White Paper
Managing variable data center rack densities
White Paper STI-100-015
March 2015
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Thereis no shortage of predictionsregarding thefuture of datacenter power densities, buttheirtrack-record overthelast decade has been poor.Atthe same time, there isalso a generalinconsistencyin theway different groups define and discussthe topic of the high-density datacenter.This paperwilltouch on these predictionsand definitions.Following that,itwilltiethe density discussion into the other main datacenter goals:efficiency,capacity planning,and uptime.Finally,itwill providea bit of guidanceforcutting through thefog whereitcomesto how powerand equipment densitiesvarywithin a datacenterand for planning howto handlethevarying densitiesatthe datacenterrack.
Overview
Current State of Data Center Density
In thefirstyears ofthe 21stcentury,the datacenterwas most often a mishmash of old mainframes, networking racks,and various server/storagecabinetswith no pre-designed structure.Power densityattherack was notatopic of discussion as mostwere in the 1-3kWrange and the power and cooling infrastructure were overbuilt for maximum uptime (see Figure 1). By 2005, the typical datacenter managerwasexposed to increasing rack power dueto newtechnology,especially bladeservers.Yet,the new
5kWracksstill proved to be onlya minorchallenge to the power distribution design with some moving to 3-phasecircuits,and
a more considerable challenge to the cooling design, bringing about the need to pre-design the data center for hot-aisle /
cold-aisleconfiguration,watch for hot-spots,and provide for backup cooling.
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Theterm “High-Density” could referto any number ofthingsincluding moreequipmentin arack, moreracksin a datacenter, or
even more applications on aserver. Forthe purposes ofthis paper,we will concentrate mostly on the increasesin total power
utilization perrack and increasesin equipmentcount perrack,though wewillalso touch on other needsin the datacenter. Thethreshold for high power densitysetforth byStrategicDirectionsGroup (2014) and endorsed byAFCOM was 8kW perrack,
with an extreme density defined at >15kW perrack.Intel (2014) pushed thelimitswith 43kW perrackextending outto 1100
W/ft2 using custom-built narrowracksand free-aircooling atelevated temperatures.Thisleads usto theconclusion thattheterm “high-density” willalways berelative, no matterwhatanysingle group triesto peg itto.
Emerson NetworkPowerreported in fall 2009 thattheaverage power density oftheirsurveyed respondentswas 7.4 kW perrack. Theserespondentsexpected thisto growto 12 kW perrack by 2011 and 16.5 kW perrack by 2019.Strangely, bythefall 2014 survey, the average had dropped to 5.83 kW perrack and projection for two years hence had become 8.9 kW. Indeed, this semi-annualsurvey oftheDataCenterUsersGroup (DCUG) showsthatthe peakwas 7.7 kWin 2012 (seeFigure 2).Assuming thevariation through theyearsfrom 2006 to 2014 wassimply dueto varied respondents,itseemsthere has been no increasein
rack density on average.So why do these same datacenterskeep expecting the density to rise?And what might be keeping it stable?
High power densityis often linked to high equipment density.That isin general,the more equipmentyou installin arack,the more power must be delivered to therack.We haveseen thisto betruewith theimmediate popularity oftheServerTechnology® HDOT™ cabinetPDUwhich has becometheindustrystandard forrackequipment density.Itisimportantto understand thatthe link between high equipment densityand high power densityis onlyvaluablein someinstances.Forexample,comparing onerack to anotherwith similarequipment oranalyzing onerack evolving overtimewill demonstratethelink between thetwo forms of density.On the other hand,anysingleinstallcould be ofverylow powerequipmentwhich fullyloadstheRUspace in therack, orasmall number of bladechassis drawing much higher power.
