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ORGERIE Anne-Cecile 2019-07-19 10:38:59 +02:00
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@ -493,7 +493,7 @@ In our case with small and sporadic network traffic, these results show that wit
\begin{figure}
\centering
\includegraphics[width=0.8\linewidth]{./plots/vmSize-cloud.png}
\caption{Server power consumption 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}
\end{figure}
#+END_EXPORT
@ -518,16 +518,16 @@ In our case with small and sporadic network traffic, these results show that wit
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\subfigure[Average server energy consumption]{
\subfigure[Average server energy consumption multiplied by the PUE (= 1.2)]{
\includegraphics[width=0.4\linewidth]{./plots/sensorsNumberLine-cloud.png}
\label{fig:sensorsNumber-server}
}
\hspace{0.5cm}
\subfigure[Average sensors energy cost on server]{
\subfigure[Average sensors energy cost on the server hosting only our VM]{
\includegraphics[width=0.4\linewidth]{./plots/WPS-cloud.png}
\label{fig:sensorsNumber-WPS}
}
\caption{Server energy consumption for sensors sending data every 10s}
\caption{Server energy consumption multiplied by the PUE (= 1.2) for sensors sending data every 10s}
\label{fig:sensorsNumber-cloud}
\end{figure}
#+END_EXPORT
@ -545,7 +545,7 @@ In our case with small and sporadic network traffic, these results show that wit
\begin{figure}
\centering
\includegraphics[scale=0.5]{plots/sendInterval-cloud.png}
\caption{Server energy consumption 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}
\end{figure}
#+END_EXPORT
@ -554,10 +554,8 @@ In our case with small and sporadic network traffic, these results show that wit
#+LaTeX: \label{sec:discuss}
To have an overview of the energy consumed by the overall system, it is important to consider the
end-to-end energy consumption. The Figure \ref{fig:end-to-end} represents the end-to-end system
energy consumption while varying the number of sensors. The values
are extracted from the experiments presented in the previous
section. We detail here the model used to attribute the energy
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:
1. For the IoT part, the entire consumption of the IoT device
@ -578,15 +576,15 @@ In our case with small and sporadic network traffic, these results show that wit
part as follows:
#+BEGIN_EXPORT latex
\[P_{static}^{netdevice} = \frac{P_{static}^{device} \times Bandwidth^{application}}{Aggregatebandwidth^{device}
\[P_{static}^{netdevice} = \frac{P_{static}^{device} \times Bandwidth^{application}}{AggregateBandwidth^{device}
\times LinkUtilization^{device}}\]
#+End_EXPORT
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,
$Aggregatebandwidth^{device }$ is the overall aggregated bandwidth of the
network device on all its ports, and $LinkUtilization^{device} $ is the
$AggregateBandwidth^{device }$ 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
link utilization in order to charge for the effective energy cost
per trafic and not for the theoretical upper bound which is the
@ -608,13 +606,26 @@ In our case with small and sporadic network traffic, these results show that wit
\[P_{static}^{Cloudserver} = \frac{P_{static}^{server} \times PUE^{DataCenter}}{HostedVMs^{server}}\]
#+End_EXPORT
Where $ P_{static}^{server}$ is the static power consumption of the
Where $P_{static}^{server}$ is the static power consumption of the
server, $PUE^{DataCenter}$ is the data center PUE, and
$HostedVMs^{server}$ is the number of VMs a server can host. We do not
$HostedVMs^{server}$ is the number of VMs a server can host. This last
parameter should be adjusted in the case of VMs with multiple
virtual CPUs. We do not
consider here over-commitment of Cloud servers. Yet, the dynamic
energy part is computed with the real dynamic measurements, so it
accounts for VM over-provisionning and resource under-utilization.
In our case, the Cloud server has 14 cores, which corresponds to
the potential hosting of 14 small VMs with one virtual CPU each,
and each vCPU is pinned to a server core. We consider that for
fault-tolerance purpose, the IoT application has a replication
factor of 2, meaning that two cloud servers store its database.
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