potential energy saving in wireline telecoms

IEEE Communications Magazine • August 201180 0163-6804/11/$25.00 © 2011 IEEE

INTRODUCTION

Only recently, telecom operators (telcos) andInternet service providers (ISPs) have raised theirinterest in energy efficiency for wired networksand service infrastructures, making it a high-priori-ty objective. This interest is motivated by theincrease in energy prices, the continuing growth ofthe customer population, the spreading of broad-band access, and the expanding services offered.Indeed, the number of new services being offeredand the increase in the volume of data traffic fol-low Moore’s law, doubling every 18 months.

To support new-generation network infras-tructures and related services for a rapidly grow-ing customer population, telcos and ISPs needan ever larger number of devices, with sophisti-

cated architectures able to perform increasinglycomplex operations in a scalable way. Forinstance, high-end routers are increasingly basedon complex multirack architectures, which pro-vide more and more network functionalities; his-toric data from manufacturers’ datasheets showcontinuously raising capacities, by a factor of 2.5every 18 months [1]. However, silicon technolo-gies improve their energy efficiency at a slowerpace following Dennard’s law (i.e., by a factor of1.65 every 18 months) with respect to routers’capacities and traffic volumes.

In the last few years, telcos, ISPs, and publicorganizations around the world reported statis-tics of network energy requirements and therelated carbon footprint, showing an alarmingand growing trend. The Global e-SustainabilityInitiative (GeSI) [2] estimated an overall net-work energy requirement of about 21.4 TWh in2010 for European telcos, and foresees a figureof 35.8 TWh in 2020 if no green network tech-nologies (GNTs) are adopted. The sole introduc-tion of novel low-consumption silicontechnologies clearly cannot cope with such trendsand be sufficient for drawing current networkequipment toward a greener future Internet.

Moreover, it is well known that network linksand devices are provisioned for busy or rush hourload, which typically exceeds their average utiliza-tion by a wide margin. While this margin is sel-dom reached, nevertheless the overall powerconsumption in today’s networks is determined byit and remains more or less constant even in thepresence of fluctuating traffic loads. This situationsuggests the possibility of adapting network ener-gy requirements to actual traffic profiles.

Motivated by these considerations, we recent-ly established a partnership with a group of pri-mary device manufacturers and telcos to launchthe ECONET initiative [3], an integrated project

ABSTRACT

Recently, network operators around the worldreported statistics of network energy requirementsand the related carbon footprint, showing analarming and growing trend. Such high energyconsumption can be mainly ascribed to networkingequipment designed to work at maximum capacitywith high and almost constant dissipation, inde-pendent of the traffic load. However, recent devel-opments of green network technologies suggestthe chance to build future devices capable ofadapting their performance and energy absorptionto meet actual workload and operational require-ments. In such a scenario, this contribution aimsat evaluating the potential impact on next-genera-tion wireline networks of green technologies ineconomic and environmental terms. We based ourimpact analysis on the real network energy-effi-ciency targets of an ongoing European project,and applied them to the expected deployment ofTelecom Italia infrastructure by 2015–2020.

ENERGY EFFICIENCY IN COMMUNICATIONS

Raffaele Bolla and Franco Davoli, DIST-University of Genoa

Roberto Bruschi, National Inter-University Consortium for Telecommunications

Ken Christensen, University of South Florida

Flavio Cucchietti, Telecom Italia

Suresh Singh, Portland State University

The Potential Impact of GreenTechnologies in Next-GenerationWireline Networks: Is There Room forEnergy Saving Optimization?

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IEEE Communications Magazine • August 2011 81

(IP) funded by the European Commission (EC).The main aim of ECONET is to design anddevelop innovative solutions and device proto-types for wired network infrastructures (fromcustomer premises equipment to backboneswitches and routers) by 2013. The resulting net-work platforms will adopt GNTs for aggressivelymodulating power consumption according toactual workloads and service requirements.

In this scenario, the goal of the present arti-cle is to evaluate the potential gain to be derivedfrom the application of GNTs in quantitativeterms. To this aim, we want to assess how tech-nological solutions able to trade device perfor-mance for power consumption can be effectivelyused in the short term for reducing the carbonfootprint and operating expenditures (OPEX) ofnext-generation wireline networks. In order tomake our impact analysis as realistic as possible,we consider the energy efficiency targets of theECONET project, and apply them to a perspec-tive network of a large-scale telco, which corre-sponds to an expected deployment of TelecomItalia infrastructure by 2015–2020.