Another driverin terms of both rack equipment densityand rack power density is increasesin the heights ofracks.Based on
recenttrends,the 42RUrackis no longerthestandard forlarge purpose-built datacenters. 45RUto 52RU have been adopted by many organizationsto relievethecost offloorspace.Byallowing more equipmentto be installed in arack,thetypicalaverage Now, a decade later,there remains the discussion of the potential forsudden rapid increasesin datacenter power density.Of
course,itisvery difficultto increase power densityin existing datacenters, butthecontinualcallforconsolidation isleaving this possibility open for new data centerconstruction.Additionally, the cooling strategies are becoming much more efficient and tuned for minimal needsratherthan being significantly overbuiltasin the past.
Figure 2: Average power density (in kW) per rack - Emerson DCUG data from 2006 to 2014
power densitywillincreasefrom previous builds.We may becoming upon thetimewherewestartseeing theDCUGrespondent’s projectionscometrue– higheraverage power densities mayfinally beat hand.
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Itisimportantto understand and differentiate between theaveragerack power densityand maximum rack power densityacrossa
datacenterfloor.Itisequallyimportantto understand and differentiate between power densitythatisaveraged overtimeand power
density peakwithin a period oftime.We mightcallthefirsttopic “spatial power densityvariation” wheretheaveragespatial power
densityis dependent upon size ofthe datacenterand is often tied to infrastructurecapacities,and the peakspatial power densityis dependent on individualcomponentswithin the system which istied to thespecific design aspects.Forexample,a 345 W/ft2 data center (based on whitespace) might bespecified bythefactthat 1 MW of powerand cooling isavailablefor use over 2900 ft2.Using theAFCOM standard rackarea of 25 ft2, 116 racksare deployed atan average powerload ofabout 8.6kWeach.
Thesecond topic might becalled “temporal power densityvariation” wheretheaveragetemporal power densityis dependent upon
regularapplication loads,and the peaktemporal power densityis dependent upon sporadicapplication loads.Forexample,any given
rack,POD,circuit, or datacenterwill havea peakallowable powerload.Within this,therewill bean average powerload overtimeat each level based on application.Continuing theexample ofthe 8.6kWracks:ifthe datacentersimply provisions 8.6kWforeach rack, thetotal peakload will neverexceed the datacenterallowable, butalso anysinglerackwill not beallowed to exceed 8.6kW.Thisis not optimalasexperienceshowsthatthe averagerack load overtime will be much lessthan the peak.This not onlyresultsin the
average load ofthe datacenter overtime being much lessthan theallowable, butalso the peakload ofthe datacenter overtime being much lessthan theallowableforthe datacenter.Thisis because onlyafewrackswill peakatany given time, notthewhole of the datacenter.Thefinalresult ofthis design isthatitisa great over-provision of power.
Itwould be possiblethen to saythattheaverage of 8.6kW perrackis useless unlessyou allowindividualracksto exceed the 8.6kW. Thus overprovisioning ofsome orallracksallowsthe overall datacenterto reach toward theallowable peak.Thestrategies used for
power overprovisioning and for maximizing usage to the limitsset by electricalcodesrequire continual monitoring during growth stagesand will befurtherconsidered laterin this paper.
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In SchneiderElectric’swhite paper (2014),Choosing theOptimalDataCenterPowerDensity,theconclusion is madethatexceeding
about 11kW per rack in design capacity has significantly diminishing returns on reductions in cost with additional design and operationalcomplication outweighing thosesmallcost benefits.Thisis, ofcourse,a generalization which assumesthatspacecostsare minimum.On the flip side,average rack heights have been increasing and 17kWrackPDUs have become more and more popular. Presumably,these deployments have been determined to be bestforthese organizations.
