reducing network energy consumption via sleeping and …reducing network energy consumption via...

Reducing Network Energy Consumptionvia Sleeping and Rate-Adaptation

Sergiu Nedevschi∗† Lucian Popa∗† Gianluca Iannaccone †

Sylvia Ratnasamy † David Wetherall‡§

AbstractWe present the design and evaluation of two forms ofpower management schemes that reduce the energyconsumption of networks. The first is based on puttingnetwork components to sleep during idle times, reducingenergy consumed in the absence of packets. The secondis based on adapting the rate of network operation to theoffered workload, reducing the energy consumed whenactively processing packets.

For real-world traffic workloads and topologies and us-ing power constants drawn from existing network equip-ment, we show that even simple schemes for sleepingor rate-adaptation can offer substantial savings. For in-stance, our practical algorithms stand to halve energyconsumption for lightly utilized networks (10-20%). Weshow that these savings approach the maximum achiev-able by any algorithms using the same power manage-ment primitives. Moreover this energy can be saved with-out noticeably increasing loss and with a small and con-trolled increase in latency (<10ms). Finally, we showthat both sleeping and rate adaptation are valuable de-pending (primarily) on the power profile of networkequipment and the utilization of the network itself.

1 IntroductionIn this paper, we consider power management fornetworks from a perspective that has recently begunto receive attention: the conservation of energy foroperating and environmental reasons. Energy consump-tion in network exchanges is rising as higher capacitynetwork equipment becomes more power-hungry andrequires greater amounts of cooling. Combined withrising energy costs, this has made the cost of poweringnetwork exchanges a substantial and growing fractionof the total cost of ownership – up to half by someestimates[23]. Various studies now estimate the powerusage of the US network infrastructure at between 5and 24 TWh/year[25, 26], or $0.5-2.4B/year at a rateof $0.10/KWh, depending on what is included. Publicconcern about carbon footprints is also rising, and standsto affect network equipment much as it has computers

∗University of California, Berkeley†Intel Research, Berkeley‡University of Washingon§Intel Research, Seattle

via standards such as EnergyStar. In fact, EnergyStarstandard proposals for 2009 discuss slower operationof network links to conserve energy when idle. A newIEEE 802.3az Task Force was launched in early 2007 tofocus on this issue for Ethernet [15].

Fortunately, there is an opportunity for substantial re-ductions in the energy consumption of existing networksdue to two factors. First, networks are provisioned forworst-case or busy-hour load, and this load typicallyexceeds their long-term utilization by a wide margin.For example, measurements reveal backbone utilizationsunder 30% [16] and up to hour-long idle times at accesspoints in enterprise wireless networks [17]. Second, theenergy consumption of network equipment remains sub-stantial even when the network is idle. The implicationof these factors is that most of the energy consumed innetworks is wasted.

Our work is an initial exploration of how overallnetwork energy consumption might be reduced withoutadversely affecting network performance. This willrequire two steps. First, network equipment rangingfrom routers to switches and NICs will need power man-agement primitives at the hardware level. By analogy,power management in computers has evolved aroundhardware support for sleep and performance states. Theformer (e.g.,C-states in Intel processors) reduce idle con-sumption by powering off sub-components to differentextents, while the latter (e.g.,SpeedStep, P-states in Intelprocessors) tradeoff performance for power via operatingfrequency. Second, network protocols will need to makeuse of the hardware primitives to best effect. Again, byanalogy with computers, power management preferencescontrol how the system switches between the availablestates to save energy with minimal impact on users.

Of these two steps, our focus is on the networkprotocols. Admittedly, these protocols build on hardwaresupport for power management that is in its infancyfor networking equipment. Yet the necessary supportwill readily be deployed in networks where it provesvaluable, with forms such as sleeping and rapid rateselection for Ethernet [15] already under development.For comparison, computer power management compat-ible with the ACPI standard has gone from scarce towidely deployed over the past five to ten years and isnow expanding into the server market. Thus our goal is

NSDI ’08: 5th USENIX Symposium on Networked Systems Design and ImplementationUSENIX Association 323

to learn: what magnitude of energy savings a protocolusing feasible hardware primitives might offer; what per-formance tradeoff comes with these savings; and whichof the feasible kinds of hardware primitives would max-imize benefits. We hope that our research can positivelyinfluence the hardware support offered by industry.

The hardware support we assume from network equip-ment is in the form of performance and sleep states.Performance states help to save power when routersare active, while sleep states help to save power whenrouters are idle. The performance states we assumedynamically change the rate of links and their associatedinterfaces. The sleep states we assume quickly poweroff network interfaces when they are idle. We developtwo approaches to save energy with these primitives.The first puts network interfaces to sleep during shortidle periods. To make this effective we introduce smallamounts of buffering, much as 802.11 APs do forsleeping clients; this collects packets into small burstsand thereby creates gaps long enough to profitablysleep. Potential concerns are that buffering will addtoo much delay across the network and that bursts willexacerbate loss. Our algorithms arrange for routers andswitches to sleep in a manner that ensures the bufferingdelay penalty is paid only once (not per link) and thatrouters clear bursts so as to not amplify loss noticeably.The result is a novel scheme that differs from 802.11schemes in that all network elements are able to sleepwhen not utilized yet added delay is bounded. Thesecond approach adapts the rate of individual links basedon the utilization and queuing delay of the link.

We then evaluate these approaches using real-worldnetwork topologies and traffic workloads from Abileneand Intel. We find that: (1) rate-adaptation and sleepinghave the potential to deliver substantial energy savingsfor typical networks; (2) the simple schemes we developare able to capture most of this energy-saving potential;(3) our schemes do not noticeably degrade networkperformance; and (4) both sleeping and rate-adaptationare valuable depending primarily on the utilization ofthe network and equipment power profiles.

2 Approach

This section describes the high-level model for powerconsumption that motivates our rate adaptation andsleeping solutions, as well as the methodology by whichwe evaluate these solutions.

2.1 Power Model Overview

Active and idle power A network element is activewhen it is actively processing incoming or outgoing traf-fic, and idle when it is powered on but does not processtraffic. Given these modes, the energy consumption for

a network element is:

E = paTa + piTi (1)

where pa, pi denote the power consumption in activeand idle modes respectively and Ta, Ti the times spentin each mode.

Reducing power through sleep and performancestates Sleep states lower power consumption byputting sub-components of the overall system to sleepwhen there is no work to process. Thus sleeping reducesthe power consumed when idle, i.e.,it reduces the piTi

term of Eqn. (2) by reducing the pi to some sleep-modepower draw ps where ps < pi.