The article is organized as follows. We intro-duce a reference scenario based on prospectiveTelecom Italia network development. We brieflydescribe some of the most promising GNTs we

are proposing, along with other contributions bythe research community, for disruptively reduc-ing the network carbon footprint. We report ananalysis estimating the impact of GNTs in botheconomical and environmental terms. Conclu-sions are then drawn.

A REFERENCE SCENARIO:THE ITALIAN CASE

The considered reference scenario is shown inTable 1a, which contains the number of devicesper network segment and their energy consump-tion in the business-as-usual case (i.e., the casein which no green enhancements are included innetwork devices). Both end-user and operatorequipment have been taken into account.

We refer to a network with 17.5 million cus-tomers, where we assume the presence of broad-band-only access technologies, together withsuitable overprovisioning in the metro, transport,and core segments.

Devices’ energy consumption has been forecaston the basis of present values, high quality specifica-tions (e.g., European Broadband Code of Conduct),and expected “inertial” technological improvements(e.g., Dennard’s law [1]). In the devices’ energy

Table 1. a) 2015–2020 network forecast: device density and energy requirements in the business as usual(BAU) case, example based on the Italian network; b) traffic and topological data (average figures) for the2015–2020 prospective network (source: Telecom Italia); c) internal sources of energy consumption(source: the ECONET Consortium).

(a)

Powerconsumption (W) Number of devices Overall consumption

(GWh/year)

HomeAccessMetro/transportCore

1012806000

10,000

17,500,00027,3441750175

15333079215

Overall network consumption 1947

(b)

Home accessNumber of customers per DSLAMUsage of a network access (user up time)Link utilization when a user is connected

64030%10%

Core, transport,and metro

Redundancy degree for metro/transport devices ( t)Redundancy degree for core devices ( c)Redundancy degree of metro/transport links ( t)Redundancy degree of core device links ( c)Link utilization in metro networksLink utilization in core networks

13%100%100%50%40%40%

(c)

Data plane ( d) Control plane ( c) Cooling/power supply ( p)

HomeAccessMetro/transportCore

79%84%73%54%

3%3%

13%11%

18%13%14%35%

The main aim of

ECONET is to design

and develop

innovative solutions

and device

prototypes for

wired network

infrastructures (from

customer-premises

equipment to

backbone switches

and routers)

within 2013.


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IEEE Communications Magazine • August 201182

requirements, we included the contribution of sitecooling and powering systems, which account for 36percent of direct device consumption.

Starting from the scenario in Table 1, the per-user average energy requirement consists of about111 kWh/year, mainly due to home and accessnetworks, for 79 and 16 percent, respectively.Metro/transport and core networks account onlyfor 5 percent, but their joint energy requirementof about 107 GWh/year (about a quarter of thetelco’s direct energy consumption) can be a con-vincing driver for reducing the carbon footprint ofbackbone devices in the near future.

Table 1b defines key parameters that allowsynthetically representing the average usage ofnetwork devices and links. This has been doneby defining the expected (by 2015–2020) averagecustomer up times and loads, the average trafficutilization on metro/transport and core net-works, and the number of devices and links thatare usually deployed for redundancy purposes. Itis worth noting that the traffic load values inTable 1b are significantly larger (and conse-quently give rise to conservative consumptionestimations) than those of the current networkand indicated in other studies [1].

Finally, the values in Table 1c, which subdi-vides the equipment consumption into its mainfunctions/building blocks, outline how the mostenergy-starving elements in network devicesreside in the data plane. In fact, data plane ener-gy shares range between 54 and 84 percent,against 13–35 percent for air cooling and powersupply for onboard components, and 3–13 percentfor control plane ones. Thus, it is not so surpris-ing that the current GNT proposals in this fieldare especially devoted to reducing the energy con-sumption of the network devices’ data plane.