Figure 3: Cost per watt vs power density – Schneider Electric (2014)
The problem with many ofthese types ofanalysesisthattheyleave outthe mostimportantfactor.Thatis,they omitthe devices such asservers,storage,and network gearthatarethe powerload in the datacenter.The process ofarbitrarily dividing powerload between acertain number ofracksand then analyzing thecost ofinfrastructureand cooling does nottakeinto accounttheITtrans -actionstaking placewithin the datacenter.ThesetransactionsincludetheITrefresh cyclein which newerequipmentisinstalled to
Figure 4: Average PUE – Uptime Institute Surveys (Symposium 2014)
provide much better performance butwith a nontrivialincreasein powerconsumption.Theyalso includevariableapplication loading overtimethat may be driven bychangesin projects, personnel, or market growth.Theyeven include “green” goalssuch as driving PUE downward by pushing moreITload.These density-based ITtransactionswill berevisited laterin this paper.
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The discussion ofincreased power densityattherack does notliein avacuum.In fact,it may beacause oran effect ofvarious other
changes orimprovementsin the datacenter. Virtualization,consolidation,automation,cloud deployment, modularity,freecooling, big data, hot- orcold-aislecontainmentand any number of other meaningfulconceptsinterrelateto power densitythrough thetopics of efficiency,capacity planning,and uptime.
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Efficiency ofthe powersystem,including thecooling components has been the hottesttopicin the datacenterforthelastseveral years. Calculation of PUE is being performed by the majority of medium to large scale data centers today.To truly maintain an understanding oftheefficiency ofthe datacenter,the datacenter manager needsacontinuously operating monitoring system such
asServerTechnology’sSPM (SentryPower Manager).Through datatrending and reporting, power managementattherackPDU,and
rackenvironmental monitoring, datacenter personnelcan keep acloseeye on therelationship between efficiencyand power density. Figure 4 showsthatthe average PUEin the datacenter hasleveled off overthelastfewyears.Adoption rate ofPUE measurement
Quite often, the mantra that consolidation and virtualization leads to better efficiency is driving the datacenterto higher power
densities.If arack has been loaded with equipment such that it is already pushing the allowable powerload, virtualization that increases utilization willalso reducethe number of devicesthatwillfitwithin the powerlimits oftherack.This may be moreefficient in thesensethatless powerwill be used to perform thesamejob, butthesparser distribution ofequipment mayreducetheefficiency ofthecooling system.Determining the overalllevel ofthe benefit ofthischangecan only beachieved through measurement. One ofthe hot topicsin efficiencyisthe ideathatcooling costsarereduced byallowing temperatureto beincreased.This may be independent orcoincidewith increasing rack power density.Higherrack power densities directlyincreasethetotal heatload per unit volume ofthe datacenter.Allowing theinlettemperaturesto increaseatthesametime hasthe potentialto dramaticallyreducethe faultresponsetime.This pushesthe datacenter managerto optfor morecomplete monitoring oftemperaturealltheway down into therack,and for morelevels ofcontrolincluding outlet management using Switched cabinetPDUs.
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DCIM and othercapacity planning tools havetaken hold in many datacenterstoday.Effective use oftheavailable powerinfrastructure
and building in a modularfashion haveled to the datacenter operating closerto peak powercapacityatany given time.One possible
way to increase capacity in an existing data center is to increase the rack power density. Of course, the power and cooling infrastructure throughoutthe datacenterwould typically need to be upgraded to allowforthis.Keeping track ofthe percentage of
capacity used asit increases overtime isimportantto properlytime these types of upgrades, or of modularexpansion ofthe data center. Toolssuch asSPM can providea meansto monitor direct measurements overtime aswellas providing a meansto predict
when capacitywill hititslimitsin thefuturevia predictivetrending.Thiscan beaccomplished atevery power distribution pointwithin the data center from UPS down to the outlets of the cabinet PDUs, not only for capacity predictions, but also as guidance for
deployment of newequipmentwithin theestablished powerlimits.Similarly,keeping track ofthefree physicalRUspacein thecabinets
within the datacentercan betracked using thesame monitoring tools.
Understanding the curve of power density growth is critical to the further planning of data center deploymentsand application installations.Bywatching trends of power usage cycles over days,weeks, months and beyond, expansions ofthe infrastructure to
allowfor power densityincreasescan be executed in acontrolled manner.Finally, this predictive method can be extended to the financialside ofthe businessforlong-term projectionsregarding needsforfuture datacenters,including guidance for deciding the design-to power densities.