Performance states reduce power consumption bylowering the rate at which work is processed. As weelaborate on in later sections, some portion of bothactive and idle power consumption depends on thefrequency and voltage at which work is processed.Hence performance states that scale frequency and/orvoltage reduce both the pa and pi power draws resultingin an overall reduction in energy consumption.

We also assume a penalty for transitioning betweenpower states. For simplicity, we measure this penaltyin time, typically milliseconds, treating it as a period inwhich the router can do no useful work. We use this as asimple switching model that lumps all penalties, ignor-ing other effects that may be associated with switchessuch as a transient increase in power consumption. Thusthere is also a cost for switching between states.

Networks with rate adaptation and sleeping supportIn a network context, the sleeping and rate adaptationdecisions one router makes fundamentally impacts –and is impacted by – the decisions of its neighboringrouters. Moreover, as we see later in the paper, thestrategies by which each is best exploited are verydifferent (Intuitively this is because sleep-mode savingsare best exploited by maximizing idle times, whichimplies processing work as quickly as possible, whileperformance-scaling is best exploited by processingwork as slowly as possible, which reduces idle times).Hence, to avoid complex interactions, we consider thatthe whole network, or at least well-defined componentsof it, run in either rate adaptation or sleep mode.

We develop the specifics of our sleeping schemes inSection 3, and our rate adaptation schemes in Section 4.Note that our solutions are deliberately constructed toapply broadly to the networking infrastructure – fromend-host NICs, to switches, and IP routers, etc. – so thatthey may be applied wherever they prove to be the mostvaluable. They are not tied to IP-layer protocols.

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2.2 Methodology

The overall energy savings we can expect will depend onthe extent to which our power-management algorithmscan successful exploit opportunities to sleep or rateadapt as well as the power profile of network equipment(i.e.,, relative magnitudes of pa, pi and ps). To clearlyseparate the effect of each, we evaluate sleep solutions interms of the fraction of time for which network elementscan sleep and rate-adaptation solutions in terms of thereduction in the average rate at which the networkoperates. 1 In this way we assess each solution withthe appropriate baseline. We then evaluate how thesemetrics translate into overall network energy savings fordifferent equipment power profiles and hence comparethe relative merits of sleeping and rate-adaptation(Section 5). For both sleep and rate-adaptation, wecalibrate the savings achieved by our practical solutionsby comparing to the maximum savings achievable byoptimal, but not necessarily practical, solutions.

In network environments where packet arrival ratescan be highly non-uniform, allowing network elementsto transition between operating rates or sleep/activemodes with corresponding transition times can introduceadditional packet delay, or even loss, that would have nototherwise occurred. Our goal is to explore solutions thatusefully navigate the tradeoff between potential energysavings and performance. In terms of performance,we measure the average and 98th percentile of theend-to-end packet delay and loss.

In the absence of network equipment with hardwaresupport for power management, we base our evaluationson packet-level simulation with real-world networktopologies and traffic workloads. The key factors onwhich power savings then depend, beyond the details ofthe solutions themselves, are the technology constants ofthe sleep and performance states and the characteristicsof the network. In particular, the utilization of linksdetermines the relative magnitudes of Tactive and Tidle

as well as the opportunities for profitably exploitingsleep and performance states. We give simple models fortechnology constants in the following sections. To cap-ture the effect of the network on power savings, we driveour simulation with two realistic network topologies andtraffic workloads (Abilene and Intel) that are summa-rized below. We use ns2 as our packet-level simulator.

Abilene We use Abilene as a test case because of theready availability of detailed topology and traffic infor-mation. The information from [27] provides us with thelink connectivity, weights (to compute routes), latenciesand capacities for Abilene’s router-level topology. Weuse measured Abilene traffic matrices (TMs) availablein the community [29] to generate realistic workloadsover this topology. Unless otherwise stated, we use as

our default a traffic matrix whose link utilization levelsreflect the average link utilization over the entire day –this corresponds to a 5% link utilization on average withbottleneck links experiencing about 15% utilization.

We linearly scale TMs to study performance withincreasing utilization up to a maximum average networkutilization of 31% as beyond this some links reach veryhigh utilizations. Finally, while the TMs specify the5-minute average rate observed for each ingress-egresspair, we still require a packet-level traffic generationmodel that creates this rate. In keeping with previousstudies [18, 31] we generate traffic as a mix of Paretoflows, and for some results we use constant bit-rate(CBR) traffic. As per standard practice, we set routerqueue sizes equal to the bandwidth-delay product in thenetwork; we use the bandwidth of the bottleneck link,and a delay of 100ms.

Intel As an additional real-world dataset, we collectedtopology and traffic information for the global Intelenterprise network. This network connects Intel sitesworldwide, from small remote offices to large multi-building sites with thousands of users. It comprisesapproximately 300 routers and over 600 links withcapacities ranging from 1.5Mbps to 1Gbps.

To simulate realistic traffic, we collected unsampledNetflow records [7] from the core routers. The records,exported by each router every minute, contain per flowinformation that allows us to recreate the traffic sourcedby ingress nodes.

3 Putting Network Elements to SleepIn this section we discuss power management algorithmsthat exploit sleep states to reduce power consumptionduring idle times.

3.1 Model and Assumptions

Background A well established technique, as used bymicroprocessors and mobiles, is to reduce idle power byputting hardware sub-components to sleep. For example,modern Intel processors such as the Core Duo [1] havea succession of sleep states (called C-states) that offerincreasingly reduced power at the cost of increasinglyhigh latencies to enter and exit these states. We assumesimilar sleep states made available for network equip-ment. For the purpose of this study we ignore the optionsafforded by multiple sleep states and assume as an initialsimplification that we have a single sleep state.

Model We model a network sleep state as character-ized by three features or parameters. The first is thepower draw in sleep mode ps which we assume to be asmall fraction of the idle mode power draw pi.

The second characterizing parameter of a sleep state isthe time δ it takes to transition in and out of sleep states.Higher values of δ raise the bar on when the network


element can profitably enter sleep mode and henceδ critically affects potential savings. While networkinterface cards can make physical-layer transitions in aslow as 10µs, transition times that involve restoring stateat higher layers (memory, operating system) are likelyto be higher [13]. We thus evaluate our solutions over awide range values of transition times.

