THE GRIDWAY FRAMEWORK FOR ADAPTIVE SCHEDULINGAND EXECUTION ON
GRIDS
∗
EDUARDO HUEDO
†
,RUBÉN S. MONTERO
‡
, AND IGNACIO M. LLORENTE
§
Abstrat.
Manyresearh and engineering elds, likeBioinformatisor Partile Physis,are ondent about the developmentof Grid
tehnologies to provide the huge amounts of omputationaland storage resoures they require. Althoughseveral projets are
workingonreating areliable infrastruture onsistingof persistent resouresand servies,thetruth isthat theGrid willbea
moreandmoredynamientityasitgrows. Inthispaper,wepresentanewtoolthathidestheomplexityanddynamiityofthe
Gridfromdevelopersandusers,allowingtheresolutionoflargeomputationalexperimentsinaGridenvironmentbyadaptingthe
shedulingandexeutionofjobstothehangingGridonditionsandappliationdynamidemands.
Keywords. gridtehnology,bioinformatis,adaptivesheduling,adaptiveexeution.
1. Introdution. Gridenvironmentsinherentlypresentthefollowingharateristis[6℄: multiple
admin-istrationdomains, heterogeneity, salability, and dynamiity oradaptability. These harateristisompletely
determinethewayshedulingand exeutiononGrids haveto be done. Forexample,salabilityand multiple
administrationdomains preventthedeploymentof entralized resourebrokers, withtotal ontrol overlient
requestsandresourestatus. Onthe otherhand,thedynami resoureharateristisintermsofavailability,
apaityandost,makeessentialtheabilityto adaptjobexeutiontothese onditions.
Moreover, the emerging of Grid tehnology has led to a new generation of appliations that relies on
its own ability to adapt its exeution to hanging onditions [5℄. These new self-adapting appliations take
deisions about resoureseletion as their exeution evolves, and provide their own performane ativity to
detetperformaneslowdown. Thereforeself-adaptingappliationsanguidetheirownsheduling.
TodealwiththedynamiityoftheGrid andtheadaptabilityoftheappliationstwotehniqueshasbeen
proposedin theliterature,namely:
1. Adaptivesheduling,toalloatependingjobstogridresouresonsideringtheavailableresoures,their
urrentstatus,andthealreadysubmittedjobs.
2. Adaptive exeution, to migrate running jobs to moresuitable resoures basedon events dynamially
generatedbyboththeGridandtheappliation.
TheAppLeS[9℄projethaspreviouslydealt withtheoneptofadaptive sheduling. AppLeSis urrently
fousedondeningtemplates forharateristiappliations, likeAPST forparametersweepandAMWATfor
master/workerappliations. Also, the Nimrod/G [10℄ resourebrokerdynamially optimizesthe shedule to
meetuser-dened deadlineand budgetonstraints. On theother hand,theneed ofanomadi migration[14℄
approah for adaptive exeution on a Grid environment has been previously disussed in the ontext of the
GrADS[8℄projet.
Inthefollowingsetions,werstexplaintheneedforanadaptiveshedulingandexeutionofjobsduetothe
dynamiityofboththeGridandtheappliationdemands. Then,inSetion3,weshowaGrid-awareappliation
model. In Setion 4, wepresenthow theGridWay framework provides support for adaptive shedulingand
exeution. InSetion5,weshowsomeresultsobtainedintheUCM-CABresearhtestbedwithaBioinformatis
appliation. Finally, inSetion6,weprovidesomeonlusions andhintsaboutourfuture work.
2. Adaptive Sheduling and Exeution. Gridshedulingorsupersheduling[11℄, hasbeendenedin
theliteratureastheproessofshedulingresouresovermultipleadministrativedomainsbaseduponadened
poliyintermsofjobrequirements,systemthroughput,appliationperformane,budgetonstraints,deadlines,
∗
ThisresearhwassupportedbyMinisteriodeCieniayTenología(researhgrantTIC2003-01321)andInstitutoNaionalde
TéniaAeroespaial(INTA).