GREEN NETWORK TECHNOLOGIESThe largest part of approaches undertaken isfounded on a few basic concepts, which havebeen generally inspired by energy-saving mecha-nisms and power management criteria that arealready partially adopted in computing systems.Following the taxonomy proposed in [1], thesebasic concepts can be classified as dynamic powerscaling and smart standby approaches.

DYNAMIC POWER SCALINGPower scaling capabilities allow dynamically reduc-ing the working rate of processing engines or linkinterfaces. This is usually accomplished by adopt-ing two basic techniques: adaptive rate and LowPower Idle (LPI). The former allows dynamicallymodulating the capacity of a link or a processingengine in order to meet traffic load and servicerequirements. The latter forces links or processingengines to enter low-power states when not send-ing/processing packets and quickly switch to ahigh-power state when sending one or more pack-ets. These techniques are not exclusive and can bejointly adopted in order to adapt system perfor-mance to current workload requirements.

In previous work [4–6], some of the authorsof the present article first faced the energy effi-ciency issue in wireline networks and developedseveral algorithms that used traffic prediction toput links to low power idle modes. The idea

behind these algorithms is simple: the upstreaminterface on a link maintains a window of inter-packet arrival times. This information is thenused to determine the length of time for whichan interface can be put to sleep such that, with ahigh probability, the buffers at that interface willnot overflow. The constraint is the non-zero timeit takes for the downstream interface to wakeup. The results obtained with real traces showthat for loads up to 30 percent of link capacity,considerable energy savings can be achieved.

Similarly, the research of Christensen et al. hasspecifically addressed how to reduce direct energyuse of Ethernet links, and has contributed to thedevelopment of the IEEE 802.3az standard. In [7]they first explored the notion of an adaptive linkrate (ALR) for Ethernet, whereby a link wouldoperate at a low data rate during periods of lowutilization and at high data rate only for high uti-lization periods. Given that most links are highlyunderutilized, with ALR most Ethernet linkscould operate at a low data rate (and thus reduceenergy consumption compared to operation at ahigh data rate) most of the time [8].

An implementation of ALR would entail anEthernet interface having two physical layerimplementations and switching between them.The time to switch between physical layer imple-mentations was deemed to be a major issue,resulting in an alternative LPI approach [9] pro-posed by Intel. LPI is the approach specified inthe emerging 802.3az standard, and currentlyallows a 10 Gb/s link to wake up in less than 3 s.

In [10], Bolla et al. analyzed and empiricallymodeled the energy modulation capabilities ofprocessing engines in Linux-based software routersequipped with general-purpose and multicore pro-cessors that already include LPI and adaptive rateprimitives. The results achieved were obtained byevaluating several hardware architectures, andthey suggest that such technologies permit thetrade-off between power consumption and net-work performance to scale almost linearly.

In [11], the authors extended their approachby introducing a control framework for optimallytuning LPI and adaptive rate mechanisms inorder to statistically meet current traffic loadsand service requirements. The results obtainedon real traffic traces show that energy gains upto 60 percent are feasible.

Working on similar platforms, Intelresearchers [12] especially focused on LPI primi-tives and performed a comprehensive study of theimpact of transition times on LPI as a function ofload. They showed that as the transition timesshrink from the value of 10 ms to 1 ms and thenfurther to 100 s, the time spent sleeping at 30percent load goes from 0 at transition time of 10ms to 40 percent when this time is 1 ms, and to 70percent when the transition happens in 100 s.

SMART STANDBYSleeping and standby approaches are founded onpower management primitives that allow devicesor parts of them turning themselves almost com-pletely off and entering very low energy states,while all their functionalities are frozen.

The widespread adoption of this kind of ener-gy-aware capability is generally hindered by thenecessity of maintaining the “network presence”

The largest part of

approaches

undertaken is

founded on a few

basic concepts,

which have been

generally inspired

by energy-saving

mechanisms and

power management

criteria that are

already partially

adopted in

computing systems.


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of the device. Network hosts and devices mustmaintain network connectivity or they will liter-ally “fall off the network,” become unreachable,and network applications and services will fail.

