Drs Performance Best Practices Wp

8/7/2019 Drs Performance Best Practices Wp

1/19

Performance Study

Copyright 2008 VMware, Inc. All rights reserved. 1

DRS Performance and Best PracticesVMware Infrastructure 3

VMwareInfrastructure3providesasetofdistributedinfrastructureservicesthatmaketheentireIT

environmentmoreserviceable,available,andefficient.WorkingwithVMwareESX3,VMwareVirtualCenter

2,andVMwareVMotion,VMwareDistributedResourceScheduler(DRS)dynamicallyallocatesresourcesto

enforceresourcemanagementpolicieswhilebalancingresourceusageacrossmultipleESXhosts.

ThispaperisintendedforreaderswhounderstandthearchitectureandbasicconceptsofDRS.Seethepaper

ResourceManagementwithVMwareDRSforthesedetails.(SeeResourcesonpage 19foralink.)This

paperidentifiesvariousscenariosinwhichyoucanbenefitfromDRSandexplainshowtoconfigureyour

environmenttotakebestadvantageofDRS.

Thepapercoversthefollowingtopics:

OverviewofDRSPerformanceonpage 1

ResourceAllocationforVirtualMachinesandResourcePoolsonpage 3

ScalingtheNumberofHostsandVirtualMachinesonpage 9

DRSAggressivenessonpage 13

IntervalBetweenDRSInvocationsonpage 14

CostBenefitAnalysisonpage 15

HeterogeneousHostEnvironmentsonpage 15

ImpactofIdleVirtualMachinesonpage 16

DRSPerformanceBestPracticesonpage 16

MonitoringDRSClusterHealthonpage 17

Conclusionsonpage 19

Resourcesonpage 19

Overview of DRS PerformanceVMwareDistributedResourceSchedulerimprovesresourceallocationacrossallhostsandresourcepoolsin

acluster.WhenyouenableaclusterforDRS,VirtualCentercontinuouslymonitorsthedistributionofCPUand

memoryresourceusageforallhostsandvirtualmachinesinthatcluster.DRScomparesthesemetricstoideal

resourceutilizationthatis,thevirtualmachinesentitlements.Theseentitlementsaredeterminedbasedon

theresourcepoliciesoftheresourcepoolsandvirtualmachinesintheclusterandtheircurrentdemands.

VirtualCenterusesthisanalysistoperforminitialplacementofvirtualmachines,virtualmachinemigration

forloadbalancing,enforcementofrulesandpolicies,anddistributedpowermanagement,ifdistributed

powermanagementisenabled.

ThisstudyfocusesonunderstandingtheeffectivenessandscalabilityofDRSalgorithms.


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DRS Performance and Best Practices

Effectiveness of DRS Algorithms

OurgoalinthesetestswastoseeifDRSiseffectiveinimprovingperformancewhenthereisresource

contentionandifDRScanallocateresourceswhilebalancingloadinproportiontotheresourcepolicies

allocatedtovirtualmachines.WeevaluatedvariousconfigurationsoftheDRSalgorithmandcomparedhow

effectivetheyareinvarioususecases.

Weexaminedwhethertheshares,reservations,andlimitsofavirtualmachineorresourcepoolareeffectively

enforced

by

DRS

and

how

the

migrations

recommended

by

DRS

affect

the

performance

of

the

affected

cluster

asawhole.Weusedvirtualmachinethroughput(orthethroughputofmultiplevirtualmachines)asa

measureofhoweffectiveDRSis,inthepresenceofvariousworkloadswithvariouspolicysettings.

TheloadsshouldachievethroughputthatisapproximatelyproportionaltotheirCPUandmemory

entitlements.TheseresourceentitlementsareameasureofhowmanyCPUcyclesandhowmuchmemorythe

virtualmachineorresourcepoolshouldreceiveandarecalculatedbasedontheshares,reservations,and

limitstheresourcepoliciessetinVirtualCenterandtheestimatedCPUandmemoryresourcesdemanded

bythevirtualmachineorresourcepool.Byplacingormigratingthevirtualmachineseffectivelybasedon

theseentitlements,DRSensuresthatthevirtualmachineorresourcepoolgetswhatitdeserveswithout

violatingtheshares,reservationsandlimitpolicy.

Scalability of DRS Algorithms

DRSsupportsacertainnumberofhostsandvirtualmachinesinaDRScluster.Thegoalinourstudywasto

seehowDRSscaledasweincreasedthenumberofhostsandvirtualmachines.Asthenumberofvirtual

machinesandhostincreases,thepossibilitiesforbalancingwithvirtualmachinemigrationsalsoincrease.On

theotherhand,eachbalancingmigrationdoeshavearesourceoverhead.Thismighteffectivelyimproveor

worsenclusterthroughputaswescaledtheenvironmenttoincludemorehostsandvirtualmachines.

Furthermore,throughputimprovementdependsheavilyonthecharacteristicsoftheworkloadforexample,

placementofthevirtualmachinesandvariabilityofworkloads.Weexploredanumberofdifferentworkloads,

atvariousscales,toseehowDRShandledeachofthem.

Dimensions for DRS Performance Analysis

TobetterunderstandtheperformanceofDRS,wecharacterizedDRSalongseveraldimensions.These

dimensionsinclude:

Resourceallocationforvirtualmachinesandresourcepools

Numberofhostsandvirtualmachines

Degreeofaggressiveness

DRSperiodicfrequency

Heterogeneoushostenvironments

CostbenefitanalysisofVMotion

Impactofidlevirtualmachines


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Resource Allocation for Virtual Machines and Resource Pools

DRSensuresthatresourcepoliciesaresatisfiedandbalancesloadacrossaclusterwhenappropriate.Inorder

toevaluateitsperformanceimpact,weusedthroughputofvariousworkloadsasameansofcomparisonin

variouscaseswitharealisticload.DRSalgorithmsshouldresponddynamicallytotheresourceallocation

changesforvirtualmachinesandresourcepools.Wesetupseveralexperimentstoverifythisbehaviorand

evaluateDRSeffectiveness,seekingtoanswerthefollowingquestions:

Can

DRS

automatically

reduce

the

load

on

an

overcommitted

host

when

additional

resources

are

added

oravailableintheclusterandconsequentlyalsoimprovethethroughputfortheworkloads?

HowdoesDRSrespondtoahighlyimbalancedcluster?HowdoesDRSaggressivenessaffectthe

placementofvirtualmachines?

CanDRSeffectivelyenforceresourceallocationpoliciesacrosshostsinaDRSclusterbysettingdifferent

sharevaluesforindividualvirtualmachines?