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Thetop priority of mostenterprise datacentersremains uptime.Increased power densitycan put pressure on the powerredundancy
conditionsin the datacenterwith toolssuch asSPM.Though this maystill bestrictlytrueforsome datacenters,it has been quite differentfor many others.With theincreased power density dueto consolidationsand virtualization,additional dataand network
redundancy has been implemented.This additional redundancy, in some cases, means that the uptime requirement for any particularrackis greatlyreduced.In someverylarge organizations,whole datacenterscan go down withoutaffecting the business,
and aretherefore designed with lowlevels ofredundancy (e.g.UptimeInstituteTier 2 instead of 3 or 4).Theseareveryinteresting
cases, butfortherest of us,thecap-ex ofcreating such redundancyis hard to come by.
Thetypicalenterprise datacenteris builtwith powerredundancy delivered from thesource power plantsalltheway down to the
rack devicesthrough the use ofa pair ofcabinetPDUs.Ifany one ofthe distribution pointsfailsto handletheloss of powerto the
redundant partner,significant downtimeislikelyto occur.This makesiteven moreimportantto havea monitoring system to keep track ofeach “side” ofthe powerchain and thecombined loads per phase along theway.Featuressuch astheCircuit modeling
and CabinetRedundancyintegrated into SPM help datacenter personnel maintain awareness ofthis as powerand equipment densitiesincrease overtime.
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Taking into accountthe goalsin theenterprise datacenterwe have discussed such asefficiency,capacity planning, uptime,and power densityalong with the currentstate oftheindustryand theexpectation offuturetechnology,wecome to the question of
whatcan and should be donein a new datacenter design.Whatisthe optimal densityto provision forand whatshould be the expectation ofvariationsin actual power usageacrossthe datacenterand overtime?
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According to Schneider’s data, oversizing racksto handle peakto average powerratio of 2:1 comesat< 1%cost premium to the full datacenterwhen keeping all upstream distribution builtto thespatialaverage power density, oversizing a group ofracks or
PODtied to alargePDUcomesata 4%cost premium to thefull datacenter,and oversizing thefull datacentercomesata 20%+
cost premium.Theirrecommendation isto oversizetheracksaslong asthereare proceduresto prevent overloading of upstream systems.Asthey definethisstrategy, “RackPDUs, breaker(s),cablessized to handle expected peak densityin anyrackwithin the pod, butthe pod will notexceed theaverage overall.”
Some may objectto thestrategywith theconcern thatany particularindividualrackcould beloaded such thatitwill besafewhile pushing a pod overthesafelimits.Thisspeaksto thelogistics ofthe deployment process.In fact,thisstrategyisstandard practice
attherackPDUlevelalready.Casein point:a 30Asingle-phase powerstrip in North Americaisrated at 24Acontinuous usage, but itwill be designed to havetwo (2) branches protected by 20Acircuit breakers orfuses.An installercould easilytrip the panel breaker
withouttripping theinternal branch circuit protection.Thisallcomes down to risktolerancevslogisticalcontrol,which isatopicI
Table 1: Standard circuit capacities for data center racks – 3-phase circuits must be balanced
Thisiswherewe need to beveryclear on the definition of “average”.Theconsequences of high usageattheracklevel oversubscribing the pod may bevery damaging to the business.The pod muststill be designed to handlethe peakloading expected atany given time. Let’sassume a pod of 20 racksis designed to handle a peak loading of 100kWwhich givesaspatialaverage loading perrack at temporalloading peak of 5kW.Thiscorrespondsto theNorth American safetyrating ofa 208V/24Acircuit.Itis useful hereto takea
tangentto recognizethatthe 100kW pod must be built to handle 125kW pertheNECin the USA,so weshould onlyanalyze this eitherfrom thefull maximum rating standpoint orfrom thesafetyrating standpoint.Forconsistency,wewill usethesafetyrating.If theracksin this pod are deployed with the 208V/24Acircuit,each rack mustalways be belowthisvalue.Itisa good timeto notethat
rack power distribution units do notlie on acontinuum of powercapacities.Thereare discretevalues based on standard connections forvarious parts oftheworld.
