continuation des formules

2019-ICA3PP.bbl

@@ -169,4 +169,13 @@ A.~Shehabi, S.~Smith, D.~Sartor, R.~Brown, M.~Herrlin, J.~Koomey, E.~Masanet,
   \url{http://www.osti.gov/servlets/purl/1372902/}
 \BIBentrySTDinterwordspacing
 
+\bibitem{Hassidim2013}
+A.~{Hassidim}, D.~{Raz}, M.~{Segalov}, and A.~{Shaqed}, ``{Network utilization:
+  The flow view},'' in \emph{IEEE INFOCOM}, 2013, pp. 1429--1437.
+
+\bibitem{Zhang2016}
+Z.~{Zhang}, Y.~{Bejerano}, and S.~{Antonakopoulos}, ``{Energy-Efficient IP Core
+  Network Configuration Under General Traffic Demands},'' \emph{IEEE/ACM
+  Transactions on Networking}, vol.~24, no.~2, pp. 745--758, 2016.
+
 \end{thebibliography}

2019-ICA3PP.org

@@ -472,7 +472,7 @@ In our case with small and sporadic network traffic, these results show that wit
 
 ** Cloud Energy Consumption
    In this end-to-end energy consumption study, cloud accounts for a huge part of the overall energy
-   consumption. According a report \cite{shehabi_united_2016-1} on United States data center 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 hyper-scale data center is 1.2. Thus, in
    our analysis, all energy measurement on cloud server will account
    for this PUE. It means that the power consumption of the server is multiplied by
@@ -510,12 +510,12 @@ In our case with small and sporadic network traffic, these results show that wit
    consumption is high (around 97 Watts), it is more
    energy efficient to maximize the number of sensors per server. As shown on Figure
    \ref{fig:sensorsNumber-WPS}, a significant amount of energy can be save when passing from 20 to
-   300 sensors per VM. Note that these measurements are the row
+   300 sensors per VM. Note that these measurements are not the row
-   measurements taken from the wattmeters: they do not include the PUE
+   measurements taken from the wattmeters: they include the PUE
-   and are not shared among all the VMs that could be hosted on this
+   but they are not shared among all the VMs that could be hosted on this
-   server.
+   server. So, for the studied server, its static power consumption
-
+   (also called idle consumption) is around 83.2 Watts and we consider
-   \hl{Figure 5 n'inclut pas le PUE non? le Pidle est bien à 97 Watts environ?}
+   a PUE of 1.2, this value is taken from \cite{shehabi_united_2016-1}}.
 
    #+BEGIN_EXPORT latex
    \begin{figure}
@@ -576,17 +576,46 @@ In our case with small and sporadic network traffic, these results show that wit
    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:
+   given network device, we compute our share of the static energy
+   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
+   #+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
+   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
+   link bandwidth. Indeed, using such an upper bound leads to greatly
+   underestimate our energy part, since link utilization typically
+   varies between 5 to 40% \cite{Hassidim2013,Zhang2016}.
+
+   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.
 
    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.  
+   PUE. For a given Cloud server hosting our IoT application, we
+   compute our share of the static energy part as follows: 
+
+   #+BEGIN_EXPORT latex
+   \[P_{static}^{Cloudserver} = \frac{P_{static}^{server} \times PUE^{DataCenter}}{HostedVMs^{server}}\]
+   #+End_EXPORT
+   
+   Where $ P_{static}^{server}$ is the static power consumption of the
+   server, $PUE^{DataCenter}$ is the data center PUE, and
+   $HostedVMs^{server}$ is the number of VMs a server can host. 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.
 
    Note that, for small-scale systems, the server energy consumption
    is dominant compared to the energy consumed by the 

references.bib

@@ -2524,3 +2524,13 @@ volume={18},
 number={4}, 
 pages={2822-2846}, 
 }
+
+@ARTICLE{Zhang2016, 
+author={Z. {Zhang} and Y. {Bejerano} and S. {Antonakopoulos}}, 
+journal={IEEE/ACM Transactions on Networking}, 
+title={{Energy-Efficient IP Core Network Configuration Under General Traffic Demands}}, 
+year={2016}, 
+volume={24}, 
+number={2}, 
+pages={745-758}, 
+}