Test Methodology

Thissectiondescribesthetypesofbenchmarkworkloadsused,resourcesdemanded,andmetricsusedtotest

theimpactofresourceallocationonDRS.

We

setup

a

homogenous

cluster

of

hosts,

each

configured

with

two

2.8GHz

Intel

Xeon

CPUs

with

hyperthreadingenabled.Eachhosthad4GBofmemory.WeusedadedicatedGigabitEthernetnetworkfor

VMotionandconfiguredDRStorunatthedefaultmigrationthreshold.

Forthisfirstsetoftests,weusedbenchmarkstoreflectrealisticworkloads.Thestableperiodfortheseloads

wasanhourduringwhichwetookmeasurements.Table1liststheseworkloads.

Test Results

ToverifythatDRScanautomaticallyreducetheloadsonanovercommittedhost,weranthefiveworkloads

showninTable1,eachinitsownvirtualmachine,ononehost.Allvirtualmachineshadequalsharesandno

reservationsorlimits.Figure1showsthiscase.Eachcirclerepresentsonevirtualmachine,withdifferent

lettersrepresentingthedifferentworkloads,andtheboxesrepresentingseparatephysicalhosts.Thesizeof

eachcirclerepresentshowmanysharesaregiventothatvirtualmachine,nottheCPUormemoryresource

demandsofthevirtualmachine.DetailsontheresourcedemandsarelistedinTable1.

Tests for Load Balancing with Increase in Available Resources

WeusedamixofvirtualmachinesthatdemandedmoreCPUresourcesthanwereavailableonthehoston

whichweplacedthem.Wemeasuredthethroughputforeachvirtualmachineasweaddedorpoweredona

newhostinthecluster.

Table 1. Workloads for Resource Allocation Tests

Virtual Machine Workload Benchmark Resource Demand Metric

Databaseserver Swingbench CPU:850MHzMemory:700MB

Transactions/sec

Webserver SPECweb2005 NetworkI/O:30Mb/sCPU:1000MHz

Accesses/sec

Fileserver Dbench DiskI/O:120Mb/sCPU:550MHz

MB/sec

Javaserver SPECjbb2005 Memory:1.7GBCPU:550MHz

Neworders/sec

Idlevirtualmachine None None None


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ThevirtualmachineworkloadsinFigure1andsubsequentdiagramsareidentifiedusingthefollowingkey:

Ddatabaseserver

Ffileserver

Iidle

JJavaapplicationserver

WWebserver

Figure 1. Initial Workload Distribution with DRS Disabled

Figure 2. Workload Distribution with DRS Enabled

WhenweenabledDRS,itmovedsomeoftheloadontheovercommittedhosttotheidlehostasshownin

Figure2.Usingthedefaultmigrationthreshold(moderate),DRSimprovedoverallsystemthroughput

(geometricmeanoftheindividualthroughputimprovements)by27percent.Theseresultsconfirmthatsimple

balancingprovidedbyDRScanautomaticallybringsignificantimprovementsinthroughputwithouttheneedtoplacevirtualmachinesmanually.

CPUintensiveloadsimprovedsignificantly,becausesystemCPUresourceswereovercommittedbeforewe

enabledDRS.Figure3showsthenormalizedthroughputimprovementforindividualvirtualmachinesused

inthistest.Thedatabaseserverthroughputimprovedby25percentbecausemoreCPUresourceswere

availableafterweenabledDRS.WebserverthroughputimprovedsignificantlyafterweenabledDRS.Because

theWebserverisanetworkintensiveload,itbenefitedfromDRSbecausethenetworkI/Ocausesincreased

CPUload.Ontheotherhand,theJavaapplicationserverthroughputremainedthesame,becausetheJava

serverworkloadismemoryintensive,andthesystemdidnotexperienceanymemorycontentionduringthe

test.Wedidnotuseafileserverworkloadforthistest,becausefileserverdemandsarelativelylowlevelof

CPUresources.Instead,weusedasecondinstanceoftheWebserverworkloadforthistest,whichrequires

moreCPUresourcesthanthefileserverworkloaddoes.

D W

J

IW

D

I

W

J

W


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Figure 3. Throughput Normalized to Baseline Individual Workload Throughput

Tests for Robust Response to Highly Imbalanced Cluster

TofurthervalidatetherobustnessoftheDRSalgorithms,wethenran10workloads,twoofeachoftheloads

describedinTable1,ontwoseparateESXhosts.Onceagain,wegaveallvirtualmachinesequalsharesandno

reservationsorlimits.Figure4depictsthescenariosthatweattemptedtovalidateduringthistest.

Figure 4. Distribution of Workloads When DRS Aggressiveness Is Adjusted

Scenario1showsthebaselineorbalancedthroughputconfiguration.Wechosethisasthebalanced

arrangementbecauseputtingoneofeachvirtualmachineperhostbalancestheCPU,memory,andI/O

demandsevenlywhileignoringlocalityorcachingeffect.WeusedthisasourbaselineandweexpectedDRS

toachieveathroughputclosetothis.

Scenario2depictstheworstpossibleconfiguration.WestartwiththisconfigurationtoseehowDRSbalances

theloadofthesevirtualmachines.

Weperformedthetestusingdefault(moderate)andaggressivemigrationthresholdsettingstotestwhether

theresourceallocationthatDRSgeneratescanmatchthebaselineconfiguration.Theworstpossible

configurationcouldnotbesustainedononehostwithoutfailuresinthebenchmarkmetricsbecauseof

resourceconstraints.SoweinsertedadelaybetweenthestartofonevirtualmachineandthenexttoallowDRS

tobalanceresourcesasweaddedeachnewloadtothefirsthost.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

DB server Java server Web server 1 Web server 2

DRS disabled DRS enabled

D W

J

IF

Scenario 2 DRS enabled (before)Scenario 1 baseline (optimal)

D W

J

IF

D W

J

IF

D W

J

FS

D W

J

IF

Scenario 2b aggressive DRS (after)

DD W

J

W

J

IF

Scenario 2a moderate DRS (after)


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OurresultsshowedthatthevirtualmachineplacementaswellassystemthroughputwithDRSwasveryclose

tothatofthebaselineconfiguration.Theoverallsystemthroughputwas95percentofthatofthebaseline

configurationfortheDRSconfigurationwithamoderatethresholdand99percentofthatofthebaseline

configurationwiththeaggressivethresholdsetting.Thisincludestheimpactonthroughputcausedbythe

virtualmachinemigrationsneededtogettothefinalstateofthecluster.Becausetheloadswereconstant,the

finalstatewasachievedquicklyaftertheloadsreachedstablestateandnofurthermigrationsweremade.