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Variation in powerload acrossthe datacenteris onlythe beginning ofthe design plan.An understanding of power usage overtime based on application load variabilityis needed to minimize the oversizing of power distribution components.Load balancing and batch processscheduling will help keep thetemporalvariation in power densityfrom exceeding the peakallowableload.Wewillfind in practicethatthetemporalaverage power usage oftherack,and thusthe pod will becloserto 50% ofthe design max, or 2.5kW
perrackand 50kW per pod.Ofcourse,thisisthesaferoute, butitis notthe mostefficient use ofresources.Instead,iftheracksare
all deployed with 208V/24A 3-phasewhich provide 8.6kWand thoseracksloaded with 4.3kW on atemporalaverage,the pod will see 86kWaverage overtime.Thisallowsanythree ofthetwentyracksto doubletheirload oralltwentyracksto increase load by 16%,whilethe pod remains underthe 100kWrating.
Figure 5 shows howa particularserver mightvaryits power draw based on itsCPU utilization.This utilization is directlytied to appli -cation processesand showsthat anysingle servercould see an increase in power draw between atypicalload state of 30%and “full” utilization of 80%.Also shown isthe maximum number ofservers ofthistype thatcan be racked on a 30A 3-phase 208V
Figure 5: Power per server and Servers per rack vs. CPU usage – 30A 3-phase 208V circuit.
Figure 6 showsthe effect of Virtualization using an examplethat peaksatan 8:1 ratio using thesameserver modelasabove. Herethevirtualization causesthe utilization to rise upwardstoward 80%.Thetraceshowsthe effect on the percentage ofthe number ofracksto obtain thesame performanceasthereferenceserversrunning at 30% utilization.Asthe utilization increases to 80%through theaddition of morevirtualservers,the number ofracksfull ofserversreducesto 18% ofthereference design.
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Planning forthefuture ofthe datacenteris difficult but must bea part ofthe density discussion.With modular deployment being
alltherage,itisimportantto plan forthe process of growth.Thestrategy of oversizing theracksallows oneto continuethe modu
-larityatthe pod level.Thatis,a proper modular datacenterconstruction willallowadditions of podswith theircorresponding electricaland mechanicalinfrastructure aslong as physicalconstraintssuch asfloorspace and weightlimitsallow.Thetrickier
part ofthis prognostication istheequipmentrefresh cycle.Atypicalrefresh ofcomputing servers on athree-yearcycle mightsee
Figure 6: Effect of virtualization in regards to consolidation.
the powerefficiencystandpoint,thisisa no-brainer.Theconsequencethough isthattheracksin theexamplewillseetheseincreases
and we alreadyknowthatwecan onlyabsorb a 16%increasein overallload in the example given.Thisresultsin lowerserver/rack densitiesand thusreducesthe overall performance boost ofthe refresh at the datacenterlevel.Ifthis datacenterisexpected to operatefor 5 refresh cycles (15 years),itwillsee powerloading increaseto 3xits originalload foritsservers (much lessforits network
and storage gear) for 1024x performance boost.Even if only half ofthe equipment are servers,there will probably be double the originaltotal datacenter powerload ifspaceis notsacrificed.
Figure 7 showsthe effect ofserverrefresh cycles overtimewhen the goalisto keep theracksfull ofequipment.This providesfull
accessto the performanceimprovements overtime, butforcesan infrastructure design thatallowsfor doublethe powerloading,as previously mentioned.This method is difficultto achievein practice,though it does provideforconsistencyin thecooling system,thus keeping PUE minimized.