Finally, network equipment must support a mechanismfor invoking and exiting sleep states. The option thatmakes the fewest assumptions about the sophistication ofhardware support is timer-driven sleeping, in which thenetwork element enters and exits sleep at well-definedtimes. Prior to entering sleep the network elementspecifies the time in the future at which it will exit sleepand all packets that arrive at a sleeping interface are lost.The second possibility, described in [12], is for routersto wake up automatically on sensing incoming trafficon their input ports. To achieve this “wake-on-arrival”(WoA), the circuitry that senses packets on a line is leftpowered on even in sleep mode. While support for WoAis not common in either computers or interfaces today,this is a form of hardware support that might provedesirable for future network equipment and is currentlyunder discussion in the IEEE 802.3az Task Force [13].Note that even with wake-on-arrival, bits arriving duringthe transition period δ are effectively lost. To handle this,the authors in [6] propose the use of “dummy” packetsto rouse a sleeping neighbor. A node A that wishes towake B first sends B a dummy packet, and then waitsfor time δ before transmitting the actual data traffic. Thesolutions we develop in this paper apply seamlessly toeither timer-driven or WoA-based hardware.

Measuring savings and performance In this section,we measure savings in terms of the percentage of timenetwork elements spend asleep and performance in termsof the average and 98th percentile of the end-to-endpacket delay and loss. We assume that individual linecards in a network element can be independently put tosleep. This allows for more opportunities to sleep than ifone were to require that a router sleep in its entirety (asthe latter is only possible when there is no incoming traf-fic at any of the incoming interfaces). Correspondinglyour energy savings are with respect to interface cardswhich typically represent a major portion of the overallconsumption of a network device. That said, one couldin addition put the route processor and switch fabric tosleep at times when all line cards are asleep.

3.2 Approaches and Potential savings

For interfaces that support wake-on-arrival, one ap-proach to exploiting sleep states is that of opportunisticsleeping in which link interfaces sleep when idle – i.e.,arouter is awakened by an incoming (dummy) packet and,after forwarding it on, returns to sleep if no subsequent

RS

RS

Figure 1: Packets within a burst are organized by destination.

packet arrives for some time. While very simple, suchan approach can result in frequent transitions whichlimits savings for higher transition times and/or higherlink speeds. For example, with a 10Gbps link, evenunder low utilization (5%) and packet sizes of 1KB, theaverage packet inter-arrival time is very small – 15µs.Thus while opportunistic sleeping might be effective inLANs [11, 21] with high idle times, for fast links thistechnique is only effective for very low transition timesδ (we quantify this shortly). In addition, opportunisticsleep is only possible with the more sophisticatedhardware support of wake-on-arrival.

To create greater opportunities for sleep, we considera novel approach that allows us to explicitly controlthe tradeoff between network performance and energysavings. Our approach is to shape traffic into smallbursts at the edges of the network – edge devices thentransmit packets in bunches and routers within thenetwork wake up to process a burst of packets, and thensleep until the next burst arrives. The intent is to providesufficient bunching to create opportunities for sleep ifthe load is low, yet not add excessive delay. This isa radical approach in the sense that much other workseeks to avoid bursts rather than create them (e.g., tokenbuckets for QOS, congestion avoidance, buffering atrouters). As our measurements of loss and delay show,our schemes avoid the pitfalls associated with burstsbecause we introduce only a bounded and small amountof burstiness and a router never enters sleep until it hascleared all bursts it has built up. More precisely, weintroduce a buffer interval “B” that controls the tradeoffbetween savings and performance. An ingress routerbuffers incoming traffic for up to B ms and, once everyB ms, forwards buffered traffic in a burst.

To ensure that bursts created at the ingress are retainedas they traverse through the network, an ingress routerarranges packets within the burst such that all packetsdestined for the same egress router are contiguous withinthe burst (see figure 1).

The above “buffer-and-burst” approach (B&B) createsalternating periods of contiguous activity and sleepleading to fewer transitions and amortizing the transitionpenalty δ over multiple packets. This improvementcomes at the cost of an added end-to-end delay of up


R0 R1

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Figure 2: Examples of burst synchronization.

to B ms. Note that because only ingress routers buffertraffic, the additional delay due to buffering is onlyincurred once along the entire ingress-to-egress path. Asimportantly, this approach – unlike opportunistic sleep –can be used by interfaces that support only timer-drivensleep. A router R1 that receives a burst from upstreamrouter R2 at time t1 knows that the next start-of-burst willarrive at time t1+B and can hence sleep between bursts.

The question then is how significant are the savingsthis approach enables for reasonable additional delay?We note that the best possible savings would occur ifa router received the incoming bursts from all ingressrouters close in time such that it processes all incomingbursts and returns to sleep thus incurring exactly onesleep/wake transition per B ms. This might appear pos-sible by having ingress routers coordinate the times atwhich they transmit bursts such that bursts from differentingresses arrive close in time at intermediate routers.For example, consider the scenario in Figure 2(a) whereingress routers R0 and R1 are scheduled to transmittraffic at times 2 and 1 respectively. If instead R1 wereto schedule its burst for time 7 instead, then bursts fromR0 and R2 would align in time at R2 thus reducingthe number of distinct burst times - and sleep-to-waketransitions - at downstream routers R3 and R4.

Unfortunately, the example in Figure 2(b) suggests thisis unachievable for general topologies. Here, Si and Sj

represent the arrival times of incoming bursts to nodes R3and R1 respectively and we see that the topology makesit impossible to find times Si and Sj that could simulta-neously align the bursts downstream from R2 and R4.

We thus use a brute-force strategy to evaluate themaximum achievable coordination. For a given topologyand traffic workload, we consider the start-of-burst timefor traffic from each ingress I to egress J (denoted Sij)and perform an exhaustive search of all Sij to find a setof start times that minimizes the number of transitionsacross all the interfaces in the network. We call thisscheme optB&B. Clearly, such an algorithm is notpractical and we use it merely as an optimistic bound onwhat might be achievable were nodes to coordinate inshaping traffic under a buffer-and-burst approach.

We compare the sleep time achieved by optB&B to theupper bound on sleep as given by 1 − µ, where µ is the

Average utilization (%)0 10 20

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Idle (bound)WoA(Pareto)WoA(CBR)OptB&B

Figure 3: Time asleep using optB&B and opportunisticsleeping and compared to the upper bound (1 − µ).

network utilization. This upper bound is not achievableby any algorithm since (unlike optB&B) it does not takeinto account the overhead δ due to sleep/wake transi-tions. Nonetheless it serves to capture the loss in savingsdue to δ and the inability to achieve perfect coordination.