†
Laboratorio de Computaión Avanzada, Centro de Astrobiología (CSIC-INTA), 28850 Torrejón de Ardoz, Spain
(huedoeinta.es).
‡
Departamento de Arquitetura de Computadores y Automátia, Universidad Complutense, 28040 Madrid, Spain
(rubensmdaya.um.es) .
§
Departamento de Arquitetura de Computadores y Automátia, Universidad Complutense, 28040 Madrid, Spain
(llorentedaya.um.es ) & Laboratorio deComputaiónAvanzada, Centro deAstrobiología(CSIC-INTA), 28850 Torrejón de
et. Ingeneral,thisproessinludesthefollowingphases:resouredisoveryandseletion;andjobpreparation,
submission,monitoring,migrationandtermination[18℄.
Adaptiveshedulingistherststeptodealwiththedynamiityof theGrid. Thesheduleisre-evaluated
periodiallybasedon theavailable resouresand theirurrentharateristis,pending jobs, runningjobs and
historyproleofompletedjobs. Severalprojets[9,10℄havelearlydemonstratedthatperiodire-evaluation
oftheshedulein orderto adaptit tothehangingonditions,anresultin signiantimprovementsinboth
performaneandfaulttolerane.
Intheaseofadaptiveexeution,jobmigrationisthekeyissue[15℄. Inordertoobtainareasonabledegree
ofbothappliation performane andfault tolerane, ajobmust beable tomigrateamong theGrid resoures
adaptingitselftotheresoureavailability,load(orapaity)andost;andtotheappliationdynamidemands.
Consequently,thefollowingmigration irumstanes,relatedtothehangingonditionsandself-adapting
featuresbothdisussedin Setion1,shouldbeonsidered inaGrid environment:
1. Grid-initiatedmigration:
•
Abetter resoureisdisovered(opportunisti migration[16℄).•
Theremoteresoureoritsnetworkonnetionfails (failovermigration).•
Thesubmittedjobis aneledorsuspended.2. Appliation-initiatedmigration:
•
Performane degradation or performane ontrat violation is deteted in terms of appliationintrinsimetris.
•
Theresouredemandsoftheappliationhange (self-migration).The fundamental aspet of adaptive exeutionis the reognitionof hanging onditionsof bothGrid
re-souresandappliation demands. Inorderto ahievesuhfuntionality,weproposeaGrid-awareappliation
model,whihinludesself-adaptingfuntionality,andasubmissionagentthatprovidestheruntimemehanisms
needed to adapt the exeution of the appliation. The appliation must be equipped with the funtionality
neededto supporttheappliation-initiated migrationirumstanes,while theagentis ontinuouslywathing
theourreneoftheGrid-andappliation-initiatedmigrationirumstanes.
3. Appliation Model for Self-Adapting Appliations. The standard appliation model requires
modiations to be Grid-aware. In the following list (see gure 3.1) we detail the extension of the
lassi-al appliation paradigmin order to take advantageof the Grid apabilities and to be aware of its dynami
onditions:
•
Arequirement expressionisneessarytospeifytheappliationrequirementsthatmustbemetbythetarget resoures. This le an be subsequently updatedbytheappliation to adapt itsexeution
to itsdynami demands. Theappliation oulddene an initialset of requirementsand dynamially
hangethemwhen more,orevenless,resouresarerequired.
•
Aranking expressionisneessarytodynamiallyassignaranktoeahresoure,inordertoprioritizethe resoures that fulll the requirements aording to the appliation runtime needs. A
ompute-intensiveappliation would assign ahigherrankto thosehosts with fasterproessorsand lowerload,
whileadata-intensiveappliation ouldbenetthosehostslosertotheinputdata[16℄.