Forthetestwiththedefaultthresholdsetting(moderate),DRSperformedfourmigrationsthatis,itmoved

oneWebserver,oneJavaserver,andthetwodatabaseservers.

Thetestusingtheaggressivethresholdyieldedanalmostbaselineconfiguration.Thistestperformedmore

migrationsatotalofeighttogettothisstate.ThenumberofmigrationsreflectsthefactthatDRSload

balancingoccurswhiletheloadisrampingup,andhenceinthemoreaggressivecase,somemigrationswere

revertedafterallloadsreachedsteadystate.Theidlevirtualmachinewasnotmigratedbecausethecostof

migratingthevirtualmachinecouldnotbejustified,giventhattherewasnoload.Becausetheloadsare

relativelystableovertime,weobtainedbetterthroughputwithanaggressivethreshold.However,thedefault

settinginDRSismoderateinordertoaccommodatechangingworkloads.

Figure5comparesthesystemthroughputforindividualworkloadsinthebaselinecase,usingDRSwitha

moderatethreshold,andusingDRSwithanaggressivethreshold.Wenormalizedallthroughputresultstothe

baselinecaseforeasiercomparisonandmeasuredtheresultsoverastableperiodofanhour.

Inthemoderatecase,allindividualthroughputsarewithin80percentofbaselineandoverallwithin95

percent(geometricmeanofalltherelativethroughputs)ofbaseline.ThisistruedespitethefactsthatDRShas

anopportunitytorebalanceonlyonceeveryfiveminutes(default)andthatitisaffectedbyVMotionoverhead.

WhenweusedDRSwithamoderatethreshold,thedatabaseserverswerethemostaffectedbecauseDRS

placedbothdatabasevirtualmachinesonthesamehost.Migratingoneofthedatabaseloadstoadifferenthost

wasnotjustifiedunderthemoderatethresholdbecausetheimbalanceacrosshostswasnotgreatenoughto

justifyit.Someworkloadsachievedbetterthroughputthanthebaselinebecauseadditionalresourceswere

availableonthehostonwhichtheworkloadwasrunning.

Inthemoreaggressivecase,theresultsaredifferent.Becauseeachworkloadwasfairlyconstant,overtime

DRSaggressiverebalancingproducedbetterthroughput.

Iftheloadsvary,DRSwithamoderatethresholdmayachievebetterthroughputinthelongrunbecauseit

would

recommend

fewer

migrations

and

hence

incur

a

lower

VMotion

cost

compared

to

DRS

with

an

aggressivethreshold.

Whenweincreasedthenumberofhosts,asdescribedinScalingtheNumberofHostsandVirtualMachines

onpage 9,DRSwithamoderatethresholdachievedthroughputcomparabletothatofDRSwithanaggressive

thresholdanddidsowithfewermigrations.

Figure 5. Relative Change in Throughput of Workloads

0.0

0.2

0.4

0.6

0.8

1.0

1.2

File server 1 File server 2 DB server 1 DB server 2 Java server 1 Java server 2 Web server 1 Web server 2

Baseline DRS moderate (default) DRS aggressive


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Enforcing Resource Allocation Policies

InaVMwareInfrastructure3environment,youcanallocateresourcesusingabsolutememoryandCPU

bounds(reservationandlimit),proportionalvalues(shares),oracombinationofthetwoforthevirtual

machinesandresourcepoolsinthecluster.Areservationenforcesaguaranteedminimumforresourcesand

alimitenforcesahardmaximum.Sharesettingscanbelow(1),normal(2),high(4),orcustom.Youcan

assigndifferentsharevaluesforCPU,memory,orbothtoensureproportionalresourceallocation.ESX

providesresourceswhileensuringtheallocationdoesnotviolatetheassignedreservationsandlimits(inHz

orbytes).

TotesttheeffectivenessofDRSincombinationwithresourceallocationfeatures,weranvarioustypesof

workloadvirtualmachineswithdifferentsharevalues.Figure6showstheninevirtualmachinesrunningon

twohostsbeforeDRSisenabled.Thesizeofeachcircleinthisfigurereflectsthenumberofstaticshares

assignedtotheworkload,nottheactualresourceusageofeachworkload.Hence,workloadswithdifferent

resourceusagecouldhavecirclesofthesamesize.Thesharesareassignedintheratioof1:2:4.Welocatedall

low(1)sharevirtualmachinesonthesamehostatthestartofthetestandlocatedthevirtualmachineswith

normal(2)andhigh(4)sharesonadifferenthost.

Figure 6. Initial Distribution of Virtual Machines with High Resource Contention

WeassignedthethreeWebservervirtualmachineshigh,normal,andlowshares.WeplacedthetwoWeb

serverswithhighandnormalsharesonthesamehost,hencetheyshareresourcefromonehost.Thevirtual

machinewiththelowsharesettinghasnootherWebservertocontendwith,henceitgetsrelativelymoreCPU

resourcesbecauseitencounterslesscontention.

Afterweenabledit,DRSbalancedtheworkloadstogivethefinalconfigurationasshowninFigure7.DRS

detectedthattheWebserverwithhighshareswasnotreceivingtheresourcesitwasentitledtoreceive.Infact,

asshowninFigure8,itwasreceivingalowerallocationthanthevirtualmachineconfiguredforalowshare

wasreceiving.DRSswappedtheWebservervirtualmachineswithlowandhighshares.ThisallowedtheWeb

serverconfiguredforahighsharetotakeadvantageoftheresourcesavailableontheotherhost.Theswapalso

freedresourcesfortheothervirtualmachineswithhighshares.Thus,DRSbalancedtheWebserverloads

acrossthetwohostsandsatisfiedtheoverallsystemsharesforallninevirtualmachines.

Figure 7. Balanced Distribution of Virtual Machines After DRS Swaps Two Web Servers

D W

J I

W

D

J

W

I

D

W

J I

WD

J

W

I


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Figure 8. Web Server Throughput

Figure8showsthechangeinWebservervirtualmachinethroughputbeforeandafterweenabledDRS.Before

weenabledDRS,theWebservervirtualmachinewithlowsharesachievedtwicethethroughputoftheWeb

servervirtualmachinewithnormalshares.WithoutDRS,thescopeofresourcesharingislimitedtoasingle

host.BecausetheWebservervirtualmachineswithhighandnormalsharesranonthesamehost,their

respectivethroughputnumbersreflectedtheirrelativesharesasmanagedbythelocalESXresourcescheduler,

whichcannotscheduleresourcesacrosshosts.However,afterweenabledDRS,thevirtualmachine

throughputnumbersreflectedtherelativevirtualmachinesharesforallvirtualmachines.DRSenforces

resourcesharingacrosshostsbycontrollingthehostlevelresourcesettings.