Any traffic shaping incurs some additional complexityand hence a valid question is whether we need anytraffic shaping, or whether opportunistic sleeping thatdoes not require shaping is enough? We therefore alsocompare optB&B to opportunistic sleeping based onwake-on-arrival (WoA). For this naive WoA, we assumeoptimistically that an interface knows the precise arrivaltime of the subsequent packet and returns to sleep onlyfor inter-packet arrival periods greater than δ. Becausethe performance of opportunistic WoA depends greatlyon the inter-arrival times of packets we evaluate WoA fortwo types of traffic: constant bit rate (CBR) and Pareto.

For each of the above bounds, Figure 3 plots thepercentage of time asleep under increasing utilizationin Abilene. We use a buffer period of B = 10msand assume a (conservative) transition time δ of 1ms.Comparing the savings from optB&B to the utilizationbound, we see that a traffic shaping approach based onbuffer-and-burst can achieve much of the potential forexploiting sleep. As expected, even at very low utiliza-tion, WoA with CBR traffic can rarely sleep; perhapsmore surprising is that even with bursty traffic WoAperforms relatively poorly. These results suggest that –even assuming hardware WoA – traffic shaping offers asignificant improvement over opportunistic sleep.

3.3 A Practical Algorithm

We consider a very simple buffer-and-burst scheme,called practB&B, in which each ingress router sendsits bursts destined for the various egresses one after theother in a single “train of bursts”. At routers close tothe ingress this appears as a single burst which thendisperses as it traverses through the network.practB&B bounds the number of bursts (and corre-

spondingly the number of transitions) seen by any routerR in an interval of B ms to at most IR, the total numberof ingress routers that send traffic through R. In practice,our results show that the number of bursts seen by R intime Bms is significantly smaller than this bound.


Average utilization (%)0 10 20 30

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optB&BpractB&B (pareto)practB&B (CBR)

Figure 4: Time asleep using CBR and Pareto traffic.

practB&B is simple – it requires no inter-router co-ordination as the time at which bursts are transmitted isdecided independently by each ingress router. For net-works supporting wake-on-arrival the implementation istrivial – the only additional feature is the implementationof buffer-and-burst at the ingress nodes. For networksthat employ timer-driven sleeping, packet bursts wouldneed to include a marker denoting the end of burst andnotifying the router of when it should expect the nextburst on that interface.

3.4 Evaluation

We evaluate the savings vs. performance tradeoffachieved by practB&B algorithm and the impact ofequipment and network parameters on the same.

Savings vs. performance using practB&B Wecompare the sleep time achieved by practB&B to thatachievable by optB&B. In terms of performance, wecompare the end-to-end packet delay and loss in a net-work using practB&B to that of a network that neversleeps (as today) as this shows the overall performancepenalty due to our sleep protocols.

Figure 4 plots the sleep time with increasing utilizationon the Abilene network using a buffering intervalB = 10ms. We plot the percentage sleep time underboth CBR and Pareto traffic workloads. We see that evena scheme as simple as practB&B can create and exploitsignificant opportunities for sleep and approaches thesavings achieved by the significantly more complexoptB&B. As with opportunistic sleeping, we see thatpractB&B’s savings with CBR traffic are lower than forthe more bursty Pareto workloads, but that this reductionis significantly smaller in the case of practB&B thanwith opportunistic sleeping (recall Figure 3). That Paretotraffic improves savings is to be expected as burstiertraffic only enhances our bunching strategy.

Figures 5(a) and (b) plot the corresponding averageand 98th percentile of the end-to-end delay. As expected,we see that the additional delay in both cases is propor-tional to the buffering interval B. Note that this is theend-to-end delay, reinforcing that the buffering delay Bis incurred once for the entire end-to-end path.


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Figure 5: The impact on delay of practB&B.

We see that for higher B, the delay grows slightlyfaster with utilization (e.g.,compare the absolute increasein delay for B = 5ms to 25ms) because this situation ismore prone to larger bursts overlapping at intermediaterouters. However this effect is relatively small evenin a situation combining larger B (25ms) and largerutilizations (20-30%) and is negligible for smaller Band/or more typical utilizations.

We see that both average and maximum delays in-crease abruptly beyond network utilizations exceeding25%. This occurs when certain links approach fullutilization and queuing delays increase (recall thatthe utilization on the horizontal axis is the averagenetwork-wide utilization). However this increase occurseven in the default network scenario and is thus notcaused by practB&B’s traffic shaping.

Finally, our measurements revealed thatpractB&B introduced no additional packet loss (rela-tive to the default network scenario) until we approachutilizations that come close to saturating some links. Forexample, in a network scenario losses greater than 0.1%occur at 41% utilization without any buffering, theyoccur at 38% utilization with B = 10ms, and at 36%utilization with B = 25. As networks do not typicallyoperate with links close to saturation point, we do notexpect this additional loss to be a problem in practice.

In summary, the above results suggest thatpractB&B can yield significant savings with avery small (and controllable) impact on network delayand loss. For example, at a utilization of 10%, a bufferingtime of just B = 5ms allows the network to spend over60% of its time in sleep mode for under 5ms added delay.

Impact of hardware characteristics We now evaluatehow the transition time δ affects the performance ofpractB&B. Figure 6(a) plots the sleep time achieved bypractB&B for a range of transition times and comparesthis to the ideal case of having instantaneous transitions.As expected, the ability to sleep degrades drasticallywith increasing δ. This observation holds across variousbuffer intervals B as illustrated in Figure 6(b) that plotsthe sleep time achieved at typical utilization (≈5%) for



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Figure 6: The impact of hardware constants on sleep time.

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Figure 7: Time asleep per link

increasing transition times and different values of B.These findings reinforce our intuition that hardware

support featuring low-power sleep states and quicktransitions (preferably < 1ms) between these states areessential to effectively save energy.

Impact of network topology We now evaluatepractB&B for the Intel enterprise network. The routingstructure of the Intel network is strictly hierarchical witha relatively small number of nodes that connect to thewide-area. Because of this we find a wide variation inlink utilization – far more than on the Abilene network.Over 77% of links have utilizations below 1% whilea small number of links ( 2.5%) can see significantlyhigher utilizations of between 20-75%. Correspond-ingly, the opportunity for sleep also varies greatly acrosslinks. This is shown in Figure 7 – each point in thescatter-plot corresponds to a single link and we look atsleep times for two transition times: 0.1ms and 1ms. Wesee that the dominant trends in sleep time vs. utilizationremains and that higher δ yields lower savings.

