Toward Reputable Grids

TOWARD REPUTABLE GRIDS

G. VON LASZEWSKI

∗

, B. K.ALUNKAL

†

,AND I. VELJKOVIC

‡

Keywords. Grid,Quality-of-servie,Trust,Reputation

Abstrat.

TheGridapproahprovidesavisiontoaess,use,andmanageheterogeneousresouresinvirtualorganizationsarossmultiple

domainsand organizations. Thispaper foremostanalyses someof the issues related to establishing trustand reputation ina

Grid. Integratingreputationintoqualitymanagementprovidesawaytoreevaluateresoureseletionandservielevelagreement

mehanisms. WeintrodueareputationmanagementframeworkforGridstoworktowardfailitatingtheomplextaskofimproving

the qualityofresoure seletion. Basedon ommunityexperieneweadapttrustandreputation of entitiesthrough speialized

servies.Simpleontextualqualitystatementsareevaluatedinordertoeetthereputationforamonitoredresoure. Additionally,

weintroduea novelalgorithmforevaluatingGridreputation byombiningtwoknownoneptsusingeigenvetors toompute

reputationandintegratingglobaltrust.

1. Introdution. TheGridapproah[18,21℄providesavisiontodevelopanenvironmentforoordinated

resoure sharing and problem solving in dynami, multi-institutional virtual organizations under

quality-of-servieonstraints[5,10℄. However,optimaluse of thesedistributed serviesand resouresrequires notonly

knowledge about the apabilities of the resoures, but also the assurane that the available and requested

apabilitiesanbeusedsuessfully. Gridusersarefaedwithquestions suhaswhih resouresareavailable

remotely,whihapabilitiestheseresoureshave,whetheroneisauthorizedtousetheseresoures,whetherthe

informationforaresoureseletionisaurate,andonwhihresouresataskislikelytoexeutewiththemost

suess.

In a typial Grid senariousers identify possible andidate resoures throughmetainformation obtained

fromdiretories,databases,orregistries. However,theurrentgenerationofGridinformationserviesprovides

only the most elementary information to guide quality-of-servie based resoureseletion. For example, the

GlobusToolkitMonitoringandDiretoryServie(MDS) [19℄providesalimitedset ofinformationaboutGrid

resoures, inluding stati and possibly dynami attributes and properties. In many ases the information

returnedbythisservieisostlytoobtain,inaurate,oroutdatedanddoesnotintegratearesoureseletion

servie. Weobservethat, similarto Heisenbergsunertaintypriniple[13℄, themorevariability(momentum),

theinformation inregardsto aresoureattributeontains,thelessweanpredittheaurayofitsvalueat

atime,and vie versa. This prinipleisof espeialimportaneifweonsider theuseof multiple resouresin

aoordinated fashion, multiplyingthiseet. Furthermore,thesporadinatureof theGridand itsmeasured

valuesaswellasthepossibilityofintegratingadhoservies[21℄inaGridenvironmentforwhihnohistorial

dataisavailable,posesaseverelimitationontheurrentgenerationofpreditionservies.Additionally,weoften

lakinformationprovidedonthequalityofthepartiipatingentities,similartoanInternetshoppingsite,whih

lassies inluded items while augmenting them with information not only about funtionality, appearane,

availability,andprie,butalsoaboutappreiationsandratingsbyitsshoppers.

In our framework we propose a probabilisti preseletion of resoures based on likelihood to deliver the

requested apability and apaity. Suh a servie an be integrated into a quality-of-servie management

framework [7℄ to enable the reevaluation of the eetiveness of quality-of-servie poliies and servie level

agreements.

ThismotivatedustodesignareputationframeworkforGridstoassistintheseletionproessforresoures

whileintegratingthenotionsoftrustandreputation. Trustisalreadyaritialparameterinthedeision-making

proessofseveralpeer-to-peer(P2P)frameworks. Reputationisomputedbyusingatrustrating providedby

usersof serviesthroughafeedbakmehanism. Reputation-basedservie andprodut seletionhaveproved

tobeagreatassetforonlinesitessuhaseBay[9℄andAmazon[3℄.