TheidleandJavaapplicationworkloadsdidnotchangebecausetheydonothaveanysignificantCPUusage

andthereisnomemoryovercommitmentorimbalance.BecauseCPUistheresourcefacingcontentioninthis

scenario,theWebserveranddatabaseworkloadsweregoodcandidatesformigration.DRSmigratedtheWeb

serverwithhighsharestothehostwherevirtualmachineswereentitledtofewerresourcesbecauseDRS

estimatedthatthemigrationwouldbringbetterbalanceatminimalmigrationcost.Later,DRSbalancedCPU

usagebymovingthelowsharesWebservervirtualmachinetothesecondhosttofurtherbalancethe

entitlements.

Resource Allocation Recommendations

ThefollowingrecommendationscanhelpyouimproveresourceallocationinyourDRSclusters.

Sharestakeeffectonlywhenthereiscontentionforresources.Hence,ifCPUresourcesonahostarenot

fullycommitted,eachvirtualmachineorresourcepoolonthathostgetsalltheCPUresourcesitdemands.

Similarly,ifresourcesonaDRSclusterarenotfullycommitted,virtualmachinesorresourcepoolsreceive

theresourcestheydemand,solongasthefollowingconditionsaremet:

TheDRSclusterhasnounappliedmanualrecommendations.

Novirtualmachineorresourcepoollimitsaresettovaluesbelowwhatthevirtualmachineworkload

demands.

Novirtualmachineorresourcepoolreservationsaresetinsuchawaythattheypreventany

balancingmigrationfromanovercommittedhost.

Noaffinityandantiaffinityrulesaresetinsuchawaythattheypreventanybalancingmigrationfrom

anovercommittedhost.

NohostVMotionincompatibilitiessuchasCPUincompatibilities,lackofasharedvirtualmachine

orVMotionnetwork,orlackofasharedvirtualmachinedatastorepreventanybalancingmigration

fromanovercommittedhost.

0

200

400

600

800

1000

1200

1400

1600

1800

Web server 1 (high) Web server 2 (normal) Web server 3 (low)

Throughput without DRS Throughput with DRS


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Youmightwasteidleresourcesifyouspecifyaresourcelimitforoneormorevirtualmachines.The

systemdoesnotallowvirtualmachinestousemoreresourcesthanthelimit,evenwhenthesystemis

underutilizedandidleresourcesareavailable.Specifyalimitonlyifyouhavespecificreasonsfordoing

so.

Forresourcepools,resourceallocationsaredistributedamongtheirsiblingvirtualmachinesorresource

poolsbasedontheirrelativeshares,reservations,andlimits.

Theexpandablereservationsettingforresourcepoolsallowsthereservationtogoaboveitsinitialvalue

ifthereservationofthechildvirtualmachinesorresourcepoolsincrease.Settingafixedreservation

preventsanyincreaseinthechildvirtualmachineorresourcepoolreservations.

Virtualmachineshavesomeoverheadmemorythatthesystemmustaccountforinadditiontothe

memoryusedbytheguestoperatingsystem.Thismemoryischargedasadditionalreservationontopof

theuserconfiguredvirtualmachinereservation.Hence,whenyousetthereservationsandlimitsfor

resourcepools,youshouldkeepabufferfortheextraoverheadreservationneededbythevirtual

machines.SeetheResourceManagementGuideforestimatedmemoryoverhead.(SeeResourcesonpage 19foralink.)Theamountdependsonvirtualmachinememorysize,thenumberofvirtualCPUs,

andwhetherthevirtualmachineisrunninga32bitor64bitguestoperatingsystem.Forinstance,a

virtualmachinewithonevirtualCPUand1024MBofmemoryhasavirtualmachineoverheadof

approximately98MBfora32bitguestoperatingsystemorapproximately118MBfor64bitguest

operatingsystem.Also,theoverheadreservationmaygrowwithtimedependingontheworkload

runninginthevirtualmachine.Thisoverheadgrowthisdesignedtoensureoptimalperformancewithin

theguestoperatingsystem.

Resourcepoolsmakemanagingresourceallocationsmucheasierbygroupingvirtualmachines,resource

pools,orbothwithineachresourcepool.Thisallowsadministratorstoapplysettingsacrossmany

resourceentitieseasilyandtobuildahierarchicalstructurethatmakesiteasytoallocatetheirresources.

Thispaperdoesnotaddresstheuseofresourcepoolhierarchiesbecausetheydonothaveanyimpacton

virtualmachineperformance.

Scaling the Number of Hosts and Virtual Machines

Scalingofthenumberofhostsandvirtualmachinesaffectstheresourcedistributionandloadbalancing

opportunities

available

to

DRS.

Our

testing

examined

the

impact

of

size

of

the

cluster

on

the

throughput

of

the

system.IncreasingthenumberofhostsinaclustergivesDRSmorelocationswhereitcanplacevirtual

machines,andconsequentlyaffectstheoutputofDRSalgorithms.Furthermore,asystemwithmorehosts

normallyhasmorevirtualmachines,aswell.IncreasingthenumberofvirtualmachinesmeansDRScanmove

moreentitiesaround.Eachofthesevirtualmachinesmightberunningdifferentworkloadsatdifferenttimes.

IncreasingthenumberofhostsandvirtualmachinesdoesincreasethecomplexityofvariousDRS

computations,butatthesametime,theincreaseprovidesopportunityformoreefficientresourcedistribution.

However,astheseopportunitiesincrease,thenumberofmigrationsmayincreaseandasaresultmayincur

additionaloverhead.

Weaccountedforthisoverheadinourexperimentsinordertoevaluatethescaling.Resourcepoolscan

simplifythetaskofallocatingresourcesacrosstheDRScluster,especiallywhendealingwithalargenumber

ofvirtualmachinesandhosts.However,scalingthenumberofresourcepoolsdoesnotaffectperformanceof

thevirtualmachinessolongastheresourcepoliciesareeffectivelyallocatedidentically.Hence,thissection

doesnotconsiderresourcepoolscaling.

Test Methodology

TheobjectiveofthetestwastomeasuretheeffectivenessoftheDRSalgorithmsaswescaledtolarger

environments,whileensuringthattheoverallclusterutilizationremainedconstantthatis,thattheratioof

overallvirtualmachinedemandtototalclusterresourcesremainedconstant.