4 Rate-Adaptation in NetworksThis section explores the use of performance states toreduce network energy consumption.

4.1 Model and Assumptions

Background In general, operating a device at a lowerfrequency can enable dramatic reductions in energyconsumption for two reasons. First, simply operatingmore slowly offers some fairly substantial savings.

For example, Ethernet links dissipate between 2-4Wwhen operating between 100Mbps-1Gbps comparedto 10-20W between 10Gbps[3]. Second, operating at alower frequency also allows the use of dynamic voltagescaling (DVS) that reduces the operating voltage. Thisallows power to scale cubically, and hence energy con-sumption quadratically, with operating frequency[32].DVS and frequency scaling are already common inmicroprocessors for these reasons.

We assume the application of these techniques tonetwork links and associated equipment (i.e., linecards,transceivers). While the use of DVS has been demon-strated in prototype linecards [22], it is not currently sup-ported in commercial equipment and hence we investi-gate savings under two different scenarios: (1) equipmentthat supports only frequency scaling and (2) equipmentthat supports both frequency and voltage scaling.

Model We assume individual links can switch perfor-mance states independently and with independent ratesfor transmission and reception on interfaces. Hence thesavings we obtain apply directly to the consumptionat the links and interface cards of a network element,although in practice one could also scale the rate ofoperation of the switch fabric and/or route processor.

We assume that each network interface supportsN performance states corresponding to link ratesr1, . . . , rn (with ri < ri+1 and rn = rmax, the defaultmaximum link rate), and we investigate the effect thatthe granularity and distribution (linear vs. exponential)of these rates has on the potential energy savings.

The final defining characteristic of performance statesis the transition time, denoted δ, during which packettransmission is stalled as the link transitions betweensuccessive rates. We explore performance for a range oftransition times (δ) from 0.1 to 10 milliseconds.

Measuring savings and performance As in the caseof sleep we’re interested in solutions that reduce the rateat which links operate without significantly affectingperformance.

In this section, we use the percentage reduction inaverage link rate as an indicative measure of energysavings and relate this to overall energy savings inSection 5 where we take into account the power profileof equipment (including whether it supports DVS ornot). In terms of performance, we again measure theaverage and 98th percentile of the end-to-end packetdelay and packet loss.

4.2 An optimal strategy

Our initial interest is to understand the extent to whichperformance states can help if used to best effect. Fora DVS processor, it has been shown that the mostenergy-efficient way to execute C cycles within a giventime interval T is to maintain a constant clock speed of


Figure 8: An illustration of delay-constrained service curvesand the service curve minimizing energy.

C/T [20]. In the context of a network link, this translatesinto sending packets at a constant rate equal to theaverage arrival rate. However under non-uniform trafficthis can result in arbitrary delays and hence we insteadlook for an optimal schedule of rates (i.e.,the set of ratesat which the link should operate at different points intime) that minimize energy while respecting a specifiedconstraint on the additional delay incurred at the link.

More precisely, given a packet arrival curve AC, welook for a service curve SC that minimizes energy con-sumption, while respecting a given upper bound d on theper-packet queuing delay. The delay parameter d thusserves to tradeoff savings for increased delay. Figure 8(a)shows an example arrival curve and the associated latest-departure curve (AC+d), which is simply the arrivalcurve shifted in time by the delay bound d. To meet thedelay constraint, the service curve SC must lie withinthe area between the arrival and latest-departure curves.

In the context of wireless links, [19] proves that ifthe energy can be expressed as a convex, monotonicallyincreasing function of the transmission rate, then theminimal energy service curve is the shortest Euclideandistance in the arrival space (bytes × time) between thearrival and shifted arrival curves.

In the scenario where we assume DVS support, theenergy consumption is a convex, monotonically increas-ing function of link rate and thus this result applies toour context as well. Where only frequency scaling issupported, any service curve between the arrival andshifted-arrival curves would achieve the same energysavings and therefore the service curve with the shortestEuclidean distance would be optimal in this case too.In summary, for both frequency-scaling and DVS, theshortest distance service curve would achieve the highestpossible energy savings.

Fig. 8 illustrates an example of such a minimalenergy service curve. Intuitively, this is the set of lowestconstant rates obeying the delay constraint. Note thatthis per-link optimal strategy is not suited to practicalimplementation since it assumes perfect knowledge ofthe future arrival curve, link rates of infinite granularityand ignores switching overheads. Nonetheless, it isuseful as an estimate of the potential savings by which

to calibrate practical protocols.We will evaluate the savings achieved by applying

the above per-link solution at all links in the networkand call this approach link optRA. One issue indoing so is that the service curves at the different linksare inter-dependent – i.e.,the service curve for a linkl depends in turn on the service curves at other links(since the latter in turn determine the arrival curve atl). We address this by applying the per-link optimalalgorithm iteratively across all links until the service andarrival curves at the different links converge.

4.3 A practical algorithm

Building on the insight offered by the per-link opti-mal algorithm, we develop a simple approach, calledpractRA (practical rate adaptation), that seeks to nav-igate the tradeoff between savings and delay constraints.A practical approach differs from the optimum in that(i) it does not have knowledge of future packet arrivals,(ii) it can only choose among a fixed set of availablerates r1, . . . , rn, and (iii) at every rate switch, it incurs apenalty δ, during which it cannot send packets.

While knowledge of the future arrival rate is unavail-able, we can use the history of packet arrivals to predictthe future arrival rate. We denote this predicted arrivalrate as r̂f and estimate it with an exponentially weightedmoving average (EWMA) of the measured history of pastarrivals. Similarly, we can use the current link buffersize q and rate ri to estimate the potential queuing delayso as to avoid violating the delay constraint.

