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2019-ICA3PP.bbl
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@ -37,14 +37,11 @@ Sandvine, ``{The Global Internet Phenomena Report},''
\url{https://www.sandvine.com/phenomena}, Oct. 2018.
\bibitem{li_end--end_2018}
\BIBentryALTinterwordspacing
Y.~Li, A.-C. Orgerie, I.~Rodero, B.~L. Amersho, M.~Parashar, and J.-M. Menaud,
``\BIBforeignlanguage{en}{End-to-end energy models for {Edge} {Cloud}-based
{IoT} platforms: {Application} to data stream analysis in {IoT}},''
\emph{\BIBforeignlanguage{en}{Future Generation Computer Systems}}, vol.~87,
pp. 667--678, Oct. 2018. [Online]. Available:
\url{https://linkinghub.elsevier.com/retrieve/pii/S0167739X17314309}
\BIBentrySTDinterwordspacing
pp. 667--678, Oct. 2018.
\bibitem{Wang2016}
K.~{Wang}, Y.~{Wang}, Y.~{Sun}, S.~{Guo}, and J.~{Wu}, ``{Green Industrial
@ -68,13 +65,10 @@ F.~Tao, Y.~Wang, Y.~Zuo, H.~Yang, and M.~Zhang, ``{Internet of Things in
Information Integration}, vol.~1, pp. 26 -- 39, 2016.
\bibitem{jalali_fog_2016}
\BIBentryALTinterwordspacing
F.~Jalali, K.~Hinton, R.~Ayre, T.~Alpcan, and R.~S. Tucker,
``\BIBforeignlanguage{en}{Fog {Computing} {May} {Help} to {Save} {Energy} in
{Cloud} {Computing}},'' \emph{\BIBforeignlanguage{en}{IEEE Journal on
Selected Areas in Communications}}, vol.~34, no.~5, pp. 1728--1739, May 2016.
[Online]. Available: \url{http://ieeexplore.ieee.org/document/7439752/}
\BIBentrySTDinterwordspacing
\bibitem{Sarkar2018}
S.~{Sarkar}, S.~{Chatterjee}, and S.~{Misra}, ``{Assessment of the Suitability
@ -83,8 +77,7 @@ S.~{Sarkar}, S.~{Chatterjee}, and S.~{Misra}, ``{Assessment of the Suitability
\bibitem{Samie2016}
F.~Samie, L.~Bauer, and J.~Henkel, ``Iot technologies for embedded computing: A
survey,'' in \emph{IEEE/ACM/IFIP International Conference on
Hardware/Software Codesign and System Synthesis (CODES)}, 2016.
survey,'' in \emph{IEEE/ACM/IFIP CODES}, 2016.
\bibitem{Gray2015}
C.~{Gray}, R.~{Ayre}, K.~{Hinton}, and R.~S. {Tucker}, ``{Power consumption of
@ -115,19 +108,13 @@ B.~{Martinez}, M.~{Montón}, I.~{Vilajosana}, and J.~D. {Prades}, ``{The Power
\bibitem{Ehsan}
E.~{Ahvar}, A.-C. {Orgerie}, and A.~{Lebre}, ``Estimating energy consumption of
cloud, fog and edge computing infrastructures,'' \emph{IEEE Transactions on
Sustainable Computing}, 2019.
cloud, fog and edge computing infrastructures,'' \emph{IEEE Trans. on Sust.
Comp.}, 2019.
\bibitem{mahadevan_power_2009}
\BIBentryALTinterwordspacing
P.~Mahadevan, P.~Sharma, S.~Banerjee, and P.~Ranganathan,
``\BIBforeignlanguage{en}{A {Power} {Benchmarking} {Framework} for {Network}
{Devices}},'' in \emph{\BIBforeignlanguage{en}{{NETWORKING} 2009}}, ser.
Lecture {Notes} in {Computer} {Science}.\hskip 1em plus 0.5em minus
0.4em\relax Springer, Berlin, Heidelberg, May 2009, pp. 795--808. [Online].
Available:
\url{https://link.springer.com/chapter/10.1007/978-3-642-01399-7_62}
\BIBentrySTDinterwordspacing
P.~Mahadevan, P.~Sharma, S.~Banerjee, and P.~Ranganathan, ``A {Power}
{Benchmarking} {Framework} for {Network} {Devices},'' in \emph{{NETWORKING}},
ser. Lecture {Notes} in {Computer} {Science}, 2009, pp. 795--808.
