diff --git a/2019-ICA3PP.bbl b/2019-ICA3PP.bbl index 58562b8..17121e5 100644 --- a/2019-ICA3PP.bbl +++ b/2019-ICA3PP.bbl @@ -51,11 +51,6 @@ K.~{Wang}, Y.~{Wang}, Y.~{Sun}, S.~{Guo}, and J.~{Wu}, ``{Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective},'' \emph{IEEE Communications Magazine}, vol.~54, no.~12, pp. 48--54, 2016. -\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. - \bibitem{Ejaz2017} W.~Ejaz, M.~Naeem, A.~Shahid, A.~Anpalagan, and M.~Jo, ``Efficient energy management for the internet of things in smart cities,'' \emph{IEEE @@ -72,11 +67,40 @@ F.~Tao, Y.~Wang, Y.~Zuo, H.~Yang, and M.~Zhang, ``{Internet of Things in product life-cycle energy management},'' \emph{Journal of Industrial 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 + of Fog Computing in the Context of Internet of Things},'' \emph{IEEE + Transactions on Cloud Computing}, vol.~6, no.~1, pp. 46--59, 2018. + +\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. + \bibitem{Gray2015} C.~{Gray}, R.~{Ayre}, K.~{Hinton}, and R.~S. {Tucker}, ``{Power consumption of IoT access network technologies},'' in \emph{IEEE International Conference on Communication Workshop (ICCW)}, 2015, pp. 2818--2823. +\bibitem{Nest} +Google, ``{Nest Learning Thermostat -- Spec Sheet},'' + \url{https://nest.com/-downloads/press/documents/nest-thermostat-fact-sheet_2017.pdf}, + 2017. + +\bibitem{ns3-energywifi} +H.~Wu, S.~Nabar, and R.~Poovendran, ``{An Energy Framework for the Network + Simulator 3 (NS-3)},'' in \emph{International ICST Conference on Simulation + Tools and Techniques (SIMUTools)}, 2011, pp. 222--230. + \bibitem{Andres2017} P.~{Andres-Maldonado}, P.~{Ameigeiras}, J.~{Prados-Garzon}, J.~J. {Ramos-Munoz}, and J.~M. {Lopez-Soler}, ``{Optimized LTE data transmission @@ -89,23 +113,20 @@ B.~{Martinez}, M.~{Montón}, I.~{Vilajosana}, and J.~D. {Prades}, ``{The Power of Models: Modeling Power Consumption for IoT Devices},'' \emph{IEEE Sensors Journal}, vol.~15, no.~10, pp. 5777--5789, 2015. -\bibitem{ns3-energywifi} -H.~Wu, S.~Nabar, and R.~Poovendran, ``{An Energy Framework for the Network - Simulator 3 (NS-3)},'' in \emph{International ICST Conference on Simulation - Tools and Techniques (SIMUTools)}, 2011, pp. 222--230. +\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. -\bibitem{Sarkar2018} -S.~{Sarkar}, S.~{Chatterjee}, and S.~{Misra}, ``{Assessment of the Suitability - of Fog Computing in the Context of Internet of Things},'' \emph{IEEE - Transactions on Cloud Computing}, vol.~6, no.~1, pp. 46--59, 2018. - -\bibitem{jalali_fog_2016} +\bibitem{mahadevan_power_2009} \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/} +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 \bibitem{halperin_demystifying_nodate} @@ -120,6 +141,19 @@ A.~C. Orgerie, L.~Lefèvre, I.~Guérin-Lassous, and D.~M.~L. Pacheco, {IEEE} {International} {Symposium} on a {World} of {Wireless}, {Mobile} and {Multimedia} {Networks}}, Jun. 2011, pp. 1--6. +\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. + +\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. + \bibitem{cornea_studying_2014-1} B.~F. Cornea, A.~C. Orgerie, and L.~Lefèvre, ``Studying the energy consumption of data transfers in {Clouds}: the {Ecofen} approach,'' in \emph{2014 {IEEE} diff --git a/2019-ICA3PP.org b/2019-ICA3PP.org index 14d3981..bce8d9d 100644 --- a/2019-ICA3PP.org +++ b/2019-ICA3PP.org @@ -48,10 +48,10 @@ to absorb around 3% of the global energy consumption of 9% per year \cite{ShiftProject}. This alarming growth is explained by the fast emergence of numerous applications and new ICT devices. These devices supply services for smart building, smart -factories and smart cities for instance. Through connected devices, -with sensors producing data, actuators interacting with their -environment and communication means -- all being parts of the Internet of -Things (IoT) -- they provide optimized decisions. +factories and smart cities for instance. Through connected sensors +producing data, actuators interacting with their environment and +communication means -- all being parts of the Internet of Things (IoT) +-- they provide optimized decisions. This increase in number of devices implies an increase in the energy needed to manufacture and utilize them. Yet, the overall energy bill @@ -63,8 +63,8 @@ infrastructures to store, analyze and share their data. In February 2019, a report by Cisco stated that ``IoT connections will represent more than half (14.6 billion) of all global connected devices and connections (28.5 billion) by 2022" \cite{Cisco2019}. This -will represent more