With these substitutes, we define a technique inspiredby the per-link optimal algorithm. In practRA, packetsare serviced at a constant rate until we intersect one ofthe two bounding curves presented earlier (Figure 8):the arrival curve (AC), and the latest-departure curve(AC+d). Thus, we avoid increasing the operating rate ri

unless not doing so would violate the delay constraint.This leads to the following condition for rate increases:

A link operating at rate ri with current queue size q

increases its rate to ri+1 iff ( qri

> d OR δr̂f+q

ri+1> d − δ)

The first term checks whether the delay bound d wouldbe violated were we to maintain the current link rate.The second constraint ensures that the service curvedoes not get too close to the delay-constrained curvewhich would prevent us from attempting a rate increasein the future without violating the delay bound. That is,we need to allow enough time for a link that increasesits rate to subsequently process packets that arrivedduring the transition time (estimated by δr̂f ) and itsalready-accumulated queue. Note that we cannot usedelay constraints d smaller than the transition time δ.Similarly, the condition under which we allow a ratedecrease is as follows:


A link operating at rate ri with current queue size qdecreases its rate to ri−1 iff q = 0 AND r̂f < ri−1

First, we only attempt to switch to a lower rate when thequeue at the link is empty (q = 0). Intuitively, this corre-sponds to an intersection between the arrival curve andthe service curve. However, we don’t always switch to alower rate ri−1 when a queue empties as doing so wouldprevent the algorithm from operating in a (desired)steady state mode with zero queuing delay. Instead, thedesirable steady state is one where ri > rf > ri+1

and we want to avoid oscillating between rates (whichwould lead to larger average delay). For example, alink that sees a constant arrival rate of 3.5Mbps mightoscillate between 3 and 4Mbps (incurring queuingdelays at 3Mbps), instead of remaining at 4Mbps (withlow average queuing delay). We thus use the additionalcondition: r̂f < ri−1 to steer our algorithm toward thedesired steady state and ensure that switching to a lowerrate does not immediately lead to larger queues.

In addition to the above conditions, we further dis-courage oscillations by enforcing a minimum time Kδbetween consecutive switches. Intuitively, this is becauserate switching should not occur on timescales smallerthan the transition time δ. In our experiments, we foundK = 4 to be a reasonable value for this parameter.

Note that unlike the link optRA algorithm, theabove decision process does not guarantee that the delayconstraints will be met since it is based on estimatedrather than true arrival rates. Similarly, practRAcannot guarantee that the rates used by the links matchthose used by the link-optimal algorithm. In Section 5,after discussing how power scales with rate, we usesimulation to compare our approximate algorithm to theoptimal under realistic network conditions. We leave itto future work to analytically bound the inaccuracy dueto our approximations.

Finally, we observe that the above rate-adaptation issimple in that it requires no coordination across differentnodes, and is amenable to implementation in high-speedequipment. This is because the above decision makingneed not be performed on a per-packet basis.

4.4 Evaluation

We evaluate the savings vs. performance tradeoffachieved by our practical rate-adaptation algorithm andthe impact of equipment and network parameters on thesame. We first evaluate the percentage reduction in av-erage link rate achieved by practRA for the Abilenenetwork. For comparison, we consider the rate reductiondue to link optRA and the upper bound on rate reduc-tion as determined by the average link utilization. In thiscase, since we found the reduction from link optRAwas virtually indistinguishable from the utilizationbound, we only show the latter here for clarity. (We re-


Rate

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Figure 10: Average and 98th percentile delay achieved bypractRA for various available rates

port on the energy savings due to link optRA in thefollowing section.) To measure performance, we measurethe end-to-end packet delay and loss in a network usingpractRA to that in a network with no rate adaptation.

Impact of hardware characteristics The main con-stants affecting the performance of practRA are (1)the granularity and distribution of available rates, and(2) δ, the time to transition between successive rates. Weinvestigate the reduction in rate for three different dis-tributions of rates r1, . . . , rn: (i) 10 rates uniformly dis-tributed between 1Gbps to 10Gbps, (ii) 4 rates uniformlydistributed between 1Gbps to 10Gbps and (iii) 4 expo-nentially distributed rates (10Gbps, 1Gbps, 100Mbps,10Mbps). We consider the latter case since physical layertechnologies for these rates already exist making theselikely candidates for early rate-adaptation technologies.

Figure 9 plots the average rate reduction under increas-ing utilizations, with a per-link delay constraint d = δ+ 2ms, and a transition time δ =1ms. We see that for 10uniformly distributed rates, practRA operates links ata rate that approaches the average link utilization. With4 uniformly distributed rates this reduction drops, butnot significantly. However, for exponentially distributedrates, the algorithm performs poorly, indicating thatsupport for uniformly distributed rates is essential.


Transition Time (ms)0.10.5 1 2.5 5 10

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Figure 11: Impact of Switch Time (δ) for practRA

Figure 10 plots the corresponding average and 98thpercentile of the end-to-end delay. We see that, for all thescenarios, the increase in average delay due to practRAis very small: ∼ 5ms in the worst case, and less than 2msfor the scenario using 4 uniform rates. The increase inmaximum delay is also reasonable: at most 78ms withpractRA, relative to 69ms with no adaptation.

Perhaps surprisingly, the lowest additional delayoccurs in the case of 4 uniformly distributed rates. Wefound this occurs because there are fewer rate transitions,with corresponding transition delays, in this case. . Fi-nally, we found that practRA introduced no additionalpacket loss for the range of utilizations considered.

Next, we look at the impact of transition times δ.Figures 11 (a) and (b) plot the average rate reduction and98th percentile of delay under increasing δ for differentnetwork utilizations. For each test, we set the delayconstraint as d = δ + 2ms, and we assume 10 uniformrates. As would be expected, we see that larger δ leadto reduced savings and higher delay. On the whole wesee that, in this scenario, both savings and performanceremain attractive for transition times as high as ∼ 2ms.

In summary, these results suggest that rate adaptationas implemented by practRA has the potential to offerssignificant energy savings with little impact on packetloss or delay. In all our tests, we found practRA to haveminimal effects on the average delay and loss and hence,from here on, we measure the performance impact onlyin terms of the 98th percentile in packet delay.

Impact of network topology We now evaluatepractRA applied to the Intel enterprise network. Fig-ure 12 plots the rate reduction across links - each point inthe scatter-plot corresponds to a single link, and we lookat rate reduction for two rate distribution policies: 10 uni-formly distributed rates and 4 exponentially distributedrates. Since these are per-link results, we see significantvariations in rate reduction for the same utilization, dueto specifics of traffic across various links. We also noticethat the dominant trend in reduction remains similar tothat seen in the Abilene network (Figure 9).

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Figure 12: Rate reduction per link

5 Overall Energy SavingsIn the previous sections we evaluated power-management solutions based on their ability to increasesleep times (Section 3), or operate at reduced rates(Section 4). In this section, we translate these to overallenergy savings and hence compare the relative meritsof rate adaptation vs. sleeping. For this, we develop ananalytical model of power consumption under differentoperating modes. Our model derives from measurementsof existing networking equipment [13, 5, 24]. At thesame time, we construct the model to be sufficientlygeneral that we may study the potential impact of future,more energy-efficient, hardware.

5.1 Power Model

Recall from section 2 that the total energy consump-tion of a network element operating in the absenceof any power-saving modes can be approximated as:E = paTa + piTi.