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Wesetupahomogenousclusterofhostseachconfiguredwithtwo2.8GHzIntelXeonCPUswith

hyperthreadingenabled.Eachhosthad4GBofmemory.WededicatedaGigabitEthernetnetworkfor

VMotionandconfiguredDRStorunatthedefaultmigrationthreshold(moderate).Eachhosthadthree

workloadvirtualmachines,eachwithdifferentworkloads.

WeconfiguredeachvirtualmachinewithasinglevirtualCPUand1GBofRAMandinstalledRedHat

EnterpriseLinux3astheguestoperatingsystem.Forsimplicity,wegaveeachvirtualmachinethesameshares

andsetnoreservationsorlimits.Weranoneofthefollowingcategoriesofworkloadsinsideeachvirtual

machine:

ConstantworkloadusedafixedamountofCPUresourcesof10percent,50percent,or90percentof

CPU(ofonevirtualCPU).

VaryingworkloadusedavaryingamountofCPUresources,changingwithtime.Foreasyanalysis,the

workloadvariedamongthreesteps,using10percent,50percent,or90percentofCPU,witheachstep

lastingfiveminutes.Weusedallpossiblecombinationsofthethreesteploadtogeneratesixdifferent

workloadtypes,asshowninFigure9.OurresultswiththesevaryingworkloadsdemonstratethatDRS

performseffectivelyevenifloadsconstantlychangeovertimeandnostablestateisreached.Asourtests

showed,thisisoneofthemainadvantagesofusingDRS.

Figure 9. Patterns of Varying Workloads During Tests

Wedistributedvariouscombinationsofthevaryingworkloadvirtualmachinesacrosshoststoachieve

differentpatternsthatreflectthreehostusagescenarios.

Table 2. Test Environment for Scaling Tests

Number of

Hosts

Number of Virtual

Machines

Number of Each Constant

Workload

Number of Each Varying

Workload

2 6 2 1

4 12 4 2

8 24 8 4

16 48 16 8

1

2

3

4

5

6


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Figure 10. Workload on a Host with Optimal Placement

Intheoptimalplacementscenario,showninfigure10,weplacedcompensatingloadstogetheroneachhost.

Withthisplacement,allpeaksmatchedthetroughsofothervirtualmachinesonthesamehost.Asaresult,

noneofthehostsareovercommittedandtheloadisbalancedacrossallhostsinthecluster.Thethroughput

achievedinthisscenarioisusedasthebaselineforoptimalthroughputintheteststhatfollow.

Figure 11. Workload on a Host with Bad Placement

Inthebadplacementscenario,showninFigure11,weplacedsimilarworkloadstogetheroneachhost

whereverpossible.Asaresult,peaksandvalleysoccurtogetheronahost,resultinginahighlyimbalanced

cluster.Consequently,thehostsareeitherovercommittedorhighlyundercommitted.Thisresultsinnotonly

majorloadimbalanceacrosshostsbutalsopoorperformanceinthevirtualmachinesrunningonovercommittedhosts.AsshowninTable2,weusedincreasingnumbersofvirtualmachineswithsimilar

workloadsasweincreasedthesizeofthecluster.Asaresult,wesawgreaterimbalanceforabadplacement

astheclusterscaledup.

Figure 12. Workload on a Host with Symmetric Placement

Inthesymmetricplacementscenario,theclusterismoderatelyimbalanced,withamixofhoststhatare

somewhatovercommittedandhoststhataresomewhatundercommitted.Wecallthissymmetricplacement

becauseweusesthesameloaddistributionforeachpairofhostsaswescaledup.Asaresult,theload

imbalanceremainedthesameforallstagesofourtests.

Withthesmallerconfigurations,therearefewerworkloadsofthesametypeandhencetheloaddistribution

cannotbeasflexibleasitiswiththelargersetups.

1

3

5

+

+

=

Underutilized

Host capacity

Total virtual

machine load

Host loadVirtual machine loads

1

1

1

+

+

=

Underutilized

Host capacity

Total virtualmachine load

Host load

Overcommitted

Virtual machine loads

1

2

3

+

+

=

Underutilized

Host capacity

Overcommitted

Host loadVirtual machine loads


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Test Results

OurresultsshowthatDRSimproveshostloadbalanceacrossacluster.Theloadbalanceimprovement

improvesasthenumberofhostsandvirtualmachinesintheclusterincreases.Furthermore,throughputalso

improvesasthenumberofhostsandvirtualmachinesincreases.DRSperformsslightlybetterwithmorehosts

becausetherearemoreopportunitiesforbalancingloads.Asthesizeoftheclusterincreased,thenumberof

migrationsincreasedproportionally,butDRSensuredthatthemigrationswouldbenefitthroughput,hence

theaveragevirtualmachinethroughputimproved.DRSdidnotaffectthroughputwhenthevirtualmachines

wereplacedintheoptimalconfiguration,becausetheclusterwasalreadybalanced.Thekeyresultspresented

inthissectionhighlightthesefindings.

Figure13showstheresultswhenvirtualmachinesweredistributedinabadplacementthatis,ahighly

imbalancedclusteratthebeginningoftheexperimentwitheachofthevirtualmachineworkloadsvarying

everyfiveminutes.BecauseDRScomputedloadsandoptionsforbalancingtheloadseveryfiveminutes,this

wasaworstcaseworkload.SoonafterDRSfinishedbalancingtheload,thevirtualmachineloadschanged.

AsshowninFigure13,DRSimprovesvirtualmachinethroughputevenwhenitmustbalanceworkloadsthat

varygreatly.Atallenvironmentsizes,theaveragevirtualmachinethroughputstayedwithin96percentofthe

optimalthroughput.DRSachievedtheseresultsdespitethefactthatitwasusingVMotion,withitsattendant

overhead,tobalancetheload.Furthermore,thethroughputimprovementcomparedtotheinitialcondition,

withoutDRS,increasedaswescaledtolargerenvironments.Thisresultmainlyreflectsthefactthataswe

scaled

up,

the

number

of

similar

workloads

increased.

The

higher

number

of

similar

workloads,

in

turn,

causedgreaterimbalancewithbadplacementinlargerenvironments.

Figure13showstheresultswhenwesetDRStothedefaultaggressiveness(moderate).WhenwesetDRStoa

moreaggressivethreshold,weobservedmanymoremigrations,butthroughputimprovementsweresimilar

tothoseachievedwiththemoderatethreshold.Inaseparatetest,inwhichthevirtualmachineswererunning

constantloads,weobservedevenbetterthroughputimprovements.