than 6% of global IP traffic, against 3% in -2017 \cite{Cisco2019}. This increasing impact of IoT devices on +will represent more than 6% of global IP traffic in 2022, against 3% +in 2017 \cite{Cisco2019}. This increasing impact of IoT devices on Internet connections induces a growing weight on ICT energy consumption. @@ -119,13 +119,14 @@ Our contributions include: application including the energy consumption of the WiFi IoT device and the consumption induced by its utilization on the Cloud and telecommunication infrastructures; -- an end-to-end energy model for low-bandwidth IoT applications. +- an end-to-end energy model for low-bandwidth IoT applications + relying on WiFi devices. The paper is organized as follows. Section \ref{sec:sota} presents the state of the art. The low-bandwidth IoT application is characterized in Section \ref{sec:usec}, and details on its architecture are provided in Section \ref{sec:model}. Section \ref{sec:eval} provides -our experimental results using real measurements and +our experimental results combining real measurements and simulations. Section \ref{sec:discuss} discusses the key findings an the end-to-end energy model. Finally, Section \ref{sec:cl} concludes this work and presents future work. @@ -137,75 +138,125 @@ this work and presents future work. ** Energy consumption of IoT devices The multiplication of smart devices and smart applications pushes the limits of Internet: IoT is now used everywhere for home automation, -smart agriculture, smart industry, e-health, smart cities, logistics, -smart grids, smart buildings, -etc. \cite{Wang2016,Ejaz2017,Minoli2017}. IoT devices are typically -used to optimize processes and the envisionned application domains -include the energy domain, like for instance the energy management of -product life-cycle \cite{Tao2016}. Yet, few studies adress the impact -of IoT itself on global energy consumption -\cite{jalali_fog_2016,li_end--end_2018} or CO2 emissions -\cite{Sarkar2018}. +smart agriculture, e-health, smart cities, logistics, smart grids, +smart buildings, etc. \cite{Wang2016,Ejaz2017,Minoli2017}. IoT devices +are typically used to optimize processes and the envisioned +application domains include the energy distribution and management. It +can for instance help the energy management of product life-cycle +\cite{Tao2016}. Yet, few studies address the impact of IoT itself on +global energy consumption \cite{jalali_fog_2016,li_end--end_2018} or +CO2 emissions \cite{Sarkar2018}. The underlying architecture of these smart applications usually includes sensing devices, cloud servers, user applications and telecommunication networks. Concerning the computing part, the cloud servers can either be located on Cloud data centers, on Fog -infrastructures located at the network edge or on home gateways +infrastructures located at the network edge, or on home gateways \cite{Wang2016}. Various network technologies are employed by IoT -devices to communicate with their nearby gateway; either wired like -Ethernet or wireless: WiFi, Bluetooth, Near Field Communication (NFC), -ZigBee, celular network (like 3G, LTE, 4G), Low Power Wide Area -Network (LPWAN), etc. \cite{Samie2016,Gray2015}. The chosen technology -depends on the smart device characteristics and the targeted -communication performance. The Google Nest Thermostat can for instance -use WiFi, 802.15.4 and bluetooth \cite{Nest}. In this paper, we focus -on WiFi as it is broadly available and employed by IoT devices -\cite{Samie2016,ns3-energywifi}. +devices to communicate with their nearby gateway; either wired +networks with Ethernet or wireless networks: WiFi, Bluetooth, Near +Field Communication (NFC), ZigBee, cellular network (like 3G, LTE, 4G), +Low Power Wide Area Network (LPWAN), +etc. \cite{Samie2016,Gray2015}. The chosen technology depends on the +smart device characteristics and the targeted communication +performance. The Google Nest Thermostat can for instance use WiFi, +802.15.4 and Bluetooth \cite{Nest}. In this paper, we focus on WiFi as +it is broadly available and employed by IoT devices +\cite{Samie2016,ns3-energywifi}. +Several works aim at reducing the energy consumption of the device +transmission \cite{Andres2017} or improving the energy efficiency of +the access network technologies \cite{Gray2015}. An extensive +literature exists on increasing the lifetime of battery-based wireless +sensor networks \cite{Wang2016}. Yet, IoT devices present more +diversity than typical wireless sensors in terms of hardware +characteristics, communication means and data production