•
A performane profile is advisableto keepthe appliation performane ativityin terms ofappli-ationintrinsimetris,in ordertodetetperformaneslowdown. Forexample,itouldmaintainthe
time onsumedby theodein theexeutionof aset of given fragments, in eah yle ofan iterative
methodorinasetofgiveninput/outputoperations.
Due to the high fault rate and the dynami resheduling, restart filesare highly advisable. Migration is
ommonlyimplementedbyrestartingthejobonthenewandidatehost,sothejobshouldgeneraterestartles
at regular intervals in order to restartexeution from a given point. However, forsome appliation domains
the ost ofgenerating and transferringrestart les ouldbe greaterthan the saving in omputetime due to
hekpointing. Hene,ifthehekpointinglesarenotprovidedthejobshouldberestartedfromthebeginning.
User-levelhekpointingmanagedbytheprogrammermustbeimplementedbeausesystem-levelhekpointing
isnotpossibleamongheterogeneousresoures.
Theappliationsoureodedoesnothavetobemodiediftheappliationisnotrequiredtobeself-adaptive.
However, our infrastruture requires hanging the soure ode or inserting instrumentation instrutions in
Input Files
Input Files
RESOURCE
SELECTOR
Input Files
Std. Input
Std. Output
Std. Error
Restart File
APPLICATION
Output Files
Performance
Profile
Rank
Expression
Resource
Requirements
PERFORMANCE
DEGRADATION
EVALUATOR
Fig.3.1.Modelforself-adaptingappliations.
Withself-adaptingapabilities,anappliationouldinitiallydeneaminimalsetofrequirementsand,after
itbeginstorun,itanhangethemtoamorerestritedset. Inthisway,theappliationwillhavemorehanes
tondaresouretorunon,andonerunning,itwillmigrateonlyiftheandidateresoureworthsit.
Notealsothatiftheappliationisdividedinseveralphases,eahonewithdierentrequirements,itould
hange themprogressivelyto bemoreor lessrestritive. Inthis way,theappliation doesnothavetoimpose
themostrestritedsetofrequirementsat thebeginning,sineitlimitsthehanefortheappliationtobegin
exeution(seeSetion5.3.2). Moreover,theappliationhavethehoietomaketherequirementhangeoptional
ormandatory,i.e. itan hekiftheurrentresouremeets thenewrequirements,otherwiseit mayrequesta
(self-)migration.
4. GridWay Support for Adaptive Sheduling and Exeution. GridWay is anew experimental
framework based onGlobus [4℄ that allowsan easierand more eient exeution ofjobs ona dynami Grid
environmentinasubmitandforgetfashion. TheoreoftheGridWayframework[13℄isapersonalsubmission
agent thatperformsalltheshedulingstages[18℄ andwathesovertheorretandeientexeutionofjobs.
Adaptationtohangingonditionsisahievedbydynamiresheduling: onethejobisinitiallyalloated,itis
resheduledwhenamigration irumstane(disussedin Setion2)isdeteted.
Jobexeution is performed in three stages by the following modules, whih anbe dened ona perjob
basis:
•
Theprolog module,whihpreparestheremotesystemandstagestheinputles.•
Thewrappermodule,whih exeutestheatualjobandreturnsitsexitode.•
Theepilog module,whihstages theoutputlesandleansuptheremotesystem.Migrationisperformedbyombiningtheabovestages.First,thewrapperisaneled(ifitisstillrunning),
thentheprolog issubmittedto thenewandidate resoure,preparingitand transferringto itall theneeded
les,inludingtherestart filesfromtheoldresoure. Afterthat,theepilog issubmittedtotheoldresoure
(ifitisstillavailable),butnooutputlestagingisperformed,itonlyleansuptheremotesystem. Finally,the
wrapperissubmittedtothenewandidateresoure.