Hene,weproposeaframework thatseletsthroughahierarhialproess,with thehelpof sophistiated

Gridservie,setsofresouresandserviesassuitableandidatestofulllquality-of-servierequirements. This

inludestheseletionoftrustedresouresthatbestsatisesappliationrequirementsaordingtoapredened

∗

MathematisandComputerSieneDivision,ArgonneNationalLaboratory,Argonne,IL60439,U.S.A.gregorms.anl.gov

†

DepartmentofComputerSiene,IllinoisInstituteofTehnology,Chiago,IL60616,U.S.A.

‡

trustmetri. Therefore,weproposethatourhierarhialresoureseletionproessbeaugmentedbyqualitative

andquantitativeexperienesbasedonprevioustransationswithresouresso weanintegratethisexperiene

infuture resoureseletions.

Weenvisionsuh areputation systemforGrids, in whihresouresand serviesare rankedbased onthe

reputation they obtain. Generating a reputation or establishing trust by entities (resoures, servies, and

individuals)inregardstotheiravailabilityandapability. Webelievethatsuhareputationservieframework

is of ruial importane forGrid omputing to inreasereliability, use, and popularity. Trust and reputation

serve asan importantmetri to avert the use of underprovisionedand maliious resoures; they provide the

abilityto simplifytheseletionproesswhilefousingrstonqualitativeonerns.

Consider a Grid environment that agglomerates expensive and speialized resoures inluding

high-per-formane servers, storage databases, advaned sienti instruments, and sophistiated servies to visualize

maromoleules[22℄ornanomaterial[4℄strutures. SuhanenvironmentrequiresreliableadhoGridservies

tofulll theneessaryquality-of-servierequiredbyseurereal-timeuse. Furthermore,thesporadiand

time-limitednatureoftheserviesandresouresusedmayresultinalakofhistorialdata,posingseverelimitations

onexistingpreditionservies.

Community-basedadaptivemetrissuh astrust and reputation serve asbuilding bloks to support our

quality-of-servierequirements. Weemphasizethat theself-evaluationofaserviemustbeanintegralpartof

the Grid arhiteture in order to inrease reliabilityand preditability. Consider the asein whih aservie

laimsitwillprovideapartiularlevelofqualityandengagesinaservielevelagreementwithanotherservie.

Assumethatthisserviefailstodeliverthepromisedagreement. Suhasenariomightexistwhenthemetris

available forseletiondonotoinidewiththegoals. Choosingamorereliableservieanavoidthisproblem.

Buthowdoweknowthatanotherservieismorereliable?

Conretely,ifwetryto transfer10Gbytesofdatabetweenremoteresouresthroughanetwork,wemight

betemptedto seletanetworkpathwiththehighestobservedpeakthroughput. However,ifthenetworkgets

interruptedand thetransferwould fail, themeasurementandmetrimust takethisinto aount. Weannot

relyon aserviethat seletsthe routefor transferbasedonly ona simplebandwidth measurement. Rather,

werequireaserviethat evaluatesthepromisedagreementandisavailable forfuture referene. Hene,weare

notonlyonernedwiththequality-of-servie,butalsowiththequality-of-information[20℄toestablishsuha

servie.

Weneedtoaddressin aneetivequality-of-servieframeworkthefollowingissues:

1. Identify themetristhataredening theservie,

2. Implementaquality-of-serviepoliy,

3. Providemeasurementsthatan helpseletingresouresundermetriservielevelagreements,

4. Deideforaservieagreement,

5. Preseletanumberofresouresthatwilllikelyfulll theagreement,

6. Exeutetheservie,

7. Evaluatethepoliies bymeasuringasuessfulresponse,

8. Adaptthestrategyifitwasnotsuessful,toseletnewresoures(i.e,returntoStep5).

InthispaperwewillfousonStep8ofthis framework. Otheraspetsareaddressedin [2℄.

Ourpaperisstruturedasfollows. InSetions2, 4,and 5,wedene thetermstrust andreputation and

provide an overview of the existing reputation systems for the Grids and their limitations. In Setion 3, we

presentthegeneralrequirementsofGridreputationframeworkandservie. InSetion 6,7,and8,wepropose

anewalgorithmformanaging reputationinGrid-basedsystemsanddisussitsunderlyingarhiteture. After

weprovideanoverviewofotherrelatedworkwesummarizefutureworkandonludeourwork.