\bibitem{halperin_demystifying_nodate}
D.~Halperin, B.~Greenstein, A.~Sheth, and D.~Wetherall,
@ -144,15 +131,13 @@ A.~C. Orgerie, L.~Lefèvre, I.~Guérin-Lassous, and D.~M.~L. Pacheco,
\bibitem{sivaraman_profiling_2011}
V.~Sivaraman, A.~Vishwanath, Z.~Zhao, and C.~Russell, ``Profiling per-packet
and per-byte energy consumption in the {NetFPGA} {Gigabit} router,'' in
\emph{Computer {Communications} {Workshops} ({INFOCOM} {WKSHPS}), 2011 {IEEE}
{Conference} on}.\hskip 1em plus 0.5em minus 0.4em\relax IEEE, 2011, pp.
331--336.
\emph{IEEE INFOCOM Workshops}, 2011, pp. 331--336.
\bibitem{Serrano2015}
P.~{Serrano}, A.~{Garcia-Saavedra}, G.~{Bianchi}, A.~{Banchs}, and
A.~{Azcorra}, ``{Per-Frame Energy Consumption in 802.11 Devices and Its
Implication on Modeling and Design},'' \emph{IEEE/ACM Transactions on
Networking}, vol.~23, no.~4, pp. 1243--1256, 2015.
Implication on Modeling and Design},'' \emph{IEEE/ACM Trans. on Net.},
vol.~23, no.~4, pp. 1243--1256, 2015.
\bibitem{cornea_studying_2014-1}
B.~F. Cornea, A.~C. Orgerie, and L.~Lefèvre, ``Studying the energy consumption
@ -161,13 +146,10 @@ B.~F. Cornea, A.~C. Orgerie, and L.~Lefèvre, ``Studying the energy consumption
2014, pp. 143--148.
\bibitem{shehabi_united_2016-1}
\BIBentryALTinterwordspacing
A.~Shehabi, S.~Smith, D.~Sartor, R.~Brown, M.~Herrlin, J.~Koomey, E.~Masanet,
N.~Horner, I.~Azevedo, and W.~Lintner, ``\BIBforeignlanguage{en}{United
{States} {Data} {Center} {Energy} {Usage} {Report}},'' Tech. Rep.
LBNL--1005775, 1372902, Jun. 2016. [Online]. Available:
\url{http://www.osti.gov/servlets/purl/1372902/}
\BIBentrySTDinterwordspacing
LBNL--1005775, 1372902, Jun. 2016.
\bibitem{Hassidim2013}
A.~{Hassidim}, D.~{Raz}, M.~{Segalov}, and A.~{Shaqed}, ``{Network utilization:

92
2019-ICA3PP.org
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@ -315,7 +315,7 @@ and transmission technologies.
randomly in a rectangle of $400m^2$ around the AP which corresponds
to a typical use case for a home environment. All
the cell nodes employ the default WIFI energy model provided by ns3. The different
energy values used by the energy model are provided on Table \ref{tab:wifi-energy}. These parameters
energy values used by the energy model are provided in Table 1. These parameters
were extracted from previous work\cite{halperin_demystifying_nodate,li_end--end_2018} On
IEEE 802.11n. Besides, we suppose that the energy source of each
nodes is not limited during the experiments. Thus each node
@ -366,7 +366,7 @@ and transmission technologies.
model for the dynamic energy consumption
\cite{sivaraman_profiling_2011,Serrano2015}, and it includes also a static energy consumption.
The different 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
network part are shown in left part of Table 1 and come from previous work
\cite{cornea_studying_2014-1}.
** Cloud Part
@ -460,8 +460,8 @@ In our case with small and sporadic network traffic, these results show that wit
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[width=0.6\linewidth]{./plots/numberSensors-WIFINET.png}
\caption{Analysis of the variation of the number of sensors on the IoT/Network part energy consumption.}
\includegraphics[width=0.5\linewidth]{./plots/numberSensors-WIFINET.png}
\caption{Analysis of the variation of the number of sensors on the IoT/Network part energy consumption for a transmission interval of 10s.}
\label{fig:sensorsNumber}
\end{figure}
#+END_EXPORT
@ -476,6 +476,16 @@ In our case with small and sporadic network traffic, these results show that wit
the PUE to include the external energy costs like server cooling
and data center facilities \cite{Ehsan}.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[width=0.8\linewidth]{./plots/vmSize-cloud.png}
\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
Firstly, we analyze the impact of the VM allocated memory on the server energy
consumption. Figure \ref{fig:vmSize} depicts the server energy consumption according to the VM
allocated memory for 20 sensors sending data every 10s. Note that
@ -488,15 +498,6 @@ In our case with small and sporadic network traffic, these results show that wit
heavy memory accesses and processing time. Thus, remaining experiments are based on VM with 1024MB
of allocated memory.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[width=0.8\linewidth]{./plots/vmSize-cloud.png}
\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
Next, we study the effects of increasing the number of sensors on the server energy consumption.