patterns. -Smart industry \cite{Wang2016} : Archi with sensing devices, cloud -server, user applications and networks +Based on real measurements, previous studies have proposed energy +models for IoT devices. Yet, these models are specific to a given kind +of IoT device or a given transmission technology. +Martinez et al. provide energy consumption measurements for wireless +sensor networks using SIGFOX transmissions and employed for +smart-parking systems \cite{Martinez2015}. Wu et al. implement an +energy model for WiFi devices in the well-known ns3 network simulator +\cite{ns3-energywifi}. -IoT archi : devices, gateways, fog and clouds \cite{Samie2016} - -Smart cities \cite{Ejaz2017} - -Smart building \cite{Minoli2017} - -home automation, smart agriculture, eHealth, logistics, smart grids - -product life-cycle energy management \cite{Tao2016} - - -focusing on access network technologies \cite{Gray2015}, - -improving device transmission \cite{Andres2017} - -modeling the energy consumption of WSN devices \cite{Martinez2015} or -the WiFi transmission \cite{ns3-energywifi} - -on organizing wireless sensor communications to increase the network -lifetime \cite{Wang2016} - -CO2 impact of IoT and fog computing architectures vs Cloud -\cite{Sarkar2018} - - -Fog archi to use more renewable energy \cite{li_end--end_2018} Or -reduce communication costs \cite{jalali_fog_2016} ** Energy consumption of network and cloud infrastructures -net models -server models + VM sharing +IoT architecture rely on telecommunication networks and Cloud +infrastructures to provide services. The data produced by IoT devices +are stored and exploited by servers located either in Cloud data +centers or Fog edge sites. While studies exist on the energy +consumption of network and cloud infrastructures in general +\cite{Ehsan}, they do not consider the specific case of IoT devices. +To the best of our knowledge, no study estimates the direct impact of +IoT applications on the energy consumption of these infrastructures. + +Most work focusing on energy consumption, Cloud architecture and IoT +applications tries to answer the question: where to locate data +processing in order to save energy +\cite{jalali_fog_2016}, to reduce the CO2 impact \cite{Sarkar2018}, or +to optimize renewable energy consumption \cite{li_end--end_2018}. + +In both cases, the network and cloud infrastructures, attributing the +energy consumption to a given user or application is a challenging +task. The complexity comes from the shared nature of these +infrastructures: a given Ethernet port in the core of the network +processes many packets coming from a high number of sources +\cite{jalali_fog_2016}. Moreover, the employed equipment is not power +proportional: servers and routers consume consequent amounts of +energy while being idle +\cite{mahadevan_power_2009,li_end--end_2018}. +The power consumed by a device is divided into two parts: a dynamic +part that varies with traffic or amount of computation to process, and +a static part that is constant and dissipated even while being idle +\cite{Ehsan}. This static part implies that a consequent energy cost +of running an application on a server is due to the device being +simply powered on. Consequently, sharing these static energy costs +among all the concerned users requires an end-to-end model +\cite{li_end--end_2018}. + +In this paper, we focus on IoT devices using WiFi transmission and +generating low data volumes. Our model, extracted from real +measurements and simulations, can be adapted to other kinds of devices +and transmission technologies. + * Characterization of low-bandwidth IoT applications #+LaTeX: \label{sec:usec} +In this section, we detail the characteristics of the considered IoT +application. While the derived model is more generic, we focus on a +given application to obtain a precise use-case with accurate power +consumption measurements. +The Google Nest Thermostat relies on five sensors: temperature, +humidity, near-field activity, far-field activity and ambient light +\cite{Nest}. Periodical measurements, sent through wireless +communications on the Internet, are stored on Google data centers and +processed to learn the home inhabitants habits. The learned behavior +is employed to automatically adjust the home temperature managed by +heating and cooling systems. -** Application Characteristic + #+BEGIN_EXPORT latex + \begin{figure} + \centering + \includegraphics[width=0.6\linewidth]{./plots/home.png} + \caption{Overview of IoT devices.