2. Trust and Reputation. Inthis setion wedene the basiterminologyused throughoutthe rest of

thepaper.

2.1. Denition: Entity. For simpliity,werefertoaresoure,agent,servie,organization,oruserasan

entity. ThisdenitionallowsustospeifythetermtrustinthemostgeneralwaywhileapplyingittotheGrid

approah.

2.2. Denition: Entity Trust. As pointed out by many researhers, trust is an ambiguous onept

that deesexatdenition. Basedoneonomimodels[11℄,however,we andene trustasaommodityfor

trustthrougheonomiinentivesisanimportantfatortoallowtrusttobeomeastableommodity. Forour

proposed framework,trustisthe underlyingpriniplethat wedetermined throughloalorglobalinterations

amongentitiesandtheirdeisionsbasedonit.

2.3. Denition: VirtualTrust. Sofarwehavenotdisussedtheowrelationshipsbetweentrustorsand

trustees. Ifatrustvalueinaommunityisassignedtoanentity(thetrustor)itstrustvalueanbereusedbya

newtrusteewhojoinstheommunityandadheresinprinipletothesamevaluesastheommunitymembers.

Inthis aseweusethetermommunitytrust,orvirtual trust.

2.4. Denition: Entity Reputation. Reputationreferstothevalueweattributetoaspeientityin

the Grid, basedon the trustexhibited byit in thepast. It reetsthe pereptionthat onehas of another's

intentionsandnorms. Entityreputation providesawayof assigningqualityorvaluetoanentity. Reputation

is usually assoiated with a time fator; it is often gained over time, based on qualities attributed to it by

evaluationsofotherentities. Inmanyreputationmodels,reputationdereasesquiklybasedonadversebehavior.

2.5. Denition: Entity Reputation Servie. An entity reputation servie is dened as aseure

in-formation servie responsible for maintaining a dynami and adaptive trust and reputation metri within a

ommunity. EntitiesintheGridontinuouslyinteratwiththereputationservietoreateaommunityrating

mehanismthatooperativelyassiststheirfuture deisionsbasedontheoverallommunityexperienes.

3. Trust Models. Todeneatrustmodel,weneedto establishtrustrequirements,assign trustratings,

anddenetrustmediationframeworksandalgorithms. BeauseofthediversityoftheGridanditsommunities,

weannotdeneasingletrustmodel suitableforeveryase. Instead,weneedtorevisit therequirementsand

theirumstanesinwhihsuhatrustmodelbringsaddedvaluetotheGridinfrastruture. Someofthemost

ommoningredientsusedtodesigntrustmodelsforGridsareneighborhoods,ommunities,virtualorganizations,

ontrats,branding, andownership.

3.1. Neighborhoods and Communities as Trust Models. One of the most ommon trust models

is based on the denition of neighborhoods and ommunities. Here a group of entities form a relationship

network that an be used to query about the trust the members have for another entity to be aessed or

used. Neighborhoods are typially small peer-to-peer groupswhere eah membertypiallyknowstheothers.

Inontrast to this model, ommunities ontain manymore members,and it may nolonger possiblethat for

memberoftheommunityto knowthe others. Inbothgroups,however,trustandreputation areestablished

throughstandardsandommonviewsgovernedbytheommunitiesandneighborhoods. RatingsareAdapters

throughinterpersonalommuniationorthroughpubliationonaommunity-widesale. Agoodexampleofa

neighborhoodtrustmodelistheloseinterationamongomputationalsientiststointerprettheoutomeofa

partiularsientiexperiment. Agoodexampleofaommunitytrustmodelistheolletionandpubliationof

opinionsaboutapartiulartopi. Insomeasestrustedneighborhoodsareestablishedtoprovidetheommunity

withtrustratings. An exampleis aneditorialboardforthepubliation ofartilesin asientijournal. The

sientiommunitypaysmoreattentionto anartilereviewedbyitspeersthanto anartilepublished ona

unmoderatedWebpage.

