasing consumption of energy in the IoT part. On the other side, network energy consumption
-   is stable regarding the number of sensors since the system use case does not required large data
-   transfers. Thus, to save energy, we should maximize the number
-   of sensors handle by each cloud server while keeping reasonable sensors request intervals.
+   the number of sensors for a transmission interval of 10
+   seconds. The values are extracted from the experiments presented in
+   the previous section. 
 
    #+BEGIN_EXPORT latex
    \begin{figure}
      \centering
      \hspace{1cm}
-     \includegraphics[scale=0.4]{plots/final.png}
+     \includegraphics[scale=0.35]{plots/final.png}
      \label{fig:end-to-end}
      \caption{End-to-end network energy consumption using sensors interval of 10s}
    \end{figure}
    #+END_EXPORT
    
 
+   Note that, for small-scale systems, with WiFi IoT devices, the IoT
+   sensor part is dominant in the overall energy consumption. Indeed,
+   the IoT application induces a very small cost on Cloud and network
+   infrastructures compared to the IoT device cost. But, our model
+   assumes that a single VM is handling multiple users (up to 300
+   sensors), thus being energy-efficient. Conclusions would be
+   different with one VM per user in the case of no over-commitment on
+   the Cloud side. For the network infrastructure, in our case of
+   low-bandwidth utilization (one data packet every 10 seconds), the
+   impact is almost negligible.
+
+   Another way of looking at these results is to observe that only for
+   a high number of sensors (> 300), the power consumption of Cloud and
+   network parts start to be negligible (few percent). It means that,
+   if IoT applications handle clients one by one (i.e. one VM per
+   client), the impact is high on cloud and network part if they have
+   only few sensors. The energy efficiency is really poor for only few
+   devices: with 20 IoT sensors, the overall energy cost to handle these
+   devices is 2.5 times the energy consumption of the IoT devices
+   themselves. Instead of increasing the number of sensors, which
+   would result in a higher overall energy consumption, one should
+   focus on reducing the energy consumption of IoT devices, especially
+   WiFi devices which are common due to WiFi availability
+   everywhere. One could also focus on improving the energy cost of
+   Cloud and network infrastructure for low-bandwidth applications and
+   few devices.
+
+
 
 * Conclusion 
 #+LaTeX: \label{sec:cl}
@@ -666,12 +719,17 @@ we propose an end-to-end energy consumption model.
 This model provides insights on the hidden part of the iceberg: the
 impact of IoT devices on the energy consumption of Cloud and network
 infrastructures. On our use-case, we show that for a given sensor, its
-larger energy consumption is on the Cloud part. Consequently, with the
+larger energy consumption is on the sensor part. But the impact on the
+Cloud and network part is huge when using only few sensors with
+low-bandwidth applications.
+Consequently, with the
 IoT exploding growth, it becomes necessary to improve the energy
-efficiency of applications hosted on Cloud infrastructures.
+efficiency of applications hosted on Cloud infrastructures and of IoT devices.
 Our future work includes studying other types of IoT wireless
-transmission techniques and IoT applications in order to increase the
-applicability of our model.
+transmission techniques that would be more energy-efficient. We also
+plan to study other
+IoT applications in order to increase the applicability of our model
+and provide advice for increasing the energy-efficiency of IoT infrastructures.
 
 
 
@@ -1254,7 +1312,7 @@ applicability of our model.
     #+RESULTS:
     [[file:plots/sendInterval-cloud.png]]
 
-  
+    
 * Emacs settings :noexport:
   # Local Variables:
   # eval:    (unless (boundp 'org-latex-classes) (setq org-latex-classes nil))