} + \label{fig:IoTdev} + \end{figure} + #+END_EXPORT + +Each IoT device senses periodically its environment. Then, it sends +the produced data through WiFi (in our context) to its gateway or +Access Point (AP). The AP is in charge of transmitting the data to the +cloud using the Internet. Figure \ref{fig:IoTdev} illustrates this +architecture. Several IoT devices can share the same AP in a +home. We consider low-bandwidth applications where devices produces +several network packets during each sensing period. The transmitting +frequency can vary from one to several packet sent per minute +\cite{Cisco2019}. #+BEGIN_COMMENT The IoT part is composed of an Access Point (AP), connected to several sensors using WIFI. In the @@ -219,20 +270,18 @@ server models + VM sharing of several network switches and router and it is considered to be a wired network. #+END_COMMENT - - #+BEGIN_EXPORT latex - \begin{figure} - \centering - \includegraphics[width=0.6\linewidth]{./plots/home.png} - \caption{Overview of IoT devices.} - \label{fig:IoTdev} - \end{figure} - #+END_EXPORT - - -** Cloud Infrastructure +We consider that the link between the AP and the Cloud is composed of +several network switches and routers using Ethernet as shown in Figure +\ref{fig:parts}. The number of routers on the path depends on the +location of the server, either in a Cloud data center or in a Fog site +at the edge of the network. +We assume that the server hosting the application data for the users +belongs to a shared cloud facility with classical service level +agreement (SLA). The facility provides redundant storage and computing +means as virtual machines (VMs). A server can host several VMs at the +same time. #+BEGIN_EXPORT latex \begin{figure} @@ -243,28 +292,40 @@ server models + VM sharing \end{figure} #+END_EXPORT +In the following, we describe the experimental setup, the results and +the end-to-end model. For all these steps, we decompose the overall +IoT architecture into three parts: the IoT device part, the networking +part and the cloud part, as displayed on Figure \ref{fig:parts}. + + * Experimental setup \hl{Ajouter \% de bande passante utilisé par les applis low-rate} -#+LaTeX: \label{sec:model} - Our system model is divided in three parts. First, the IoT and the network parts are modeled through - simulations. Then, the Cloud part is modeled using real servers connected to wattmeters. In this way, - it is possible to evaluate the end-to-end energy consumption of the system. +#+Latex: \label{sec:model} + In this section, we describe the experimental setup employed to + acquire energy measurements for each of the three parts of our + system model. The IoT and the network parts are modeled + through simulations. The Cloud part is modeled using real + servers connected to wattmeters. In this way, 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 Access Point (AP) - which form a cell. This cell is evaluated using the ns-3 network simulator. Consequently, we setup + which form a cell. This cell is studied using the ns3 network + simulator. In the experimental scenario, 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 $400m^2$ 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. + 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 + 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 + can communicate until the end of all the simulations. 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 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 + timestamp representing the temperature sensing date. They are stored as time series. The data are + transmitted immediately at each sensing interval $I$ that we vary from 1s to 60s. Finally, the AP is in charge of relaying data to the cloud via the network part. #+BEGIN_EXPORT latex @@ -295,96 +356,109 @@ server models + VM sharing ** Network Part 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. The first 8 - hop are edge switches and the last one is consider to be a core switch as mention in + server. It is also modeled into ns3. 