3.2. GeographyandPolitialBoundariesasTrustModel. Asimplewaytoestablishneighborhoods

andommunitiesistoonsidergeographialdistaneorpolitialboundaries. Beingaitizenofaforeignountry

willbeinmostasesrequirespeiallearanetopartiipateinentitiesontrolledbyagovernmentoruniversity

as is often thease forsuperomputingenters. Geographial onstraints may be needed in order to restrit

adaptivetrustalgorithmstoanumberofentitiesinloseviinity. Thisisoftentheaseforertiateauthorities

that havebranhesoperatingin geographialdistributed loationto verifythephysialexisteneof aperson.

HierarhialGridssuhastheTeraGridorthePhysisDataGridfuntioninsuhfashion. Althoughonsidered

avirtualorganization,membershipinto thisorganizationsponsoredbytheommunityis determinedbyloal

trustauthorities.

3.3. Contrats as Trust Model. A ontrat is a binding agreement between twoor morepersons or

parties. Contratsareurrentlyunder muhdisussionaspartofservielevelagreementsinQoS-based

frame-works suh asWebserviesandGrid servies. Here aontratbetweenentities isformed andagreementsare

ast to fulll a partiular servie. This onept is based on thetrust that the agreement will befullled. If

omputing was experimented with bythe Java CoG projet in 1997 in ahigh-throughput strutural biology

projet. Resoureswereput togetherin apooland ifaresourefailed toreportortheaveragetimetakenby

other resoures to respond was above a threshold, that resourewasmarkedas unfavorable and was hosen

onlyifnoother resoureswereavailable. Inotherwords,theresoureobtainedaertain reputationbasedon

itsontratualfulllment.

3.4. Ownership as Trust Model. Highperformane omputinghastraditionally fousedon ownership

models. Suhmodelsareanextensionoftheommunitymodelinwhih,however,theownershipofanexpliit

entityformsaommunity. Inthe80sand 90sthesemodelsweredrivenbysuperomputerentersthatoered

theirusersexlusiveuseofsuperomputersthroughbathqueuingsystems. Today,inGrid,theownershipmodel

isthemostommonone. Webelievethatin future,however,wewill seeashifttowardvirtualownerships (as

alreadypromoted by theonept ofvirtualorganizations). Not onlywill we seevirtual organizationsbut we

willalsoseesoonvirtualmembershipsto theseorganizations.

ToapplytheoneptofownershiptoommunityGrids[21℄,onemustrevisittheroleofvirtualorganizations,

institutions, and membersreatingthem. Sinesharedresouresin avirtualorganization areontributed by

various institutions, an elaborate reputation servieis needed, that dealswith the fat that resouresanbe

part of multiple domains and VOs. The dierent ases are depited in Figure 3.1. We use the following

nomenlature:

n

_E

i

denes an entity with the label

i

that is shared by

n

organizations. Inase we do know

aperentageof share,weaugmentit appropriately

p

₁

...p

n

_E

i

where

p

k

denes the perentage of ownership of

organization

k

. Consideringthis nomenlature, weandene use of entities based onthe reputation entities

obtain. Wenotethatentitieswithinorganizationsanevaluateeahother. Tomakethesystemwork,however,

weneedtodeneavalue-basedsystemarosstheorganizationsormaintainreputationfordierentommunities

andvirtualorganizations.

Fig.3.1. Institutionsontributeinvariouswaystheirresouresandserviestopossiblyvariousvirtualorganizations.

3.5. UseasTrustModel. Oneofthesimplesttrustmodelsisbasedonthenumberofuses. Theonept

isthefollowing: themoretheentityisused,thehigherthetrustinthisresoure.Commonsensesuggeststhat

when so many pereive this entity as desirable, it must be so. Use statistis have longbeen popularin the

omputerindustry,althoughtheseoftengivearstimpressionofwhihentitiesshouldbeonsidered,onemust

makesurethat the oneptof popularityis independentof other attributes suh asseurity orevenontent.

OneneedonlyonsiderpopularbutinseureoperatingsystemsonWebpageswithdubiousontentappreiated

byalargenumberofInternet usersthathaveahievedmorepopularitythantrueontentdrivenpages.

3.6. Branding as Trust Model. Oneotherimportantoneptinindustrythatisrelatedto reputation

isbranding. Herethereputationofontinuouslyhighreommendedentitiesthatbelongtoapartiularlassor

Inomputersienetheoneptofbrandingisalsooftenusedinregardstoorganizationsandprodutsderived

fromtheseorganizations.