Thesubmissionagentusesthefollowingmodules, whih alsoanbedenedonaperjobbasis,toprovide
theappliation withthesupportneededforimplementingself-adaptingfuntionality:
•
Theresoureseletor module,whihevaluatestherequirementandranking expressionswhenthejobhastobesheduledorresheduled. Dierentstrategiesforresoureseletionanbeimplemented,
fromthesimplestonebasedonapre-denedlistofhoststomoreadvanedstrategiesbasedon
require-mentltering,andresourerankingin termsofperformanemodels.
Dierent strategies ould be implemented, from the simplest one based on querying the Grid
infor-mation servies about system status information to moreadvaned strategies based on detetion of
performaneontratviolations.
Thesubmission agentalso provides theappliation with the fault toleraneapabilities needed in suh a
faultyenvironment:
•
The GRAM [1℄ job manager noties submission failures as GRAM allbaks. This kind of failuresinludes, among others, onnetion, authentiation, authorization, RSLparsing, exeutableor input
staging,redentialexpiration...
•
Thejobmanager isprobedperiodiallyat eahpolling interval. Ifthejobmanager doesnotrespond,theGRAMgatekeeperisprobed. Ifthegatekeeper responds,anewjob manager isstartedtoresume
wathing overthejob. If thegatekeeper failsto respond, aresoureornetworkourred. This isthe
approahfollowedbyCondor-G[12℄.
•
Thestandardoutputofprolog,wrapperandepilog is parsedinordertodetetfailures. Intheaseofthewrapper,thisisusefultoapturethejobexitode,whihisusedtodeterminewhetherthejobwas
suessfullyexeutedornot. Ifthejobexit odeisnotset, thejob wasprematurelyterminated,soit
failedorwasintentionallyaneled.
Whenanunreoverablefailureisdeteted, thesubmissionagentretriesthesubmissionofprolog,wrapper
orepilog anumberoftimesspeiedbytheuserand,whennomoreretriesareleft,itperformsanationhosen
bytheuseramongtwopossibilities: stopthejobformanuallyresumingitlater,orautomatiallyresheduleit.
We have developed both an API (subsetof the DRMAA [17℄ standardproposed in the GGF [3℄) and a
ommand line interfaeto interatwith thesubmission agent. Theyallowsientistsandengineers to express
theiromputationalproblemsinaGridenvironment. Theaptureoftheremoteexeutionexitodeallowusers
todeneomplexjobs,whereeahdependsontheoutputandexitodefromthepreviousjob. Theymayeven
involvebranhing, loopingand spawningof subtasks,allowingtheexploitationofthe parallelismonthework
owofertaintypeofappliations.
Ourframeworkis notbounded toaspei lassof appliations, doesnotrequirenewservies, anddoes
notneessarilyrequiresoureodehanges. Theframeworkisurrentlyfuntional onanyGridtestbedbased
onGlobus. We believethat is animportantadvantagebeause of soio-politial issues: ooperationbetween
dierentorganizations,administrators,and usersanbeverydiult.
5. Experienes.
5.1. The Target Appliation. We have tested our tool with a Bioinformatis appliation aimed at
prediting the struture and thermodynami properties of a target protein from its amino aid sequenes.
The algorithm,tested in the 5th round of Critial Assessment of tehniques forprotein StruturePredition
(CASP5),alignswithgapsthetargetsequenewithallthe6150non-redundantstruturesintheProteinData
Bank(PDB),andevaluatesthemathbetweensequeneandstruturebasedonasimpliedfreeenergyfuntion
plusagap penalty term. Thelowest soringalignmentfoundis regardedasthepreditionifit satisessome
qualityrequirements. Foreahsequene-struturepair,the searhoftheoptimalalignmentisnotexhaustive.
Alargenumberofalignmentsareonstrutedinparallelthroughasemi-deterministialgorithm,whihtriesto
minimizethesoringfuntion.
Tospeeduptheanalysisandreduethedataneeded,thePDBlesarepreproessedtoextrattheontat
matries,whihprovideareduedrepresentationofproteinstrutures. Thealgorithmisthenappliedtwie,the
rsttimeasafastsearh,inordertoseletthe100bestandidatestrutures,theseondtimewithparameters
allowingamoreauratesearhof theoptimalalignment.