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 + \cite{li_end--end_2018}. Each node from the AP to the Cloud + is a network switch with static and dynamic network energy consumption. The first 8 + hops are edge switches and the last one is consider to be a core router as mentioned in \cite{jalali_fog_2016}. ECOFEN \cite{orgerie_ecofen:_2011} is used to model the energy - consumption of the network part. ECOFEN 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 + consumption of the network part. ECOFEN is an ns3 network energy module dedicated to wired + networks. It is based on an energy-per-bit and energy-per-packet + 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 \cite{cornea_studying_2014-1}. ** Cloud Part - 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 + Finally, to measure the energy consumed by the Cloud part, we use a real server from the large-scale + test-bed Grid'5000. Grid'5000 provides clusters composed of several nodes which + are connected to wattmeters. The wattmeters provide 50 + instantaneous power measurements per second and per server. This + way, we can benefit from real energy measurements. The server used + in the experiment embeds two Intel Xeon E5-2620 v4 processors 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 - different requests characteristics namely: \textbf{1)} The number request, to virtually - add/remove sensors \textbf{2)} The requests interval. Figure \ref{fig:g5kExp} present this simulation - setup. + virtualization mechanism. We deploy a classical Debian x86_64 distribution on the Virtual Machine + (VM) along with a MySQL database. We use different amounts of allocated memory for the VM namely + 1024MB/2048MB/4096MB to highlight its effects on the server energy + consumption. The server only hosts this VM in order to easily isolate its + power consumption. #+BEGIN_EXPORT latex \begin{figure} \centering - \includegraphics[width=0.4\linewidth]{./plots/g5k-xp.png} - \caption{Grid5000 experimentation setup.} + \includegraphics[width=0.5\linewidth]{./plots/g5k-xp.png} + \caption{Grid'5000 experimental setup.} \label{fig:g5kExp} \end{figure} #+END_EXPORT + The data sent by the IoT devices are simulated using another + server from the same cluster. This server is in charge of sending + the data packets to the VM hosting the application in order to fill + its database. In the following, each data packet coming from an IoT + device and addressed to the application VM is called a request. Consequently, it is easy to vary the + different application characteristics namely: \textbf{1)} The number + of requests, to virtually + add/remove sensors \textbf{2)} The requests interval, to study the + impact of the transmitting frequency. Figure \ref{fig:g5kExp} presents this simulation + setup. * Evaluation #+LaTeX: \label{sec:eval} -** IoT/Network Consumption - In a first place, we start by studying the impact of the sensors position on their energy - consumption. To this end, we run several simulations in ns-3 with different sensors position. The - results provided by Table \ref{tab:sensorsSendIntervalEffects} show that sensors position have a very low impact +In this section, we analyze the experimental results. + +** IoT and Network Power Consumption + In a first place, we start by studying the impact of the sensors' + transmission frequency on their energy + consumption. To this end, we run several simulations in ns3 with different frequencies. The + results provided by Table \ref{tab:sensorsSendIntervalEffects} show + that the transmission frequency has a very low impact on the energy consumption and on the application delay. It has an impact of course, but it is very limited. This due to the fact that in such a scenario with very small number of communications spread over the time, sensors don't have to contend for accessing to the Wifi channel. - + +\hl{TODO: définir le 'application delay' et le nombre de capteurs utilisés pour l'expérience de la table} #+BEGIN_EXPORT latex % Please add the following required packages to your document preamble: % \usepackage{booktabs} \begin{table*}[] \centering - \caption{Sensors send interval effects} + \caption{Sensors transmission interval effects} \label{tab:sensorsSendIntervalEffects} \begin{tabular}{@{}lrrrrr@{}} \toprule Sensors Send Interval & 10s & 30s & 50s & 70s & 90s \\ \midrule Sensors Power Consumption & 13.517\hl{94}W & 13.517\hl{67}W & 13.51767W & 13.51767W & 13.517\hl{61}W \\ Network Power Consumption & 10.441\hl{78}W & 10.441\hl{67}W & 10.44161W & 10.44161W & 10.441\hl{61}W \\ - Average Appplication Delay & 17.81360s & 5.91265s & 3.53509s & 2.55086s & 1.93848s \\ \bottomrule + Average Application Delay & 17.81360s & 5.91265s & 3.53509s & 2.55086s & 1.93848s \\ \bottomrule \end{tabular} \end{table*} #+END_EXPORT + - - Previous work \cite{li_end--end_2018} on similar scenario shows that increasing application - accuracy impact strongly the energy consumption in the context of data stream analysis. However, - in our case, application accuracy is driven by the sensing interval and thus, the transmit - frequency of the sensors. Therefore, we varied the transmission interval of the sensors from 1s - to 60s. Some