3.7. Time as Trust Model. Time isanessentialvariableaspartofeahtrustmodel. Trustand

repu-tationmodelshavesometimesawidevarietyofpotentialonitingtimeassumptions. Wehavebrandingthat

learlyaugmentsanentity withareputationthat islesstime sensitivethanestablishingshort termontrats

betweenentitiesthatonlydealwithonetimeinterations. Asimilaronepttobrandingissenioritywithtime

in whih theassumedentitybeomesaseniorityvaluethat isbasedonexperienegained throughinteration

withtheommunity. StatementssuhasI havedoneitthiswayforyears,itmustthereforebeworkingforthe

upomingyearsareommon.

3.8. Eonomy as Trust Model. In orderto establishabetterreevaluation methodology,trustmodels

anbe augmentedthrougheonomimodels. Forexample,ontratsanbesignedunder exhangeof realor

virtualmoney,useanberewardedthroughaouponsystem,andautionsormarketsanbeputinplaetobid

forthemosttrustedandapableresoures.Thisapproahnaturallyansueedonlyifaommon,ontrollable

ommoditysuh as(virtual)moneyis used. Businessandeonomiresearhin theseareasisplentiful;indeed

thetermvirtualizationin businessmodelslongbefore theGridommunityusedtheseterms[17℄.

3.9. ReputationasTrust Model. Asindiatedearlier,reputationanbeusedasamajorenhanement

to eahof the models introdued. Sine reputation denes ametri, weshould be ableto use this metrito

seletentitiesfor loseronsiderationaspartof aneighborhood, ommunity, orvirtualorganizationandhelp

support models employingeonomi goals, usage, and to establish ontrats. This is of espeial importane

beausethetimeittakestoqueryallavailableentitiesforthebestpossibletmaybetoolarge. Heneweneed

togroupalass ofpropertiesofinterestto apartiularommunityandpreseletfrom themanythousandsor

millionsthosethatgivethehighestlikelihoodofsuess.

4. AppliationofReputationRelatedTrustModels. Trustmodelsanduseofreputationframeworks

has been onsidered in a wide variety of systems. The most visible frameworks have been used to enhane

businessandinformationserviesavailableforalargeommunitythroughtheInternet.

4.1. Review Trust Model. One popular use to establish reputation is to design information portals,

similartoC|net[8℄,whihmaintainsratingsforprodutsbasedontheratingsofaneditor. Integratingfeedbak

fromtheommunityprovidesanadditionalvalueinordertoreevaluatethejudgmentoftheeditoragainstinput

fromalargerommunity. Although,theommunityfeedbakisnotintegratedinto theeditors ratingitisstill

availableforreview. Hene,theonsumermustreviewbothpieesofinformationtoobtainanauratepiture.

Detailedtextual reviewsarealsoprovidedto providetheonsumerwithasemantiexplanationonthereason

forthegivengradebyanother onsumer. The advantages ofintegratingaommunity arethat thebiasof an

editormaybeminimized. Thedisadvantageisthatinvalidresponsesnotorrespondingtotheeditorsstandard

ouldresultin aninorretevaluation.

4.2. BuyersandSellersReputationTrustModel. TheonlineautionsystemeBay[9℄isanimportant

exampleof suessful reputation management. In eBay's reputation system,buyersand sellers anrate eah

other after eah transation. The feedbak systemis basedon asimple pointsystem, that assigns apositive

pointforapositivefeedbak,Nopointsforneutralfeedbak,andanegativepointforanegativefeedbak. The

reputation is the summation of all feedbaksfor a buyeror seller overthe last six month. Additionally, the

feedbakis lassiedin adetailed viewto begroupsin time periods of thepast7days,the pastmonth,and

thepastsixmonth. E-baypointsouttheahighfeedbakratingnotneessarilymeansagoodreputation. Itis

agood sign, but aonsumershouldalwayshekamember'sfeedbak proleforanynegativeremarks. It's

best notto judgeusersonlyontheirfeedbakratings.