Figure \ref{fig:sensorsNumber-server} presents the results of the average server energy
consumption when varying the number of sensors from 20 to 500, while Figure
@ -570,8 +571,8 @@ In our case with small and sporadic network traffic, these results show that wit
Concerning the IoT part, we include the entire IoT device power
consumption. Indeed, in our targeted low-bandwidth IoT application,
the sensor is dedicated to this application. From Table
\ref{tab:wifi-energy}, one can derive that the static power
the sensor is dedicated to this application. From Table 1, one can
derive that the static power
consumption of one IoT sensor is around 0.9 Watts. Its dynamic part
depends on the transmission frequency.
@ -605,7 +606,10 @@ In our case with small and sporadic network traffic, these results show that wit
port bandwidth multiplied by the link utilization and the overall
static power consumption of the port. Table \ref{tab:netbidules}
summarizes the parameters used in our model, they are taken from
\cite{mahadevan_power_2009}.
\cite{mahadevan_power_2009,Hassidim2013}. These are the parameters
used in our formula to compute the values that we used in the
simulations and that are presented in left part of Table 1.
#+BEGIN_EXPORT latex
@ -651,28 +655,49 @@ In our case with small and sporadic network traffic, these results show that wit
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
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
transfers. Thus, to save energy, we should maximize the number
of sensors handle by each cloud server while keeping reasonable sensors request intervals.
the number of sensors for a transmission interval of 10
seconds. The values are extracted from the experiments presented in
the previous section.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\hspace{1cm}
\includegraphics[scale=0.4]{plots/final.png}
\includegraphics[scale=0.35]{plots/final.png}
\label{fig:end-to-end}
\caption{End-to-end network energy consumption using sensors interval of 10s}
\end{figure}
#+END_EXPORT
Note that, for small-scale systems, with WiFi IoT devices, the IoT
sensor part is dominant in the overall energy consumption. Indeed,
the IoT application induces a very small cost on Cloud and network
infrastructures compared to the IoT device cost. But, our model
assumes that a single VM is handling multiple users (up to 300
sensors), thus being energy-efficient. Conclusions would be
different with one VM per user in the case of no over-commitment on
the Cloud side. For the network infrastructure, in our case of
low-bandwidth utilization (one data packet every 10 seconds), the
impact is almost negligible.
Another way of looking at these results is to observe that only for
a high number of sensors (> 300), the power consumption of Cloud and
network parts start to be negligible (few percent). It means that,
if IoT applications handle clients one by one (i.e. one VM per
client), the impact is high on cloud and network part if they have
only few sensors. The energy efficiency is really poor for only few
devices: with 20 IoT sensors, the overall energy cost to handle these
devices is 2.5 times the energy consumption of the IoT devices
themselves. Instead of increasing the number of sensors, which
would result in a higher overall energy consumption, one should
focus on reducing the energy consumption of IoT devices, especially
WiFi devices which are common due to WiFi availability
everywhere. One could also focus on improving the energy cost of
Cloud and network infrastructure for low-bandwidth applications and
few devices.
* Conclusion
#+LaTeX: \label{sec:cl}
@ -694,12 +719,17 @@ we propose an end-to-end energy consumption model.
This model provides insights on the hidden part of the iceberg: the
impact of IoT devices on the energy consumption of Cloud and network
infrastructures. On our use-case, we show that for a given sensor, its
larger energy consumption is on the Cloud part. Consequently, with the
larger energy consumption is on the sensor part. But the impact on the
Cloud and network part is huge when using only few sensors with
low-bandwidth applications.
Consequently, with the
IoT exploding growth, it becomes necessary to improve the energy
efficiency of applications hosted on Cloud infrastructures.
efficiency of applications hosted on Cloud infrastructures and of IoT devices.