In [13, 14], Christensen et al. first exploredthe idea of using a proxy to “cover” for a net-work host and thus allow it to go to sleep. Specif-ically, they addressed requirements for a proxyto be able to respond to ARP packets on behalfof a sleeping host (to maintain reachability fromthe router), and to respond to other protocoland application messages as needed to maintainfull network presence. A specific focus of theirresearch was on peer-to-peer (P2P) protocolsand how they might be proxied to allow for a PCsharing files to sleep most of the time. The prox-ying requirements they developed led to the cre-ation of the European Computer ManufacturersAssociation (ECMA) TC38-TG4 standard.Apple has recently announced a product withgreen proxying capabilities.

Nonetheless, the development of such net-work-specific low-energy modes is a fundamentalkey factor for reducing the carbon footprint insidenetworks as well, since it will allow switchingsome links, entire network devices, or parts there-of to a sleep mode in a smart and effective way.This is the main idea behind emerging approach-es to network control, routing, and traffic engi-neering [15], which aim at dynamically puttingnetwork portions to sleep during light utilizationperiods, in order to minimize the energy require-ments of the overall network while meeting theoperational constraints and current workloads.

IMPACT ANALYSISThis section is organized as follows. We estimatethe impact of GNTs on energy consumption offuture network devices. We try to assess howmuch these technologies can really be exploited.Finally, we aggregate all the estimated data forgiving a complete overview of the potentialimpact of GNTs in terms of energy savings, andreduction of both CO2 emissions and OPEX.

THE DEVICE ENERGY PROFILESEnergy-aware devices are meant to dynamicallysave energy by applying the adaptive capacitiesof GNTs introduced earlier.

Due to the heterogeneity of functional andperformance requirements of the various kindsof networking equipment, different exploitationsof green optimizations and technologies can bereasonably expected. Anyway, the joint adoptionof these green optimizations will lead future net-work devices to have a composite energy profile,

depending on their current workload and ontheir actual usage status. From a simplified pointof view, we characterized such composite energyprofile for a generic network device by consider-ing the following power breakdown:• Pfull is the power absorption of an entirely

busy device, and corresponds to no greenoptimization.

• Pidle is the power absorption of a device thatis active, but not performing any opera-tions.

• Pstandby is the power absorption of a devicein low-energy standby modes; only basicoperations to maintain the network pres-ence are performed.We assume that, as experimentally demon-

strated on software router architectures in [10],power consumption values between the “idle”and “full” cases vary linearly with respect to theactual workload.

The specific values of Pidle and Pstandby dependon the efficiency of power scaling and standbyprimitives that will be adopted in future networkdevices.

In order to provide a solid basis for our esti-mation, we decided to use the same efficiencydegrees that are targeted by the ECONET pro-ject. Table 2 reports such minimum efficiencytargets. Taking the full consumption of thedevice as a term of comparison, the andparameters represent how much energy can besaved by the data plane hardware in standby andidle modes, respectively. The parameter weighsan indirect gain on the device’s power supplyand air cooling due to the use of power scalingprimitives. Finally, is meant to represent theimpact of network-wide control strategies (e.g.,traffic engineering) for optimizing the energy-aware configuration of the overall network attransport and core levels.

Starting from these considerations and theabove definitions, we expressed Pidle and Pstandbyas follows:

Pidle = Pfull[(1 – ) d + c + (1 – ) p] (1)

Pstandby = Pfull[(1 – ) d + c + (1 – ) p] (2)

where d, c, and p are defined in Table 1c.Focusing on Eqs. 1 and 2, we can outline how

the power requirements of the data plane in idleand standby states are scaled down by and ,respectively, while those of the control plane ele-ments are maintained constant and equal to the fullload condition. This is due to the fact that energy-aware network devices are meant to keep their pres-ence in the network by sending, elaborating, andreceiving signaling and control protocol packets.

Figure 1 shows the estimated energy profilesfor energy-aware future devices following the2015–2020 forecast in Table 1a.

POWER SCALING AND STANDBY EXPLOITATIONIn order to estimate the real energy-savingscoming from a massive adoption of GNTs, weneed to know the device energy profiles, and wehave also to estimate which and how much stand-by and power scaling primitives would be exploit-ed in real operating scenarios for bothaccess/home networks and transport/core ones.

Table 2. Green efficiency degrees targeted in theECONET project.