4.3. Information Ranking. The searh engine Google [6, 15℄ provides a reputation and trust model

basedonamethod alled PageRankthatusesthelinks betweenpagesasinput. Here alink from otherpages

tothepageinquestionis interpretedas apositivesignandindiates thatthepagehassomeimportane. The

modelisbasedontheoneptthat themorelinksanbefoundthemoreimportantthepageis. Additionally,

itweighsthepagesbasedontheimportane ofthevotingpage.

work. TheGridEigenTrust algorithmisinherentlybasedonthepeer-to-peer(P2P) EigenTrustalgorithm [16℄

and the use of reputation to dene evolving and managed trust in Grids through the introdution of global

trust [1℄. The GridEigenTrust algorithm ombines these algorithms making it onduive for a large Grid

environmentbyinreasingitssalability.

5.1. EigenTrust Algorithm for P2P Networks. A reputation management algorithm for P2P

net-works, alled EigenTrust, isintrodued in [16℄. Wesummarize themain priniplebut use within this setion

thetermentityinsteadofpeerinordertoprovideauniformnomenlature. Everyentity

E

i

ratesotherentities

basedon the qualityof servie theyprovide. Therefore, everyentity

E

j

with whom

E

i

had business will be

ratedwithagrade

g

ij

(

i

g

ij

→

j

)andisnormalizedasdesribedin[16℄. Hene,foreahentity

E

j

,thenormalized

loaltrustvalue

c

ij

is denedasfollows:

c

ij

=

max(g

ij

,

0)

X

j

max(g

ij

,

0)

(5.1)

ThenormalizedloaltrustvaluesthroughouttheP2Pdomainneedstobeaggregated. Thisproedurean

be doneby means of a transitive trust mehanism: entity

E

i

asks his friends for their opinions about other

entities:

t

ij

=

X

k

c

ik

c

kj

(5.2)

where

t

ij

representsthetrust that entity

E

i

puts in entity

E

j

based ontheopinion ofhis

k

friends. The

oeientsareassembledinto amatrix,

C

= [c

ik

]

. Hene,equation(5.2)anbewrittenin matrixnotationas

shownin equation(5.3):

~

t

i

=

C

T

c

~

i

(5.3)

Theproessofobtainingthetrustvaluesoffriendsisrepeatedtoobtainthetransitivelosureofthematrix.

After

n

iterations,where

n

istherankof thematrix,thetransitivetrustisobtained. Forlarge

n

,

~t

i

onverges

rapidly asshownin [12℄, to thesame value

~t

. Hene,

~t

showshowmuh trustthe systemasawhole hasfor

everyentity

E

i

.

5.2. ManagingReputationinGridNetworks. In[1,14℄severalaspetsoftrustvaluesareonsidered

as partof aglobal reputation model. In this model it is assumed that the trust values deay with time. It

isalso assumedthatthetrustmodel shouldstimulate organizationstosantionentitieswhoare notbehaving

onsistentlyintheGridenvironmentandwhobreaktrustrelations. Finally,itisassumedthattrustrelationships

arebasedonaweightedombinationofadiretrelationshipbetweendomainsandtheglobal reputationofthe

domains. The model is also based on ontexts that, in Grids, an be numerous, varying from exeuting a

spei job, to storinginformation, downloadingdata, andusing thenetwork. Toreet moreauratelythe

terminologyoftheGrid,wereplaethetermdomainwithorganization. Webelievethatthedomainisnotan

appropriatedivisionfortrustwithin Grids.

Ourgoalistodeneaformulaforthetrustrelationshipfuntion

Γ

,basedontheparameterstime,ontext,

andtheorganizationsinvolved.

•

Let

O

i

and

O

j

denotetwoorganizations.

•

Let

Γ(O

i

, O

j

, t, c)

denoteatrustrelationshipbasedonaspeiontext

c

atagiventime

t

of

O

i

toward

O

j

.

Nextwedene

Γ

withthehelpofthefollowingfuntions:

•

Let

Θ(O

i

, O

j

, t, c)

denoteadiretrelationshipfortheontext

c

attime

t

of

O

i

towards

O

j

,whihisthe

relationshipbetweenneighboringorganizationsthathavediretrelationshipsbetweenentitiesinboth.