Our future work includes studying other types of IoT wireless
transmission techniques and IoT applications in order to increase the
applicability of our model.
transmission techniques that would be more energy-efficient. We also
plan to study other
IoT applications in order to increase the applicability of our model
and provide advice for increasing the energy-efficiency of IoT infrastructures.

25
references.bib
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@ -1199,16 +1199,8 @@ IoT use cases. Index Terms—IoT, Centralized management, Orchestration, ILP, Fo
@inproceedings{mahadevan_power_2009,
series = {Lecture {Notes} in {Computer} {Science}},
title = {A {Power} {Benchmarking} {Framework} for {Network} {Devices}},
isbn = {978-3-642-01398-0 978-3-642-01399-7},
url = {https://link.springer.com/chapter/10.1007/978-3-642-01399-7_62},
doi = {10.1007/978-3-642-01399-7_62},
abstract = {Energy efficiency is becoming increasingly important in the operation of networking infrastructure, especially in enterprise and data center networks. Researchers have proposed several strategies for energy management of networking devices. However, we need a comprehensive characterization of power consumption by a variety of switches and routers to accurately quantify the savings from the various power savings schemes. In this paper, we first describe the hurdles in network power instrumentation and present a power measurement study of a variety of networking gear such as hubs, edge switches, core switches, routers and wireless access points in both stand-alone mode and a production data center. We build and describe a benchmarking suite that will allow users to measure and compare the power consumed for a large set of common configurations at any switch or router of their choice. We also propose a network energy proportionality index, which is an easily measurable metric, to compare power consumption behaviors of multiple devices.},
language = {en},
urldate = {2018-01-26},
booktitle = {{NETWORKING} 2009},
publisher = {Springer, Berlin, Heidelberg},
booktitle = {{NETWORKING}},
author = {Mahadevan, Priya and Sharma, Puneet and Banerjee, Sujata and Ranganathan, Parthasarathy},
month = may,
year = {2009},
pages = {795--808},
file = {Mahadevan et al. - 2009 - A Power Benchmarking Framework for Network Devices.pdf:/home/loic/.zotero/zotero/383myqxk.default/zotero/storage/7M3E6ARS/Mahadevan et al. - 2009 - A Power Benchmarking Framework for Network Devices.pdf:application/pdf;Snapshot:/home/loic/.zotero/zotero/383myqxk.default/zotero/storage/7AHE5AUI/978-3-642-01399-7_62.html:text/html}
@ -1217,7 +1209,6 @@ IoT use cases. Index Terms—IoT, Centralized management, Orchestration, ILP, Fo
@inproceedings{orgerie_ecofen:_2011,
title = {{ECOFEN}: {An} {End}-to-end energy {Cost} {mOdel} and simulator {For} {Evaluating} power consumption in large-scale {Networks}},
shorttitle = {{ECOFEN}},
doi = {10.1109/WoWMoM.2011.5986203},
abstract = {Wired networks are increasing in size and their power consumption is becoming a matter of concern. Evaluating the end-to-end electrical cost of new network architectures and protocols is difficult due to the lack of monitored realistic infrastructures. We propose an End-to-End energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks (ECOFEN) whose user's entries are the network topology and traffic. Based on configurable measurement of different network components (routers, switches, NICs, etc.), it provides the power consumption of the overall network including the end-hosts as well as the power consumption of each equipment over time.},
booktitle = {2011 {IEEE} {International} {Symposium} on a {World} of {Wireless}, {Mobile} and {Multimedia} {Networks}},
author = {Orgerie, A. C. and Lefèvre, L. and Guérin-Lassous, I. and Pacheco, D. M. Lopez},
@ -1643,8 +1634,7 @@ IoT use cases. Index Terms—IoT, Centralized management, Orchestration, ILP, Fo
@inproceedings{sivaraman_profiling_2011,
title = {Profiling per-packet and per-byte energy consumption in the {NetFPGA} {Gigabit} router},
booktitle = {Computer {Communications} {Workshops} ({INFOCOM} {WKSHPS}), 2011 {IEEE} {Conference} on},
publisher = {IEEE},
booktitle = {IEEE INFOCOM Workshops},
author = {Sivaraman, Vijay and Vishwanath, Arun and Zhao, Zhi and Russell, Craig},
year = {2011},
pages = {331--336},
@ -2292,8 +2282,6 @@ ALGOL 68 is substantially different from ALGOL 60 and was not well received, so
volume = {87},
issn = {0167739X},
shorttitle = {End-to-end energy models for {Edge} {Cloud}-based {IoT} platforms},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0167739X17314309},
