From 2d9c7a6a784916ae699db55e5e01806181936607 Mon Sep 17 00:00:00 2001 From: ORGERIE Anne-Cecile Date: Fri, 19 Jul 2019 10:38:59 +0200 Subject: [PATCH] correction typos formules --- 2019-ICA3PP.org | 39 +++++++++++++++++++++++++-------------- 1 file changed, 25 insertions(+), 14 deletions(-) diff --git a/2019-ICA3PP.org b/2019-ICA3PP.org index 1089552..8d7f777 100644 --- a/2019-ICA3PP.org +++ b/2019-ICA3PP.org @@ -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