Figure 13. Throughput with Varying Load and Bad Placement

Figure14showstheresultswhenwedistributedvirtualmachinesinasymmetricplacementthatis,a

somewhatimbalancedclusteratthebeginningofthetestwitheachofthevirtualmachineworkloadsvarying

everyfiveminutes.AsFigure14shows,theinitialcondition,withoutDRS,achievesthesamevirtualmachine

throughputatallenvironmentsizesbecausethevirtualmachinedistributionissymmetric.Ineachsize

environment,whenweenabledDRSatamoderatethreshold,itbroughttheaveragethroughputtowithin98

percentofoptimal.Increasingtheenvironmentsizebroughtminimalimprovementinthroughput,butthe

mainreasonoptimalthroughputwasnotachievedwastheoverheadofVMotionasDRSbalancedtheload.

Overall,DRAachievedsomeperformancegainsbytakingadvantageofsmalleropportunitiesforavoiding

overcommitment.

0.82

0.84

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0.90

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0.98

1.00

1.02

2 hosts / 6 VMs 4 hosts / 12 VMs 8 hosts / 24 VMs 16 hosts / 48 VMs

Averagevirtualmachinethrough

put

No DRS DRS moderate Optimal placement


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Figure 14. Throughput with Varying Load and Symmetric Placement

DRS Aggressiveness

DegreeofaggressivenessforDRScontrolshowaggressivelyDRSactstobalanceresourcesacrosshostsina

cluster.Thedegreeofaggressivenessissetfortheentirecluster.Thefiveoptionsrangefromconservative(level

one)tomoderate(levelthree)toaggressive(levelfive)andcontrolthedegreetowhichDRStriestobalance

resourcesacrosshostswithinthecluster.

DRSmakesVMotionrecommendationsbasedonwhatisreferredtoastheirgoodnessvaluethatis,how

muchimprovementinloadbalancingtheactioncanachieve.Thegoodnessvalueistranslatedtoarating

betweenonestarandfivestars,andDRSexecutesmigrationsbasedontheaggressivenessthresholdsetfora

cluster.InVMwareInfrastructure3,amigrationreceivesagoodnessvalueoffivestarsifitisrecommendedto

resolve

affinity,

antiaffinity,

or

reservation

rules.

For

each

lower

star

rating

the

balancing

impact

of

the

migrationisless.Soamigrationwitharatingoffourstarswouldimprovetheloadbalancemorethana

migrationwitharatingofthreestars,twostars,oronestar.

Figure15showshowthroughputcomparesforthemoderateandaggressivelevelsforthemixofconstantload

virtualmachinesandbadplacementdescribedinScalingtheNumberofHostsandVirtualMachineson

page 9.Theresultsshowthattheaggressivethresholdachievedslightlybetterthroughputcomparedtothe

moderatesetting.However,aswescaledtolargerconfigurations,thethroughputwasalmostthesameforboth

settings.Whenweusedtheaggressivesetting,wesawmoremigrationsthanwedidwhenweusedthe

moderatesetting.Whenweusedvaryingworkloads,notonlyweretheremoremigrationsintheaggressive

casebutthethroughputdifferencesforthemoderateandaggressivesettingswereevensmaller.

0.82

0.84

0.86

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0.90

0.92

0.94

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0.98

1.00

1.02


Averagevirtualmachine

throughput

No DRS DRS moderate Optimal placement


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Figure 15. Throughput for Constant Load and Bad Placement

Interval Between DRS Invocations

VirtualCenteractivatestheDRSalgorithmafterafixedinterval(thedefaultisfiveminutes)soDRScanmake

recommendationsbasedonthepastperformancemetricsofthevirtualmachinesintheDRScluster.This

sectiondiscussestheimpactontheDRSalgorithmofchangingthisinterval.TheDRScalculationsareinvoked

betweentheregularlyscheduledinvocationsiftherearechangesthatrequireDRSdecisionsforexample,if

youchangeresourceallocationsettings.Ourdiscussionignorestheseintermediateinvocationsbecausethey

dependonthefrequencywithwhichyouperformadministrativeoperationsinthecluster.

OurresultsshowthatchoosingtheappropriateDRSperiodicfrequencydependsontheperiodicityofthe

workloadswithinthevirtualmachinesinthecluster.FairlyconstantloadsmaybenefitfromfrequentDRS

invocationssothatimmediateactionistakenwhentheloaddoeschange.However,wedonotrecommendan

intervaloflessthanfiveminutesbecauseofthewaythealgorithmusesstatistics.Weobservednonotable

benefitwhenweincreasedfrequencytomorethanonceeveryfiveminutes,andrunningtheDRSalgorithm

veryoftencausesunnecessaryoverhead.Inaddition,anintervaloflessthanfiveminutesmightbetoo

aggressivegiventhatVMotionmigrationscantakeontheorderoftensofsecondstocomplete,dependingon

theloadrunninginthevirtualmachine.(Theguestoperatingsystemrunsnormallyforthemostofthe

VMotionoperationandgenerallyincursadowntimeoflessthanonesecond.)

Ifyounoticeveryfrequentmigrations,youmightfeeltheneedtoincreasetheinvocationinterval.However,

beforedoingso,youshouldchecktheDRSaggressivenesssetting.Decreasingtheaggressivenessmightbethe

betterchoice.WhenahighnumberofmigrationsisrecommendedinoneDRSinvocation,thisisusuallythe

resultofanaggressivethresholdsetting.

Ifaclusterincludesvirtualmachineswithmemoryintensiveworkloads,aninfrequentinvocationsettingmightnotallowDRStorespondtomemorypressuresoonenoughandthevirtualmachinemightstart

ballooningorswappingunnecessarily.Thiscansignificantlydegradeperformance.However,becausean

increaseinmemoryuseistypicallyamoregradualprocess,keepingthedefaultoffiveminutesdoesnotaffect

performanceforamajorityofworkloads.DRSalsoprioritizesmemorybalancingmigrationsoverCPU

balancingmigrationswhenmemorycommitmentishighinordertopreventunnecessaryballooningor

swapping.

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0.84

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1.02


Averagevirtualmachinethroughput

No DRS DRS moderate DRS aggressive Optimal placement


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Althoughwerecommendkeepingthedefaultvalueoffiveminutes,youcanchangethefrequencybyadding

thefollowingoptionsinthevpxd.cfgfile,changing300tothedesirednumberofseconds.

.

300


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Highlyskewedhostsizes

Virtualmachinesmightnotfitonallhosts.Forexample,virtualmachineswithfourvirtualCPUs

cannotmigratetohostswithonlytwophysicalCPUs.