We haveapplied the algorithm to thepredition of thermodynami properties of families of orthologous
proteins,i.e. proteinsperformingthesamefuntionin dierentorganisms. Ifarepresentativestrutureofthis
setisknown,thealgorithmpreditsitastheorretstruture. Thebiologialresultsoftheomparativestudy
ofseveralproteinsare presentedelsewhere[19,7℄.
5.2. Experiment Preparation. We have modied the appliation to provide a restart file and a
performane profile. Thearhiteture independentrestart filestoresthebest andidateproteinsfound
to that moment and the next protein in the PDB to analyze. The performane profile stores the time
TheUCM-CABresearhtestbed.
Name Arhiteture OS Speed Memory Jobmgr. VO
ursa 1
×
UltraSPARC-IIe Solaris 500MHz 256MB fork UCMdrao 1
×
UltraSPARC-I Solaris 167MHz 128MB fork UCMpegasus 1
×
Pentium4 Linux 2.4GHz 1GB fork UCMsolea 2
×
UltraSPARC-II Solaris 296MHz 256MB fork UCMbabiea 5
×
AlphaEV6 Linux 466MHz 256MB PBS CABInitially,theappliationdoesnotimposeanyrequirementtotheresoures,sotherequirement expression
isnull. Theranking expressionusesaperformanemodel to estimatethejob turnaroundtime asthesum
ofexeutionandtransfertime,derivedfromtheperformaneandproximityoftheandidateresoures[16℄.
The resoure seletor onsists of a shell sript that queries the MDS [2℄ for potential exeution hosts.
Initially, available omputeresouresare disoveredby aessingthe GIIS serverandthose resouresthat do
notmeettheuser-providedrequirementsarelteredout. Atthisstep,anauthorization test(via GRAMping
request) is performed on eah disovered hosts to guarantee useraess. Then, theresoure is monitoredto
gather its dynami status by aessing its loal GRIS server. This information is used to assign a rank to
eah andidateresourebasedonuser-providedpreferenes. Finally,theresultantprioritized listof andidate
resouresisusedtodispaththejobs.
Inordertoredue theinformationretrievaloverhead,theGIISandGRIS informationisloally ahedat
thelienthostand updatedindependentlyin orderto separately determinehowoftenthe testbed issearhed
fornewresouresandthefrequenyofresouremonitoring. InthefollowingexperimentswesettheGIISahe
timeoutto 5minutesandtheGRISahetimeoutto 30seonds.
Theperformaneevaluator is anothershellsriptthat parses theperformane profileanddetets
per-formaneslowdownwhen thelast iterationtimeisgreaterthanagiventhreshold.
Thewholeexperimentwassubmittedasanarrayjob,whereeahsequenewasanalyzedinaseparatetask
ofthearray,speifyingalltheneededinformationin ajob templatele.
Theexperimentlesonsistsof: theexeutable(0.5MB) providedfor alltheresourearhiteturesin the
testbed,thePDBlessharedandompressed(12.2MB)toreduethetransfertime,theparameterles(1KB),
and the le with thesequene to be analyzed (1KB).The nal le name of theexeutable and the le with
thesequene to be analyzedis obtainedby resolvingthe variables GW_ARCH andGW_TASK_ID, respetively, at
runtimefortheurrenthostandjob. Inputlesanbeloalorremote(speiedasaGASSoGridFTPURL),
andbothanbeompressed(tobeunompressedontheseletedhost) anddelaredasshared(thenstoredin
theGASS aheandsharedbyallthejobssubmittedtothisresoure).
5.3. Results on the UCM-CAB Testbed. We have performed the experiments in the UCM-CAB
researhtestbed, whihissummarized intable5.1.