We start by considering the power consumption whenactively processing packets (pa). Typically, a portionof this power draw is static in the sense that it does notdepend on the operating frequency (e.g.,refresh powerin memory blocks, leakage currents and so forth) whilethe dominant portion of power draw does scale withoperating frequency. Correspondingly, we set:

pa(r) = C + f(r) (2)

Intuitively, C can be viewed as that portion of powerdraw that cannot be eliminated through rate adaptationwhile f(r) reflects the rate-dependent portion of energyconsumption. To reflect the relative proportions of C andf(r) we set C to be relatively small – between 0.1 and0.3 of the maximum active power pa(rn). To study theeffect of just frequency scaling alone we set f(r) = O(r)and set f(r) = O(r3) to evaluate dynamic voltage scal-ing (DVS). In evaluating DVS, we need to consider anadditional constraint – namely that, in practice, there isa minimum rate threshold below which scaling the linkrate offers no further reduction in voltage. We thus definea maximum scaling factor λ and limit our choice of avail-able operating rates to lie between [rn/λ, rn], for scenar-


ios that assume voltage scaling. While current transistortechnology allows scaling up to factors as high as 5 [32],current processors typically use λ ∼ 2 and hence weinvestigate both values as potential rate scaling limits.

Empirical measurements further reveal that the idlemode power draw, pi, varies with operating frequencyin a manner similar to the active-mode power draw butwith lower absolute value[13]. Correspondingly, wemodel the idle-mode power draw as:

pi(r) = C + βf(r) (3)

Intuitively, the parameter β represents the relativemagnitudes of routine work incurred even in the absenceof packets to the work incurred when actively processingpackets. While measurements from existing equipmentsuggest values of β as high as 0.8 for network interfacecards [13] and router linecards [5], we would like to cap-ture the potential for future energy-efficient equipmentand hence consider a wide range of β between [0.1, 0.9].

Energy savings from rate adaptation With the abovedefinitions of pa and pi, we can now evaluate the overallenergy savings due to rate adaptation. The total energyconsumption is now given by:

∑

rk

pa(rk)Ta(rk) + pi(rk)Ti(rk) (4)

Our evaluation in Section 4 yields the values of Ta(rk)and Ti(rk) for different rk and test scenarios, whileEqns. 2 and 3 allow us to model pa(rk) and pi(rk) fordifferent C, β and f(r). We first evaluate the energysavings using rate adaptation under frequency scaling(f(r) = O(r)) and DVS (f(r) = O(r3)). For thesetests, we set C and β to middle-of-the-range valuesof 0.2 and 0.5 respectively; we examine the effect ofvarying C and β in the next section.

Figure 13(a) plots the energy savings for our practical(practRA) and optimal (link optRA) rate adaptationalgorithms assuming only frequency scaling. We seethat, in this case, the relative energy savings for thedifferent algorithms as well as the impact of the differentrate distributions is similar to our previous results (Fig. 9)that measured savings in terms of the average reductionin link rates. Overall, we see that significant savings arepossible even in the case of frequency scaling alone.

Figure 13(b) repeats the above test assuming voltagescaling for two different values of λ, the maximum ratescaling factor allowed by DVS. In this case, we seethat the use of DVS significantly changes the savingscurve – the more aggressive voltage scaling allowsfor larger savings that can be maintained over a widerange of utilizations. Moreover, we see that once againthe savings from our practical algorithm (practRA)approach those from the optimal algorithm. Finally, as

expected, increasing the range of supported rates (λ)results in additional energy savings.

Energy savings from sleeping To model the energysavings with sleeping, we need to pin down the relativemagnitudes of the sleep mode power draw (ps) relativeto that when idle (pi). We do so by introducing aparameter γ and set:

ps = γpi(rn) (5)

where 0.0 ≤ γ ≤ 1.0. While the value of γ will dependon the hardware characteristics of the network elementin question, empirical data suggest that sleep modepower is typically a very small fraction of the idle-modepower consumption: ∼ 0.02 for network interfaces [13],0.001 for RFM radios [11], 0.3 for PC cards [11] andless than 0.1 for DRAM memory [8]. In our evaluationwe consider values of γ between 0 and 0.3.

With this, the energy consumption of an element thatspends time Ts in sleep is given by:

E = pa(rn)Ta + pi(rn)(Ti − Ts) + psTs. (6)

Our evaluation from Section 3 estimated Ts fordifferent scenarios. Figure 13(c) plots the correspondingoverall energy savings for different values of γ for ourpractB&B algorithm. We assume a transition timeδ =1ms, and a buffering interval B=10ms. Again, ourresults confirm that sleeping offers good overall energysavings and that, as expected, energy savings are directlyproportional to γ.

5.2 Comparison: Sleep vs. Rate Adaptation

We now compare the savings from sleeping vs. rateadaptation by varying the two defining axes of ourpower model: C, the percentage of power that does notscale with frequency, and β that determines the relativemagnitudes of idle to active power draws. We considertwo end-of-the-range values for each: C = 0.1 andC = 0.3 and β = 0.1 and β = 0.8. Combining thetwo gives us four test cases that span the spectrum ofhardware power profiles:

• C = 0.1 and β = 0.1: captures the case where thestatic portion of power consumption (that cannotbe rate-scaled away) is low and idle-mode power issignificantly lower than active-mode power.

• C = 0.1 and β = 0.8: the static portion of powerconsumption is low and idle-mode power is almostcomparable to active-mode power.

• C = 0.3 and β = 0.1: the static portion of powerconsumption is high; idle-mode power is significantlylower than that in active mode.

• C = 0.3 and β = 0.8: the static portion of powerconsumption is high; idle-mode power is almostcomparable to active-mode power.



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Figure 13: Total Energy Saving with Sleeping and Rate Adaptation

We evaluate energy savings for each of the abovescenarios for the case where the hardware supports DVSand when the hardware only supports frequency scaling.

With DVS: f(r) = O(r3) Figures 14 plots the overallenergy savings for practRA and practB&B for thedifferent test scenarios. These tests assume 10 uniformlydistributed rates and a sleep power ps = 0.1pi(rn).In each case, for both sleep and rate-adaptation, weconsider hardware parameters that reflect the best andworst case savings for the algorithm in question. ForpractRA, these parameters are λ (the range for voltagescaling) and δ (the transition time). For the best-caseresults these are λ = 5 and δ = 0.1ms; for the worstcase: λ = 2, δ = 1ms. The parameter for practB&Bis the transition time δ which we set as δ = 0.1ms (bestcase) and δ = 1ms (worst case).