Standby ( ) 85%

Performance scaling ( ) 50%

Network-wide control ( ) 20%

Air cooling/power supply ( ) 15%

The widespread

adoption of this kind

of energy-aware

capability is generally

hindered by the

necessity to maintain

the “network pres-

ence” of the device.

Network hosts and

devices must main-

tain network con-

nectivity or else they

will literally “fall off

the network.”


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Focusing on the standby capabilities, it is rea-sonable to expect a different exploitation way inthe home and access devices with respect to thetransport/core ones.

Regarding end-users and network access,standby modes can be thought to suitably followthe end-user behavior — for example, by tem-porarily putting home gateways or digital sub-scriber line (DSL) links in standby states when notdirectly used or not needed. Thus, the averagetime, in which home devices, or links in an accessdevice can be suitably put in standby modes, main-ly depends on end-users’ up-times. Recalling theaverage end-user up-time (i.e., 30 percent, whichcorresponds to about 7 h/day) from Table 2, wecan deduce that home gateways and access linkscan be put in standby for 70 percent of the time.

Regarding backbone network infrastructures,we have to take tighter performance and opera-tional constraints into account. Core, transportand metro network devices have usually to workon large volumes of traffic, and to guarantee topperformance in terms of quality of service (QoS),network reliability, and so on.

Here, while real operating devices and partsof them cannot be put in standby modes, there isa large set of redundant devices and links that isleft powered on only to fast recover from faults.Our main proposal is that devices working atthese levels should include specific standby sup-port to allow redundant hardware (i.e., entiredevices, line cards, etc.) to be woken up uponfault detection with very fast recovery times. Forthese reasons, such elements cannot be simplyshut off, but they need to maintain their networkcontrol and signaling activities to always haveup-to-date network data and information.

According to Table 1, at the core networklevel, each active device is coupled with a redun-dant copy, and furthermore each active device hasa quarter of its network links still in redundancy.Thus, the actually operating hardware elements in

a core network correspond to about 37.5 percent1

of the deployed (and powered) ones. Similarremarks can be made also for transport devices,where the redundancy degree is however usuallyless than the one at the core level. Still accordingto Table 1, redundant transport/metro nodes areabout 13 percent of the deployed ones, while eachactive device usually has a redundant copy foreach of its operating links. In such case, the per-centage of operating hardware results being about43.5 percent of the network deployment. More-over, green traffic engineering and routing mech-anisms can further reduce the number of activelinks and devices [15], while meeting desired ser-vice requirements and traffic loads. Recent stud-ies [15] demonstrate that such policies can lead,on average, to put more than 20 percent of theactive devices to sleep. As shown in Fig. 2, byapplying this last “energy gain” we obtained theaverage shares of time with reference to theaccess and home networks, and the shares ofdevices and parts of them with reference to thetransport and core networks where standby primi-tives can be exploited fruitfully.

Regarding the exploitation of power scalingmechanisms, they may be applied to active devices,and allow modulating power consumption withrespect to the processed traffic loads. Startingfrom the discussions earlier, we can easily deducethat the average power need of a generic networkdevice using power scaling primitives can be esti-mated to be equal to (1 – )Pidle + Pfull, whereis the average link (and device) utilization report-ed in Table 1b for each network segment.

THE OVERALL IMPACTThis impact analysis has been performed by con-sidering both the devices working inside thetelco network and the customer premises equip-ment (thus, our estimation covers the wholewireline network, from home gateway to corerouters). For all such devices, we used the ener-

Figure 1. a) Energy profiles of next generation green home gateways; b), digital subscriber line access multiplexers (DSLAMs); c)metro/transport; d) core devices. The energy profiles have been obtained by using Eqs. 1 and 2 and the input data in Tables 1 and 2.

(a)Full load Idle Standby

5

Pow

er c

onsu

mpt

ion

(Wh)

10

0

15

(b)Full load Idle Standby

500

Pow

er c

onsu

mpt

ion

(Wh)

1000

0

1500

(c)Full load Idle Standby

2000

Pow

er c

onsu

mpt

ion

(Wh)

4000

0

8000

6000

(d)Full load Idle Standby

5000

Pow

er c

onsu

mpt

ion

(Wh)

10,000

0

15,000

1 This value has beenobtained as (1 – c/2)

c/2 with reference toTable 1b.