DRStendstofavormigratingvirtualmachinestoahostwithmorememoryorCPUresourcesto

balancememoryorCPUloads.Essentially,thealgorithmtriestobalancetherelativeutilizationsfor

eachhost.Soifaclusterhasvirtualmachineswithidenticalloads,DRStypicallyplacesmorevirtual

machinesonahostthathasmoreCPUormemoryresources.Ifthevirtualmachineloadsare

primarilyCPUintensive,thehostmemorysizesdonotreallymatter.Conversely,iftheloadsare

memoryintensive,CPUcapacitiesdonotmatter.

HostconfigurationTheconfigurationofthehostnetworkordatastoremaydisallowmigrationusing

VMotionbecausenotallhostssharethesamevirtualmachineorVMotionnetworkordatastore.

DRSdoesareasonablejobinbalancingloadacrossheterogeneoushostssolongastheclusterismadeupofa

subsetofhoststhatareVMotioncompatible.Thefactorsdiscussedinthissectionmaylimitbenefits.We

recommendahomogenousclusterwhereverpossible.

Impact of Idle Virtual Machines

BasedonthetestsweperformedinourlabswithDRS,inenvironmentsrangingfromsmalltolarge,idle

virtualmachinesweregenerallynotmigratedbecausetheydidnotusesignificantresources.However,inan

imbalancedDRSclusterwithaggressivemode,alargenumberofidlevirtualmachinesperhost,orboth,DRS

wouldmigratetheidlevirtualmachinesbecausetheyhavesomememoryoverhead.Furthermore,some

operatingsystemsuseCPUcyclesforidlingforexample,fordeliveringguesttimerinterruptsandthiscan

alsoaffectDRSmigrationrecommendations.

DRS Performance Best Practices

ClusteringconfigurationscanhavesignificantimpactonDRSperformance.VMwarerecommendsthe

followingDRSconfigurationsandpracticesforoptimalperformance:

WhendecidingwhichhoststogroupintoaDRScluster,trytochoosehoststhatareashomogeneousas

possibleinCPUandmemory.Thisensureshigherperformancepredictabilityandstability.VMotionis

notsupportedacrosshostswithincompatibleCPUs.Hencewithheterogeneoussystemsthathave

incompatibleCPUs,DRSislimitedinthenumberofopportunitiesforimprovingtheloadbalanceacross

thecluster.ToensureCPUcompatibility, systemsshouldbeconfiguredwithCPUsfromthesamevendor,

withsimilarCPUfamilyandSSE3status.However,inVMwareInfrastructure3beginningwithversion

3.5Update2,EnhancedVMotionCompatibilitygivesyoutheabilitytoensureVMotioncompatibilityfor

thehostsintheclusterbypresentingthesameCPUfeaturesetacrosshosts,evenwhentheactualCPUs

onthehostsdiffer.SeeVMwareVMotionandCPUCompatibilityfordetails.SeeResourceson

page 19foralink.

DRSperformsinitialplacementofvirtualmachinesacrossallhostseveniftheyarenotVMotion

compatible.

IfheterogeneoussystemsdohavecompatibleCPUsbuthavedifferentCPUfrequencies,differing

amountsofmemory,orboth,thesystemswithmorememoryandhigherCPUfrequenciesaregenerallypreferred,allotherthingsbeingequal,ashostsforvirtualmachines,becausetheyhavemoreroomto

accommodatepeakload.

Usingmachineswithdifferentcacheormemoryarchitecturesmaycausesomeinconsistencyin

performanceofvirtualmachinesacrossthosehosts.

WhenmoreESXhostsinaDRSclusterareVMotioncompatible,DRShasmorechoicestobetterbalance

workloadsacrossthecluster.Werecommendclustersofupto32hosts.


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BesidesCPUincompatibilities,somemisconfigurationscanmaketwoormorehostsincompatible.For

instance,ifthehostsVMotionnetworkadaptersarenotconnectedbyaGigabitEthernetlink,the

VMotionmigrationmightnotoccurbetweenthehosts.YoushouldalsobesuretheVMotiongatewayis

configuredcorrectly,VMotionnetworkadaptersonthesourceanddestinationhostshavecompatible

securitypolicies,andthevirtualmachinenetworkisavailableonthedestinationhost.SeetheResourceManagementGuideandtheESXServer3ConfigurationGuidefordetails.SeeResourcesonpage 19forlinks.

Thedefaultmigrationthreshold(moderate)worksformostconfigurations.Youcansetthemigrationthresholdtomoreaggressivelevelswhenallofthefollowingconditionsaresatisfied:

Thehostsintheclusterarerelativelyhomogeneous.

Thevirtualmachinesresourceutilizationremainsfairlyconstant.

Theclusterhasrelativelyfewconstraintsonwhereavirtualmachinecanbeplaced

Youshouldsetthemigrationthresholdtomoreconservativelevelswhentheconverseistrue.

ThedefaultDRSfrequencyisonceeveryfiveminutes,butyoucansetittoanyperiodbetweenoneand

60minutes.Youshouldavoidchangingthedefaultvalue.Ifyouareconsideringachangeinthesetting,

seeIntervalBetweenDRSInvocationsonpage 14.

Ingeneral,donotspecifyaffinityrulesunlessyouhaveaspecificneedtodoso.Insomecases,however,

specifyingaffinityrulescanimproveperformance.

Keepingvirtualmachinestogethercanimproveperformanceifthevirtualmachinesneedto

communicatewitheachother,becausenetworkcommunicationbetweenvirtualmachinesonthe

samehostenjoyslowerlatencies.

Separatingvirtualmachinesmaintainsmaximalavailabilityofthevirtualmachines.

Forexample,iftwovirtualmachinesarebothWebserverfrontendstothesameapplication,you

wanttomakesurethattheydonotbothgodownatthesametime.

Anotherexampleofvirtualmachinesthatmightneedtobeseparatedisvirtualmachineswith

I/Ointensiveworkloads.Iftheyshareasinglehost,theymightsaturatethehostsI/Ocapacity,

leadingtoperformancedegradation.DRSdoesnotmakevirtualmachineplacementdecisionsbased

ontheirusageofI/Oresources.

Assignresourceallocationstovirtualmachinesandresourcepoolscarefully.Bemindfuloftheimpactof

limits,reservationsandvirtualmachinememoryoverhead.SeeResourceAllocationRecommendations

onpage 8fordetails.

VirtualmachineswithsmallermemorysizesorfewervirtualCPUsprovidemoreopportunitiesforDRS

tomigratetheminordertoimprovebalanceacrossthecluster.Virtualmachineswithlargermemorysize

ormorevirtualCPUsaddmoreconstraintsinmigratingthevirtualmachines.Henceyoushould

configureonlyasmanyvirtualCPUsandasmuchmemoryforavirtualmachineasneeded.