of these results are proposed on Table \ref{tab:sensorsSendIntervalEffects}. In - case of small and sporadic network traffic, these results show that with a reasonable - transmission interval the energy consumption of the IoT/Network if almost not affected by the + Previous work \cite{li_end--end_2018} on a similar scenario shows that increasing application + accuracy impacts strongly the energy consumption in the context of data stream analysis. However, + in our case, application accuracy is driven by the sensing interval and thus, the transmission + frequency of the sensors. +In our case with small and sporadic network traffic, these results show that with a reasonable + transmission interval, the energy consumption of the IoT and the + network parts are almost not affected by the variation of this transmission interval. In fact, transmitted data are not large enough to leverage the energy consumed by the network. - - - The number of sensors is a dominant factor that leverage the energy consumption of the - IoT/Network part. Therefore, we varied the number of sensors in the Wifi cell to analyze its - impact. The Figure \ref{fig:sensorsNumber} represents the energy consumed by each simulated part - according the the number of sensors. It is clear that the energy consumed by the network is the - dominant part. However, since the number of sensors is increasing the energy consumed by the - network will become negligible face to the energy consume by the sensors. In fact, deploying new + We then vary the number of sensors in the Wifi cell. + The Figure \ref{fig:sensorsNumber} represents the energy consumed by each simulated part + according to the number of sensors. It is clear that the energy consumed by the network is the + dominant part. However, if the number of sensors is increasing, the energy consumed by the + network can become smaller than the sensors part. In fact, deploying new sensors in the cell do not introduce much network load. To this end, sensors energy consumption - is dominant. + can become dominant. #+BEGIN_EXPORT latex \begin{figure} @@ -397,39 +471,51 @@ server models + VM sharing ** Cloud Energy Consumption - In this End-To-End energy consumption study, cloud account for a huge part of the overall energy + 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 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. + 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 + the PUE to include the external energy costs like server cooling + and data center facilities \cite{Ehsan}. - 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 horizontal red line represent + 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 + the horizontal red line represents the average energy consumption for the considered sample of energy values. We can see that at - each sensing interval, server face to peaks of energy consumption. However, VM allocated memory - do not influence energy consumption. In fact, simple database requests do not need any particular - huge memory access and processing time. Thus, remaining experiments are based on VM with 1024MB + each transmission interval, the server faces spikes of energy + consumption. However, the amount of allocated memory to the VM + does not significantly influence the server energy consumption. In + fact, simple database requests do not need any particular + 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{VM size impact on the server energy consumption using 20 sensors sending data every 10s} + \caption{Server power consumption 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} present the results of the average server energy - consumption when varying the number of sensors from 20 to 500 while Figure - \ref{fig:sensorsNumber-WPS} present the average server energy cost per sensors according to the + 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 + \ref{fig:sensorsNumber-WPS} presents the average server energy cost per sensor according to the number of sensors. These results show 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 + the server energy consumption. Moreover, we can see that the more sensors we have per VM, the + more energy we can save. In fact, since the server's idle power + 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 server. + 300 sensors per VM. Note that these measurements are the row + measurements taken from the wattmeters: they do not include the PUE + and are not shared among all the VMs that could be hosted on this + server. + + \hl{Figure 5 n'inclut pas le PUE non? le Pidle est bien à 97 Watts environ?