The conclusion we draw from Figure 14 is that, ineach scenario there is a “boundary” utilization belowwhich sleeping offers greater savings, and above whichrate adaptation is preferable. Comparing across graphs,we see that the boundary utilization depends primarilyon the values of C and β, and only secondarily on thetransition time and other hardware parameters of thealgorithm. For example, the boundary utilization forC = 0.1 and β = 0.1 varies between approximately5-11% while at C = 0.3, β = 0.8 this boundaryutilization lies between 4% and 27%. We also evaluatedsavings under different traffic characteristics (CBR,Pareto) and found that the burstiness of traffic has amore secondary effect on the boundary utilization.

For further insight on what determines the boundaryutilization, we consider the scenario of a single idealizedlink. The sleep-mode energy consumption of such anidealized link can be viewed as:

Esleep = pa(rmax)µT + ps(1 − µ)T (7)

Similarly, the idealized link with rate adaptation is onethat runs with an average rate of µrmax for an energyconsumption of:

Erate = pa(µrmax)T (8)

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Figure 14: Comparison of energy savings between sleepand rate adaptation. Support for dynamic voltage scaling.

Figure 15 represents the boundary utilization for thisidealized link as a function of C. In this idealizedscenario, the dominant parameter is C because the linkis never idle and therefore β has only a small, indirecteffect on ps. The gray zone in the figure represents thespread in boundary utilization obtained by varying βbetween 0.1 and 0.9.

With frequency scaling alone: f(r) = O(r) Fig-ures 16 plots the overall energy savings for practRAand practB&B for the different test scenarios in themore pessimistic scenario where voltage scaling is notsupported. Due to lack of space, we only plot the com-parison for the first two test scenarios where C = 0.1;at C = 0.3, the savings show a similar scaling trend butwith significantly poorer performance for rate-adaptation


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Figure 15: Sleeping vs. rate-adaptation

and hence add little additional information.The primary observation is that the savings from rate

adaptation are significantly lower than in the previouscase with DVS and, in this case, sleeping outperformsrate adaptation more frequently. We also see that –unlike the DVS case – network utilization impactsenergy savings in a similar manner for both sleeping andrate-adaptation (i.e., the overall “slope” of the savings-vs-utilization curves is similar with both sleeping andrate-adaptation while they were dramatically differentwith DVS – see Fig. 14).

Once again, we obtain insight on this by studying thethe highly simplified case of a single idealized link. Forthis idealized scenario with f(r) = O(r), we find thatthe boundary condition that determines whether to usesleep or rate adaptation is in fact independent of networkutilization. Instead, one can show that sleep is superiorto rate-adaptation if the following inequality holds:

c >γβ

1 − γ(1 − β)(9)

Otherwise, rate adaptation is superior.In practice, network utilization does play a role

(as our results clearly indicate) because the variouspractical constraints due to delay bounds and transitiontimes prevent our algorithms from fully exploiting allopportunities to sleep or change rates.

In summary, we find that both sleeping and rate-adaptation are useful, with the tradeoff between themdepending primarily on the power profile of hardware ca-pabilities and network utilization. Results such as thosepresented here can guide operators in deciding how tobest run their networks. For example, an operator mightchoose to run the network with rate adaptation during theday and sleeping at night based on where the boundaryutilization intersects diurnal behavior, or identify com-ponents of the network with consistently low (or high)utilization to be run with sleeping (or rate-adaptation).

6 Related WorkThere is a large body of work on power management incontexts complementary to ours. This includes powerprovisioning and load balancing in data centers[6, 9],

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Figure 16: Energy savings of sleep vs. rate adaptation,β = 0.1, frequency scaling alone.

and OS techniques to extend battery lifetimes inmobiles [10, 30].

Perhaps the first to draw attention to the problemof saving overall energy in the network was an earlyposition paper by Gupta et al. [12]. They use data fromthe US Department of Commerce to detail the growthin network energy consumption and argue the case forenergy-saving network protocols, including the possi-bility of wake-on-arrival in wired routers. In follow-onwork they evaluate the application of opportunisticsleeping in a campus LAN environment [21, 11].

Other recent work looks at powering-down redundantaccess points (APs) in enterprise wireless networks [17].The authors propose that a central server collect APconnectivity and utilization information to determinewhich APs can be safely powered down. This approachis less applicable to wired networks that exhibit muchless redundancy.

Sleeping has also been explored in the context of802.11 to save client power, e.g., see [2]. The 802.11standard itself includes two schemes (Power-Save Polland Automatic Power Save Delivery) by which accesspoints may buffer packets so that clients may sleep forshort intervals. In some sense, our proposal for bunchingtraffic to improve sleep opportunities can be viewed asextending this idea deep into the network.

Finally, the IEEE Energy Efficient Ethernet TaskForce has recently started to explore both sleeping andrate adaptation for energy savings. Some initial studiesconsider individual links and are based on synthetictraffic and infinite buffers [4].

In the domain of sensor networks, there have beennumerous efforts to design energy efficient protocols.Approaches investigated include putting nodes to sleepusing TDMA-like techniques to coordinate transmis-sion and idle times (e.g., FPS [14]), and distributedalgorithms for sleeping (e.g.,S-MAC [28]). This contextdiffers from ours in many ways.


7 Conclusion

We have argued that power management states that slowdown links and put components to sleep stand to savemuch of the present energy expenditure of networks.At a high-level, this is apparent from the facts thatwhile network energy consumption is growing networkscontinue to operate at low average utilizations. Wepresent the design and evaluation of simple power man-agement algorithms that exploit these states for energyconservation and show that – with the right hardwaresupport – there is the potential for saving much energywith a small and bounded impact on performance, e.g., afew milliseconds of delay. We hope these preliminaryresults will encourage the development of hardwaresupport for power saving as well as algorithms that usethem more effectively to realize greater savings.

Aknowledgments

We thank Robert Hays, Bob Grow, Bruce Nordman,Rabin Patra and Ioan Bejenaru for their suggestions. Wealso thank the anonymous reviewers and our shepherdJon Crowcroft for their useful feedback.

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Notes1In reality the energy savings using rate-adaptation will

depend on the distribution of operating rates over time and thecorresponding power consumption at each rate. For simplicity,we initially use the average rate of operation as an indirectmeasure of savings in Section 4 and then consider the completedistribution of operating rates in Section 5 when we computeenergy savings.


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