Merge work

Merge branch 'master' of gitlab.inria.fr:lguegan/paper-lowrate-iot

3 files changed, 43 insertions, 2 deletions

diff --git a/2019-ICA3PP.org b/2019-ICA3PP.org
index ecbd534..4bfdf5d 100644
--- a/2019-ICA3PP.org
+++ b/2019-ICA3PP.org
@@ -553,8 +553,39 @@ In our case with small and sporadic network traffic, these results show that wit
    
    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. Note that, for
-   small-scale systems, the server energy consumption is dominant compared to the energy consumed by the
+   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
+   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
+      belongs to the system's accounted consumption.
+   2. For the network part, the data packets generated by the IoT
+      device travel through network switches, routers and ports that
+      are shared with other trafic.
+   3. For the cloud part, the VM hosthing the data is shared with
+      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. 
+   
+   Concerning the sharing of the network costs, for each router, we
+   consider its aggregate bandwidth (on all the ports), its average
+   link utilization and the share taken by our IoT application. For a
+   given network device, we compute our share as follows:
+ 
+   #+BEGIN_EXPORT latex
+   \[P_{static}^{netdevice} = \frac{P_{static}^{device} \times Bandwidth^{application}}{AggregateBandwidth^{device}
+   \times LinkUtilization^{device}}\]
+   #+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
+   PUE.  
+
+   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
diff --git a/2019-ICA3PP.pdf b/2019-ICA3PP.pdf
index 09e2a60..4605cd2 100644
--- a/2019-ICA3PP.pdf
+++ b/2019-ICA3PP.pdf
diff --git a/references.bib b/references.bib
index 99fca13..4056bb9 100644
--- a/references.bib
+++ b/references.bib
@@ -2514,3 +2514,13 @@ volume={23},
 number={4}, 
 pages={1243-1256}, 
 }
+
+@ARTICLE{Sun2016, 
+author={X. {Sun} and N. {Ansari} and R. {Wang}}, 
+journal={IEEE Communications Surveys Tutorials}, 
+title={{Optimizing Resource Utilization of a Data Center}}, 
+year={2016}, 
+volume={18}, 
+number={4}, 
+pages={2822-2846}, 
+}