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gy profiles in Fig. 1 by weighing them with theexploitation shares in Fig. 2.

Table 3 reports the overall impact of GNTsin terms of energy consumption reduction withrespect to the BAU scenario, which was intro-duced in Table 1a. The obtained values showthat the energy requirements of the referencenetwork can be sensibly scaled down by about1331 GWh/year (which roughly equals 956ktons/year of CO2 emissions), corresponding to68 percent of BAU energy consumption — a sig-nificant figure, comparable (and additional!) tothe improvement obtainable by the sole increasein hardware efficiency (as predicted by Den-nard’s law) with respect to the capacity increaseof network devices.

This gain especially arises from the customerside, where, by considering only the savings at thehome gateways, we obtained a potential reductionequal to 1060 GWh/year, which corresponds toabout 70 percent with respect to the BAUrequirements. The energy gain would be muchlarger if we also considered the potential addi-tional savings of GNTs applied to other customerdevices like set-top-boxes, VoIP phones, and PCs.

Also, the telco side energy savings, eventhough corresponding to about a quarter of thoseat the customer side , show an almost surprisingimpact: a total gain equal to 271 GWh/year (areduction of more than 65 percent with respectto the BAU scenario). Using the EIA2 forecast-ing on 2020 energy cost, this energy saving can bedirectly translated to a total OPEX reduction ofnearly $33 million/year for the reference Telco.

This considerable OPEX reduction especiallyarises from devices working at the access level,which, thanks to their numerousness, account for78.6 percent of the overall Telco’s gain. AlsoGNTs applied to metro/transport and core net-work devices, which account for 18.1 percent and3.3 percent, respectively, provide a non negligibleimpact in terms of both OPEX saving and carbonfootprint reduction, especially when compared totheir number in the network.

Finally, Fig. 3 shows how the overall energygain varies according to different values of effi-ciency degrees for standby and power scalingtechnologies. These results outline that standbyprimitives have a sensibly higher impact on thefinal gain than the power scaling ones.

CONCLUSIONWe propose a forecast of the potential impact ofGNTs, if massively adopted in a large-scale oper-ator network by 2015–2020. Our estimates consid-er both the equipment inside the telco networkand the customer premises equipment, and wetarget the same short-term energy efficiency goalsas the ECONET European project. The figuresresulting from our calculations on this basis out-line that GNTs can allow saving about 68 percentof energy requirements of the overall networkand an OPEX reduction for the single referencetelco of about $33 million/year, due to a gain of271 GWh/year in the energy consumption of wire-line infrastructures. These figures are the upperbounds of what could be attained, whereas the

Figure 2. a) Standby exploitation shares for access and home devices; b) for metro and transport ones; c) for core routers. In case a), suchtimes correspond to the user activity profiles (Table 1b). In the b) and c) cases, standby times arise from both the share of redundanthardware and the devices (and/or parts of them) put in standby modes by green traffic engineering and routing mechanisms. The figuresalso report the average utilization of devices when active.

Active30%

(at 10%of themax

capacity)

Active37.5%

(at 40%of themax

capacity)

Active30%

(at 40%of themax

capacity)

Standby70%

Standby70%

Standby62.5% Network-wide

control9.4%

Network-widecontrol7.5%

Redundant HW53.1%

RedundantHW

62.5%

(a) (b) (c)

Table 3. Impact of green technologies on the 2015–2020 perspective network in terms of energy savings.

Full load powerconsumption (Wh)

Number ofdevices

Overall full consumption(GWh/year) Percentage gains Energy gains

(GWh/year)

Home 10 17,500,000 1533 70% 1060

Access 1,280 27,344 307 70% 213

Metro/transport 6,000 1,750 92 54% 49

Core 10,000 175 15 58% 9

Overall gain 68%

Total BAU (GWh/year) 1947 Total gains with green technologies (GWh/year) 1331

2 EIA, U.S. Energy Infor-mation Administration,http://www.eia.doe.gov/.


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actual savings will depend on the extent of theadoption of the new methodologies. The technol-ogy to achieve these savings is there. At thispoint, the only question that remains open iswhether these numbers will be impressive enoughto convince research and industrial communitiesto set forth the actual development of a greenerInternet. Whereas telcos should be encouraged bythe potential OPEX reduction, customers andregulators would also support the process onceprovided with a simple and clear explanation oftheir own cost savings, especially if guaranteed bycertification authorities (e.g., Energy Star3).