} #+BEGIN_EXPORT latex \begin{figure} @@ -448,32 +534,35 @@ server models + VM sharing \end{figure} #+END_EXPORT - A last parameter can leverage server energy consumption namely sensors send interval. In addition - to increasing the application accuracy, sensors send interval increase network traffic and - database accesses. Figure \ref{fig:sensorsFrequency} present the impact on the server energy - consumption of changing the send interval of 50 sensors to 1s, 10s and 30s. We can see that, the - lower sensors send interval is, the more server energy consumption peaks occurs. Therefore, it - leads to an increase of the server energy consumption. + A last parameter can leverage server energy consumption, namely + sensors transmission interval. In addition + to increasing the application accuracy, sensors transmission frequency increases network traffic and + database accesses. Figure \ref{fig:sensorsFrequency} presents the impact on the server energy + consumption when changing the transmission interval of 50 sensors + to 1s, 10s and 30s. We can see that, the lower sensors transmission + interval is, the more server energy consumption peaks + occur. Therefore, it leads to an increase of the server energy consumption. #+BEGIN_EXPORT latex \begin{figure} \centering \includegraphics[scale=0.5]{plots/sendInterval-cloud.png} + \caption{Server energy consumption for 50 sensors sending requests at different transmission interval.} \label{fig:sensorsFrequency} - \caption{Server energy consumption for 50 sensors sending request at different interval.} \end{figure} #+END_EXPORT -** End-To-End Consumption +* End-To-End Consumption Model +#+LaTeX: \label{sec:discuss} - To have an overview of the energy consume by the system, it is important to consider the + 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. It is important to see that, for - small-scale systems, the server energy consumption is dominant face to the energy consumed by the + 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 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 to the number of sensors since the system use case do not required large data - transfer. Thus, it is important to remember that, to save energy, we should maximize the number + 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. #+BEGIN_EXPORT latex @@ -488,12 +577,35 @@ server models + VM sharing -* Discussion -#+LaTeX: \label{sec:discuss} - * Conclusion #+LaTeX: \label{sec:cl} +Information and Communication Technology takes a growing part in the +worldwide energy consumption. One of the root causes of this increase +lies in the multiplication of connected devices. Each object of the +Internet-of-Things often does not consume much energy by itself. Yet, +their number and the infrastructures they require to properly work +have leverage. + +In this paper, we combine simulations and real +measurements to study the energy impact of IoT devices. In particular, +we analyze the energy consumption of Cloud and telecommunication +infrastructures induced by the utilization of connected devices. +Through the fine-grain analysis of a given low-bandwidth IoT device +periodically sending data to a Cloud server using WiFi, +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 +IoT exploding growth, it becomes necessary to improve the energy +efficiency of applications hosted on Cloud infrastructures. +Our future work includes studying other types of IoT wireless +transmission techniques and IoT applications in order to increase the +applicability of our model. + + + \bibliographystyle{IEEEtran} \bibliography{references} diff --git a/2019-ICA3PP.pdf b/2019-ICA3PP.pdf index b95e4f3..9c7edb3 100644 Binary files a/2019-ICA3PP.pdf and b/2019-ICA3PP.pdf differ diff --git a/references.bib b/references.bib index cd3c9c7..99fca13 100644 --- a/references.bib +++ b/references.bib @@ -2489,3 +2489,28 @@ year = {2017}, howpublished = {\url{https://nest.com/-downloads/press/documents/nest-thermostat-fact-sheet_2017.pdf}}, author = {Google} } + +@INPROCEEDINGS{Hassidim2013, +author={A. {Hassidim} and D. {Raz} and M. {Segalov} and A. {Shaqed}}, +booktitle={IEEE INFOCOM}, +title={{Network utilization: The flow view}}, +year={2013}, +pages={1429-1437}, +} + +@ARTICLE{Ehsan, +author={E. {Ahvar} and A.-C. {Orgerie} and A. {Lebre}}, +journal={IEEE Transactions on Sustainable Computing}, +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}, +title={{Per-Frame Energy Consumption in 802.11 Devices and Its Implication on Modeling and Design}}, +year={2015}, +volume={23}, +number={4}, +pages={1243-1256}, +}