•

Let

Ω(O

j

, t, c)

denotetheglobalreputationof

O

j

fortheontext

c

attime

t

.

•

Let

DT T

(O

i

, O

j

, c)

denoteadirettrusttable entryof

O

i

for

O

j

forontext

c

. Thetablereordsthe

•

Let

Υ(t

−

t

ij

, c)

denotethedeayfuntion forspei ontext

c

, where

t

isurrenttimeand

t

ij

isthe

timeofthelastupdateofDTTorthetimeofthelasttransationbetween

O

i

and

O

j

.

In[1,14℄,

Γ(O

i

, O

j

, t, c)

isomputedastheweightedsumofdiretrelationshipbetweendomainandglobal

reputationofthedomain:

Γ(O

i

, O

j

, t, c) =

α

·

Θ(O

i

, O

j

, t, c) +

β

·

Ω(O

j

, t, c)

(5.4)

where

α, β

≥

0

,

α

+

β

= 1

.

Thediret relationshipisaeted bythetimeelapsedbetweeninterdomainontats,hene

Θ(O

i

, O

j

, t, c) =

DT T

(O

i

, O

j

, c)

·

Υ(t

−

t

ij

, c)

(5.5)

Theglobaltrustfordomain

O

j

isomputedas

Ω(O

j

, t, c) =

n

X

k

=1

R(O

k

, O

j

)

·

RT T

(O

k

, O

j

, c)

·

Υ(t

−

t

kj

, c)

n

X

k

=1

(O

k

)

(5.6)

where

R(O

k

, O

j

)

is thereommender'strust level,and RTTis usually equaltoDTT. Sine reputation is

basedprimarily on what organizations say aboutanother domain, the reommender'strust fator

R(O

k

, O

j

)

is introdued to preventheating throughollusion among agroup of domains. Hene,

R(O

k

, O

j

)

is avalue

between0and1and will haveahigher valueif

O

k

and

O

j

areunknown orhavenopriorrelationshipamong

eahotherandalowervalueif

O

k

and

O

j

arealliesthrough,forexample,avirtualorganizationrelationship.

6. GridEigenTrust Framework. In this setion we introdue more details about our proposed

Grid-EigenTrustframework. Webeginbypointingoutsomeofthelimitationsofthetwootherapproahesdisussed

inSetion5. Then,weshowhowonean buildamoreadvanedframeworkbyombiningthetwoapproahes,

whileavoidingtheirlimitationswhileappliedtotheGrid.

Theeigenvalueapproahdisussed in5.1 isexpliitlydesignedforP2P networks. It hasnotbeenapplied

totheunderlyingarhitetureofGridsthat introduevirtualorganizations,providinganobviouslassiation

of resoures,users,and theirreputation that is neededto establishsalability. Theapproahdisussed in [1℄

has several limitations. First, as already pointed, the use of the term domain is not appropriate for Grids.

Hene wehavemodiedthe original formulationasshown in Setion 5.2. Seond, in ase ofa largenumber

oforganizations,it willbeostlyto omputetheglobaltrust(Equation 5.6)beausewewill havetoonsider

all relationshipsto inrease auray. To improvesalability, onean ompute the global trustamong aset

of neighbors; however, suh a omputation would represent only trust between neighbors but not a global

trust value. Third, the authors suggest in their study limiting the number of ontexts on. Speially, the

authors redued the number of ontexts in the study to only three: printing, storage, and omputing. In

Grid environments, however, we deal with many more ontexts. An example is the evaluation of trust and

reputation for network harateristis, an essential part of any Grid infrastruture. Fourth, the funtion

Υ

,

whih depends on the duration of the interation between two organizations, must be hosen arefully. We

believethat forontextssuhasle transfer,atimedeayfuntionmayhavetobehosenfarlargerthanthe

longestle transferto beonsidered,otherwise thedeay funtion may invalidate thereputation even before

thetransation is ompleted. Hene,it will be neessaryto introdue lassesof similarontext, for example,

forletransferswithdierentnumbersofbytes. Anotherlimitationis thatin theaseofnetworkstheatual

speedbetweenresouresouldvary,makingitevenmoreomplexto obtainthepropertrustvalues.