YoucanspecifyDRSmodesofautomatic,manual,orpartiallyautomatedattheclusterlevelaswellasthe

virtualmachinelevel.Werecommendthatyoukeeptheclusterinautomaticmode.Forvirtualmachines

sensitivetoVMotion,youcansetmanualmodeatthevirtualmachinelevel.Thissettingallowsyouto

decideifandwhenthevirtualmachinecanbemigrated.KeepvirtualmachinesinDRSautomaticmode

asmuchaspossible,becausevirtualmachinesinautomaticmodeareconsideredforclusterloadbalance

migrationsacrossESXhostsbeforevirtualmachinesthatcannotbemigratedwithoutuseractions.

Monitoring DRS Cluster Health

InVMwareInfrastructure3,youcanmonitoraDRSclustershealthbytrackingresourceutilizationofthe

cluster,loaddistributionacrosshosts,andwhetherthevirtualmachinesarereceivingtheresourcetowhich

theyareentitled.Thisinformationisdisplayedintwographsthatarepartoftheclustersummarydisplay.


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Host Utilization Percentage Graph

Thehostutilizationgraph,thetopgraphintheVMwareDRSResourceDistributionsection,isahistogramthat

showsthenumberofhostsontheYaxisandtheutilizationpercentageontheXaxis.Iftheclusteris

unbalanced,youseemultiplebars,correspondingtodifferentutilizationlevels.ThebluebarsrepresentCPU

usageandtheorangerepresentmemoryusage.Forexample,inthescreencapturebelow,twohostsareat010

CPUutilization,thethirdat8090percentCPUutilization,eachrepresentedbyabluebar.Inthiscluster,DRS

isinmanualmode(moderateaggressiveness)andismakingarecommendation.Aftertherecommendationis

appliedthebluebarsbecomeclosertoeachotherontheXaxis.Theclosertheblueandorangebarsaretoeach

other,themorebalancedtheclusteris.

Percent of Entitled Resources Delivered Graph

Chart(BottomDRSResourceDistributionChart)

Theentitledresourcesdeliveredgraph,thebottomgraphintheVMwareDRSResourceDistributionsection,

isahistogramthatshowsthenumberofhostsontheYaxisandthepercentageofentitledresourcesdelivered

foreachhostontheXaxis.Thetopgraphreportsrawresourceutilizationvalues.Thebottomgraph

incorporatesadditionalinformationaboutresourcesettingsforvirtualmachinesandresourcepools.

DRScomputesaresourceentitlementforeachvirtualmachine,basedonvirtualmachineandresourcepool

configuredshares,reservations,andlimitssettings,aswellasthecurrentdemandsofthevirtualmachinesand

resourcepools.Whatthevirtualmachinedemandsisnotnecessarilywhatitdeservesorisentitledtobecause

ofthevirtualmachineandresourcepoolconfigurations.

Aftercomputingtheentitlements,DRScomputesaresourceentitlementforeachhostbyaddingtheresource

entitlementsforallvirtualmachinesrunningonthathost.Thepercentageofentitledresourcesdeliveredis

equaltothehostscapacitydividedbytheentitlementsofthevirtualmachines.Thismeansthatinaclusterin

whichalltheentitledresourcesaredelivered,thegraphshouldhaveasinglebarforeachresourceinthe

90100percenthistogramrange.ThescreenshotintheprevioussectionshowsthateventhoughtheDRS

clusterwasnotbalanced,eachofthevirtualmachinesgottheresourcestowhichitwasentitled.

However,theremaybecasesinwhichhostsdonotdeliverallentitledresources.Forexample,inthescreen

capturebelow,oneofthehostscoulddeliveronly8090percentofentitledresourcesbecauseCPUresources

wereovercommittedonthehost.

Insummary,ifthebarsinthisgraphappearintherightmostcategory,allvirtualmachinesaregettingthe

resourcestowhichtheyareentitled.


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If you have comments about this documentation, submit your feedback to: [email protected]

VMware, Inc. 3401 Hillview Ave., Palo Alto, CA 94304 www.vmware.com

Copyright 2008 VMware, Inc. All rights reserved. Protected by one or more of U.S. Patent Nos. 6,397,242, 6,496,847, 6,704,925, 6,711,672, 6,725,289, 6,735,601, 6,785,886,6,789,156, 6,795,966, 6,880,022, 6,944,699, 6,961,806, 6,961,941, 7,069,413, 7,082,598, 7,089,377, 7,111,086, 7,111,145, 7,117,481, 7,149, 843, 7,155,558, 7,222,221, 7,260,815,7,260,820, 7,269,683, 7,275,136, 7,277,998, 7,277,999, 7,278,030, 7,281,102, 7,290,253, and 7,356,679; patents pending. VMware, the VMware boxes logo and design,Virtual SMP and VMotion are registered trademarks or trademarks of VMware, Inc. in the United States and/or other jurisdictions. All other marks and names mentionedherein may be trademarks of their respective companies.

Revision 20090109 Item: PS-060-PRD-01-01

Conclusions

TheeffectivenessandscalabilitytestsweperformedwithDRSdemonstratethatresourcesaredistributed

acrosshostsinaDRSclusteraccordingtotheresourceallocationsandthatbetterthroughputsareachieved

especiallyinthepresenceofvaryingloadsandrandomvirtualmachineplacementonhosts.TheDRS

frameworkalsoprovidestheabilitytoadjusttheaggressivenessoftheDRSalgorithmandintervalbetween

invocationsofthealgorithm.Inaddition,DRSavoidswastefulmigrationswithcostbenefitanalysisand

minimizesmigrationofidlevirtualmachines.

Basedonourexperienceandcustomerexperienceinthefield,weprovidedalistofbestpracticesthatyoucan

followtoavoidpitfalls.

Finally,DRSalsoprovidesafewmechanismsyoucanusetomonitortheDRSclusterperformanceand

potentially

provide

feedback

on

how

the

resources

are

being

delivered

and

whether

the

DRS

settings

need

to

beadjusted.

Resources

ESXServer3ConfigurationGuidehttp://www.vmware.com/pdf/vi3_35/esx_3/r35/vi3_35_25_3_server_config.pdf

ResourceManagementwithVMwareDRS

http://www.vmware.com/resources/techresources/401

ResourceManagementGuidehttp://www.vmware.com/pdf/vi3_35/esx_3/r35/vi3_35_25_resource_mgmt.pdf

VMwareVMotionandCPUCompatibilityhttp://www.vmware.com/resources/techresources/1022
mailto:[email protected]:[email protected]:[email protected]

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