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diff --git a/2019-Mascots.org b/2019-Mascots.org
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it is possible to evaluate the end-to-end energy consumption of the system.
** IoT Part
- In the first place, the IoT part is composed of several sensors connected to an AP which forms a
- cell. It is model using the ns-3 network simulator. Thus, we setup between 5 and 15 sensors
- connected to the AP using WIFI 5GHz 802.11n. The node are placed randomly in a rectangle of 400m2
- around the AP which correspond to a typical real use case. All the nodes of the cell are setup
- with the default WIFI energy model provided by ns-3. The different energy values used by the
- energy model are provided on Table \ref{tab:wifi-energy}. These energy were extracted from
- previous work\cite{halperin_demystifying_nodate,li_end--end_2018} on 802.11n. Note that we
- suppose that the energy source of the cell nodes are unlimited and thus every nodes can
- communicate until the end of all the simulations.
+ In the first place, the IoT part is composed of several sensors connected to an Access Point (AP)
+ which forms a cell. This cell is model using the ns-3 network simulator. Consequently, we setup
+ between 5 and 15 sensors connected to the AP using WIFI 5GHz 802.11n. The node are placed
+ randomly in a rectangle of 400m2 around the AP which corresponds to a typical real use case. All
+ the cell nodes are setup with the default WIFI energy model provided by ns-3. The different
+ energy values used by the energy model are provided on Table \ref{tab:wifi-energy}. These energy
+ were extracted from previous work\cite{halperin_demystifying_nodate,li_end--end_2018} on
+ 802.11n. Besides, we suppose that the energy source of each nodes are unlimited and thus each of
+ them can communicate until the end of all the simulations.
- As a scenario, sensors send to the AP packets of 192 bits that include: \textbf{1)} A 128 bits
+ As a scenario, sensors send 192 bits packets to the AP composed of: \textbf{1)} A 128 bits
sensors id \textbf{2)} A 32 bits integer representing the temperature \textbf{3)} An integer
- timestamp representing the temperature sensing time. The data are transmitted immediately at each
- sensing interval $I$ varied from 1s to 60s. Finally, the AP is in charge of relaying data to the
- cloud using the network part.
+ timestamp representing the temperature sensing time to store them as time series. The data are
+ transmitted immediately at each sensing interval $I$ varied from 1s to 60s. Finally, the AP is in
+ charge of relaying data to the cloud via the network part.
#+BEGIN_EXPORT latex
\begin{table}[]
@@ -104,26 +104,26 @@ component, formatting, style, styling, insert
#+END_EXPORT
** Network Part
- The network part represents the network starting from the AP to the Cloud excluding the server.
- It is also model into ns-3. We consider the server to be 9 hops away from the AP with a typical
- round-trip latency of 100ms from the AP to the server. Each node from the AP to the Cloud is
- assume to be network switches with static and dynamic network energy consumption. ECOFEN
+ The network part represents the a network section starting from the AP to the Cloud excluding the
+ server. It is also model into ns-3. We consider the server to be 9 hops away from the AP with a
+ typical round-trip latency of 100ms from the AP to the server. Each node from the AP to the Cloud
+ is assume to be network switches with static and dynamic network energy consumption. ECOFEN
\cite{orgerie_ecofen:_2011} is used to model the energy consumption of the network part. ECOFEN
- is a ns-3 network energy module for ns-3 dedicated to wired network energy estimation. It is
- based on an energy-per-bit model including static consumption by assuming a linear relation
- between the amount of data sent to the network interface and the power consumption. The different
- energy 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 \cite{cornea_studying_2014-1}.
+ is a ns-3 network energy module dedicated to wired network. It is based on an energy-per-bit
+ model including static energy consumption by assuming a linear relation between the amount of
+ data sent to the network interface and its power consumption. The different energy 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 \cite{cornea_studying_2014-1}.
** Cloud Part
- Finally, to measure the energy consumption of the server, we used real server from the
- large-scale test-beds Grid5000 (G5K). In fact, G5K has a cluster called Nova composed of several
- nodes which are connected to watt-meters. In this way, we can benefit from real energy
- measurements. The server used in the experiment is composed of Intel Xeon E5-2620 processor with
- 64 GB of RAM and 600GB of disk space on a Linux based distribution. This server is configured to
- use KVM as virtualization mechanism. We deploy a classical Linux x86_64 distribution on the
- Virtual Machines (VM) along with a MySQL database. We different amount of allocated memory for
- the VM namely 1024MB/2048MB/4096MB to highlight its effects on the server energy consumption.
+ Finally, to measure the energy consumed by the server, we used real server from the large-scale
+ test-beds Grid5000 (G5K). In fact, G5K has a cluster called Nova composed of several nodes which
+ are connected to watt-meters. In this way, we can benefit from real energy measurements. The
+ server used in the experiment include an Intel Xeon E5-2620 processor with 64 GB of RAM and 600GB
+ of disk space on a Linux based operating system. This server is configured to use KVM as
+ virtualization mechanism. We deploy a classical Linux x86_64 distribution on the Virtual Machine
+ (VM) along with a MySQL database. We used different amount of allocated memory for the VM namely
+ 1024MB/2048MB/4096MB to highlight its effects on the server energy consumption.
The sensors requests are simulated using another server. This server is in charge to send hundred
of requests to the VM in order to fill the database. Consequently, it is easy to vary the
@@ -188,10 +188,10 @@ component, formatting, style, styling, insert
** Cloud Energy Consumption
In this End-To-End energy consumption study, cloud account for a huge part of the overall energy
consumption. According a report \cite{shehabi_united_2016-1} on United States data center energy
- usage, the average Power Usage Effectiveness (PUE) of an hyperscale data center is 1.2. Thus, in
+ usage, the average Power Usage Effectiveness (PUE) of an hyper-scale data center is 1.2. Thus, in
our analysis, all energy measurement on cloud server will account for this PUE.
- In a first place, we analyse the impact of the VM allocated memory on the server energy
+ In a first place, we analyze the impact of the VM allocated memory on the server energy
consumption. Figure \ref{fig:vmSize} depict the server energy consumption according to the VM
allocated memory for 20 sensors sending data every 10s. Note that red horizontal line represent
the average energy consumption for sample of energy value. We can see that at each sensing
@@ -216,7 +216,7 @@ component, formatting, style, styling, insert
number of sensors. These shows a clear linear relation between the number of sensors and the
server energy consumption. Moreover, we can see that the more sensors we have per server, the
more energy we can save. In fact, since the idle server energy consumption is high, it is more
- energy efficient to maximze the number of sensors per server. As showed on Figure
+ energy efficient to maximize the number of sensors per server. As showed on Figure
\ref{fig:sensorsNumber-WPS}, a significant amount of energy can be save when passing from 20
sensors to 300 per server.
@@ -260,11 +260,11 @@ component, formatting, style, styling, insert
energy consumption while varying the number of sensors. It is important to see that, for
small-scale systems, the server energy consumption is dominant face to the energy consumed by the
sensors. However, since we are using a single server, large-scale sensors deployment lead to an
- increasing consumtion of energy in the sensors side. On the other side, network energy
+ increasing consumption of energy in the sensors side. On the other side, network energy
consumption is stable regarding to the number of sensors that are deployed since network the
- system use case do not required large data transfert. Thus, it is important to remember that, to
+ system use case do not required large data transfer. Thus, it is important to remember that, to
save energy, we should maximize the number of sensors handle by each cloud server while keeping a
- resonable sensors requests intervals.
+ reasonable sensors requests intervals.
#+BEGIN_EXPORT latex
\begin{figure}
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