Figure 7: Refresh cycle effects over data center life – constant space utilization
Figure 8 shows the effect ofserverrefresh cycles overtime when the goal isto keep power usage constant.In otherwords,the infrastructureis designed and loaded to the maximum from the outset.Theresult ofthisstrategyisthatspace utilization falls overthe life ofthe datacenterand performanceis onlyabout half oftheFigure 8 caseafter 15 years.
Thisleadsto thecrux ofthe density question and the factthatthere is not onerightanswer.Thestrategy ofsimply oversizing rack PDUsto allowfor high densityracks or moments of high power usage does not providesufficientlong-term results.Atthesametime, designing foraverylong-term potential power density has been shown to beattheexpense of near-term cap-exand op-ex.The best solution probablyliessomewherein the middle.Oversizing the power distribution atthePODleveland building allinfrastructurein a
Figure 8: Refresh cycle effects over data center life – constant power utilization
Summary
The datashowsthat, overthelast decade,actual datacenter operators have greatly overestimated theexpected increasesin power
density.The expectation seems reasonable from the standpoint that server performance perwatt and watts perRU continue to increaseexponentially. The datacenter; however,seemsto have been stuckin the 5-8kW perrackrange.Thereason maysimply be thatthe old datacenterscontinueto operatewith thesameinfrastructurewithout upgrades.In thiscase,racksactually becomeless densein terms oftheirequipmentcount.
This bottleneck may be about to open up with newer datacenters built on modular platformsand consolidation initiatives driving enterprises out oftheir old infrastructure.Indeed,the 52RU 17kW perrack datacenteris becoming moreand morecommon.Because ofthisincreasein powerand equipment density,continual monitoring of powerand environmentalconditionsthrough DCIM products such astheSPM byServerTechnology has becomeever moreimportantto align with the goals ofefficiency,capacity planning,and uptime.
With the understanding ofthevariation thatcan occurfrom rackto rack,from changesin application loading within theracks,and from ITrefresh cycles,itisclearthatthe question ofthe optimal design-to power density does not have merely onerightanswer.Itis importantto modeltheexpectationsin physicalequipment densityin therackasrefresh cycleschangethe power density per device. Thisislikelyto lead to an initial deploymentthat maximizesthe devicecountin therackwhich willresultin the need for high outlet densitycabinetPDUssuch astheHDOT byServerTechnology. Asrefresh cyclesreducethe devicecountwithin theracks, overload risks of higher power equipment can be mediated and 3-phase load balancing can be easily maintained with color-coded Alt-Phase Switched outletsavailable on thesecabinetPDUs.
References
1. IDC 2007. John Humphreys, Jed Scaramella, Enterprise Platforms Group.The Impact of Power and Cooling on Data Center
Infrastructure.
2.StrategicDirectionsGroup PtyLtd. 2014.DataCenterStandards–DataCenterSizeand Density Version 3.0.Level 9, 145 Sinnath -ambyBlvd,Springfield,Qld 4300,Australia.ABN 58 103 746 872.
3.Intel 2014.John Musilli,Paul Vaccaro.IntelITRedefinestheHigh-DensityDataCenter: 1,100 Watts/Sq Ft.
4.Emerson NetworkPower 2009.The FourTrendsDriving the Future ofDataCenterInfrastructureDesign and Management. 1050
Dearborn Drive,POBox 29186,Columbus,Ohio 43229.WP167-39,SL-24637
5.Emerson NetworkPower 2013.Integrating People,Processand Technologyto Transform DataCenterOperationsand Performance. 1050 Dearborn Drive,POBox 29186,Columbus,Ohio 43229.SL-25674
6.Emerson NetworkPower 2014.Fall 2014 DataCenterUsers’Group SurveyResults–October 2014.DataCenterUsers’Group. 7.SchneiderElectric 2014.Choosing theOptimalDataCenterPowerDensity.WhitePaper 156.
8.UptimeInstitute 2014.Symposium DataCenterSurveyResults.
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