doi = {10.1016/j.future.2017.12.048},
abstract = {Internet of Things (IoT) is bringing an increasing number of connected devices that have a direct impact on the growth of data and energy-hungry services. These services are relying on Cloud infrastructures for storage and computing capabilities, transforming their architecture into more a distributed one based on edge facilities provided by Internet Service Providers (ISP). Yet, between the IoT device, communication network and Cloud infrastructure, it is unclear which part is the largest in terms of energy consumption. In this paper, we provide end-to-end energy models for Edge Cloud-based IoT platforms. These models are applied to a concrete scenario: data stream analysis produced by cameras embedded on vehicles. The validation combines measurements on real test-beds running the targeted application and simulations on well-known simulators for studying the scaling-up with an increasing number of IoT devices. Our results show that, for our scenario, the edge Cloud part embedding the computing resources consumes 3 times more than the IoT part comprising the IoT devices and the wireless access point.},
language = {en},
urldate = {2019-05-20},
@ -2316,7 +2304,6 @@ ALGOL 68 is substantially different from ALGOL 60 and was not well received, so
@techreport{shehabi_united_2016-1,
title = {United {States} {Data} {Center} {Energy} {Usage} {Report}},
url = {http://www.osti.gov/servlets/purl/1372902/},
language = {en},
number = {LBNL--1005775, 1372902},
urldate = {2019-05-23},
@ -2407,8 +2394,6 @@ pages={2818-2823},
title = {Fog {Computing} {May} {Help} to {Save} {Energy} in {Cloud} {Computing}},
volume = {34},
issn = {0733-8716},
url = {http://ieeexplore.ieee.org/document/7439752/},
doi = {10.1109/JSAC.2016.2545559},
abstract = {Tiny computers located in end-user premises are becoming popular as local servers for Internet of Things (IoT) and Fog computing services. These highly distributed servers that can host and distribute content and applications in a peer-to-peer (P2P) fashion are known as nano data centers (nDCs). Despite the growing popularity of nano servers, their energy consumption is not well-investigated. To study energy consumption of nDCs, we propose and use flow-based and time-based energy consumption models for shared and unshared network equipment, respectively. To apply and validate these models, a set of measurements and experiments are performed to compare energy consumption of a service provided by nDCs and centralized data centers (DCs). A number of findings emerge from our study, including the factors in the system design that allow nDCs to consume less energy than its centralized counterpart. These include the type of access network attached to nano servers and nano server’s time utilization (the ratio of the idle time to active time). Additionally, the type of applications running on nDCs and factors such as number of downloads, number of updates, and amount of preloaded copies of data influence the energy cost. Our results reveal that number of hops between a user and content has little impact on the total energy consumption compared to the above-mentioned factors. We show that nano servers in Fog computing can complement centralized DCs to serve certain applications, mostly IoT applications for which the source of data is in end-user premises, and lead to energy saving if the applications (or a part of them) are off-loadable from centralized DCs and run on nDCs.},
language = {en},
number = {5},
@ -2435,7 +2420,7 @@ pages={2818-2823},
@inproceedings{Samie2016,
author = {Samie, Farzad and Bauer, Lars and Henkel, J\"{o}rg},
title = {IoT Technologies for Embedded Computing: A Survey},
booktitle = {IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES)},
booktitle = {IEEE/ACM/IFIP CODES},
year = {2016},
}
@ -2500,14 +2485,14 @@ pages={1429-1437},
@ARTICLE{Ehsan,
author={E. {Ahvar} and A.-C. {Orgerie} and A. {Lebre}},
journal={IEEE Transactions on Sustainable Computing},
journal={IEEE Trans. on Sust. Comp.},
title={Estimating Energy Consumption of Cloud, Fog and Edge Computing Infrastructures},
year={2019},
}
@ARTICLE{Serrano2015,
author={P. {Serrano} and A. {Garcia-Saavedra} and G. {Bianchi} and A. {Banchs} and A. {Azcorra}},
journal={IEEE/ACM Transactions on Networking},
journal={IEEE/ACM Trans. on Net.},
title={{Per-Frame Energy Consumption in 802.11 Devices and Its Implication on Modeling and Design}},
year={2015},
volume={23},