ACKNOWLEDGMENTSThis work was supported by the European FP7ECONET project under grant agreement no.258454.

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[3] The “low Energy COnsumption NETworks” (ECONET)Project, IP project funded by the EC in ICT-Call 5 of the7th Framework Programme, grant agreement no.258454, http://www.econet-project.eu.

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[9] R. Hays, “Active/Idle Toggling with 0BASE-x for EnergyEfficient Ethernet,” presentation to the IEEE 802.3azTask Force, Nov. 2007, http://www.ieee802.org/3/az/public/nov07/hays_1_1107.pdf

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[12] S. Nedevschi et al., “Reducing Network Energy Con-sumption via Sleeping and Rate-Adaptation,” Proc.USENIX NSDI ’08, San Francisco, CA, USA, Apr. 2008.

[13] K. Christensen and F. Gulledge, “Enabling Power Man-agement for Network-Attached Computers,” Int’l. J.Network Mgmt., vol. 8, no. 2, Apr. 1998, pp. 120–30.

[14] M. Jimeno, K. Christensen, and B. Nordman, “A NetworkConnection Proxy to Enable Hosts to Sleep and Save Ener-gy,” Proc. IEEE IPCCC ’09, Dec. 2009, pp. 101–10.

[15] J. Restrepo, C. Gruber, and C. Machoca, “Energy Pro-file Aware Routing,” Proc. IEEE GreenComm ’09, Dres-den, Germany, June 2009.

BIOGRAPHIESRAFFAELE BOLLA [M] ([email protected]) received hisLaurea degree in electronic engineering in 1989 and hisPh.D. degree in telecommunications in 1994, both fromthe University of Genoa, Italy. He is currently an associateprofessor at the Department of Communications, Comput-er and Systems Science (DIST) of the University of Genoa.His main current research interests are in the future Inter-net and green networking. He is the Principal Investigatorof the ECONET project.

ROBERTO BRUSCHI [M] ([email protected]) received hisM.Sc. degree in telecommunication engineering in 2002and his Ph.D. degree in electronic engineering in 2006from the University of Genoa, Italy. He is currently aresearcher at the National Inter-University Consortium forTelecommunications (CNIT) at the University of GenoaResearch Unit. His main research interests include thefuture Internet, green networking, and software routers.

KEN CHRISTENSEN [SM] ([email protected]) is a professorin the Department of Computer Science and Engineering atthe University of South Florida. He received his Ph.D. inelectrical and computer engineering from North CarolinaState University in 1991. His primary research interest is ingreen networks. He is a licensed Professional Engineer inthe state of Florida, and a member of ACM and ASEE.

FLAVIO CUCCHIETTI ([email protected]) is withTelecom Italia R&D Center in Turin, Italy. He is managinginnovative projects on infrastructural and powering solu-tions for the next-generation network. He is currently amember of many standardization groups acting on energyefficiency for telecommunications and IT, and networkinfrastructure. He co-chairs the Energy Efficiency InterOperators Collaboration Group (GeSi-EE IOCG). He is aboard member of the Italian standardization institute inthe electrical, electronic and telecommunication fields (CEI).

FRANCO DAVOLI [SM] ([email protected]) received his Laureadegree in electronic engineering in 1975 from the University ofGenoa, Italy. He is currently a full professor of telecommunica-tion networks at DIST, University of Genoa. His currentresearch interests are in multiservice networks, green network-ing, wireless networks, and multimedia communications andservices. He is a member of the CNIT Scientific Board.

SURESH SINGH [M] ([email protected]) received his Ph.D. in com-puter science from the University of Massachusetts at Amherst in1990 and his B.Tech., also in computer science, from the IndianInstitute of Technology in 1984. He is currently a professor in theDepartment of Computer Science at Portland State University.His research interests include green technologies for Internetdevices, energy-efficient computation, wireless networking pro-tocols, information theory, and performance analysis.

Figure 3. Overall energy gain for the whole network (including both customerand telco sides) with respect to different efficiency degrees of standby andpower scaling mechanisms.

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