5.3.1. Detetion of a Performane Degradation. Let us rst onsider an experiment onsisting in
vetasks,eahofthemappliesthestruturepreditionalgorithm toadierentsequeneoftheATPSynthase
enzyme(epsilon hain) presentin dierent organisms. Shortlyafter submitting theexperiment, pegasus was
overloadedwithaompute-intensiveappliation.
Figure 5.1 shows the exeution prole in this situation, along with the load in pegasus that aused the
performane degradation, and the progressof job 0, obtained from its performane profile. Initially four
tasks arealloated to babieaand oneto pegasus. When theperformane evaluator detets theperformane
degradation, it requests a job migration. Sine there is a slot available in babiea, the job is migrated to it
although it presents lowerperformane. In spite of the overhead indued by job migration, 6% of the total
exeutiontime,job 0endsbeforetherest ofjobs,beauseofthebetterperformaneoeredbypegasusbefore
itbeamesaturated.
5.3.2. Mandatory Change in Resoure Requirements. In the following experiment, we have
Fig.5.1. Exeutionprole(top),load inpegasus(middle),andprogressof job0(bottom)whenaperformanedegradation
isdeteted.
AsmentionedinSetion5.1,thetargetappliationisdividedintwodierentphases. First,afairanalysis
isperformedto getthe100bestandidate proteins,andthen, amoreexhaustiveanalysisisperformedto get
the20bestandidateproteinsfromthe100obtainedintherstphase. Astheseondphaseanalysisperforms
amoreauratesequenealignmentandthetargetsequeneisquitelarge,itneedsmorememorythantherst
phaseanalysis. Therefore,theappliationhangeitsresourerequirementsbeforestartingtheseondphaseto
assurethatithasenoughmemory(512MB).Theonlyresourethatmeetstherequirementsoftheseondphase
ispegasus.
Figure5.2 showstheexeutionproleinthis situation. Job0startsexeutiononpegasus,whilejobs 1to
4startexeutionon babiea. Whenjob 0ompletesits exeution,job 1detetsthat pegasushasbeomefree
andmigratestoit,sineitpresentsabetterrank(opportunisti jobmigration). Afterthat,jobs2to4request
aself-migrationastheyhavehangedtheirrequirementsto ompletetheseond phaseoftheproteinanalysis
and babieadoesn'tmeet them. Jobs0and 1also hangedtheir requirementsbefore, but its exeutionhost
in thatmoment(pegasus)metthem, sotheyouldontinuewith theirexeution. Aspegasusisbusywith job
1,jobs 2to 4haveto wait until itbeomesavailable. These jobs aresubmittedonseutivelyto pegasus(see
gure5.2)to ompletetheseondphaseoftheproteinanalysis.
6. Conlusions. We have shown an eetive way for providing adaptive sheduling and exeution on
Grids. Thepresentedframeworkdoesnotneessarilyrequiresoureodehangesintheappliations, butwith
minimalhanges, appliationsouldbenetfromtheself-adaptingfeatures alsoprovided.
On thesopeof thetarget appliation, these promising experimentsshowthepotentiality ofthe Grid to
Fig.5.2. Exeutionprolewhenamandatoryhangeinresourerequirementsours.
Weareurrentlyworkingonastorageresoureseletor moduletoprovidesupportforrepliales,speied
as a logial le or as a le belonging to a logial olletion. In this way the PDB les holding the protein
strutures,will besattered ontheGridtestbed. Thedisoveryproessis performedbyaessing theGlobus
RepliaCatalog. Theresoureseletionis basedontheproximitybetweentheseletedomputeresoureand
theandidatestorageresoures,alongwiththevaluesgatheredfromtheMDSGRIS.
Aknowledgments. Wewould liketo thankUgo Bastolla,sta sientistat theCentrode Astrobiología
anddeveloperoftheBioinformatisappliation usedintheexperiments,forhissupportonunderstandingand
modifyingtheappliation.
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Editedby: WilsonRivera,JaimeSeguel.
Reeived: July3,2003.