Wedesign anew algorithm, alled Grid EigenTrust, that overomessomeof the limitationsof these two

approahes. WeapplytheEigenTrustalgorithmexplainedinSetion5.1toaddresstheproblemsofsalability

Fig.6.1. Exampleofadistributionofreputationmanagementframeworkbased onreputationserviesina Grid.

7. GridEigenTrust Algorithm. We address the omplexity issue by introduing a set of reputation

serviesarrangedinhierarhialgraphs. Toillustratethispoint,weonsiderthesenarioshowninFigure6.1.

Inthis senario,twoVOsare depitedontainingtwoorganizationseah. Eah organization hasaset of

entities. Hene,wehaveintroduedanimpliithierarhybasedonentities,institutions, andvirtual

organiza-tions. Weassign areputation to the entities in thelowest level. Based onthe reputation ofthe entities,the

reputation of theorganization gets updated. Finally weomputethe reputation of avirtual organizationby

using thereputation values of all the organizations that belong to the virtual organization. Our reputation

servieanbereusedandintegratedineahlevelofthehierarhy.

The number ofreputation serviesneeded fora virtualorganizationorinstitution mayvary basedon its

impliitsize, determinedby theentitiesand the hierarhythey dene. Eah reputation servieis responsible

forasubsetofentitieswithinthehierarhy. Thereputationserviesomputethereputationinaollaborative,

but distributed,fashion. Under theassumption thattheinterhange ofreputation dataisseure andannot

ompromised,andthetimeintervalthatadatumisvalidislongerthantheSmallestupdate,itmaybepossible

to distribute previousreputationvaluesfrom entities in thenetwork in orderto redue the network overhead

for lookups through a simple ahing mehanism. In order to alulate and maintain the reputation, eah

reputationservieusestheGridEigenTrustalgorithmdesribedinthenextsetion. Toguaranteeauray,the

reputationserviesmustexhangemessageswitheahotherinaseurewayandthesemantisofthereputation

serviemustbeseured throughaserviesignaturethatanbeusedto learlyidentify wether theserviehas

beentamperedwith.

7.1. CalulatingTrust. TodesribeourGridEigenTrustalgorithm,weusethenotationusedin Setion

5.2. Tosimplifyourdisussion,weassumeeahentityisinonlyone organization(ompareSetion3.1).

Weestablishatrustvalueforeahentitybasedonvariousontextsitsupportswithin anorganization. We

usethetermorganizationtrust toreferto atrustvalueforeahorganization.Organizationtrust diersfrom

other ontext trustin that it agglomerates several ontext trust valuesto a singleone. It reets ageneral

opinionofthereliabilityofanorganizationtoprovideaurateinformationonwhatresouresthisorganization

supplies. As aresult, areliability trustbetweenorganizations anbealulated quikly to obtainthe global

trust. Althoughthis strategysoundsinitially ounterintuitive,itis oftenusedin aneonomimodelbasedon

thetrustmodelthroughbranding.

By ombining organization trust and the trust level of an entity within an organization (for a spei

ontext

c

attime

t

),wederiveareliabletrustvalueforthegivenentity. Weapplytheeigenvetormathematial modelto omputetheglobalreputation ofanorganization. Currently,weomputethereputationof avirtual

organizationas weightedsumofthereputationsofallorganizationsthatbelongtothevirtualorganization.

an organization), we omit the rstparameter in thefuntion speiation

Θ

, whih denotes theentity from

whih thetrustvaluewasobtained.

Allentities thatuseresouresorollaboratewithuserswithin anotherorganizationgradethequalityand

reliability of the requested entity. The overall gradeof the entity is established as the weighted sum of the

previousgrade(whihdeayswithtime)andthenewgrade. Itisalsoimportanttoonsiderhowmuhwetrust

theorganizationfromwhihtheremoteentity(i.e., entitythat givesthegrade)originatesitsrequests.

If

Θ

p

(E

i

, t

i

, c)

isthepreviousumulativegradeestablishedat time

t

i

forentity

E

i

within ontext

c

, then

g

ij

(t, c)

isanewgradegivenbyentityfromorganization

O

j

,and

T

(O

j

)

,thenreliabilitytrustleveloforganization

O

j

,istheoverallnewumulativegrade. Then,

Θ(E

i

, t, c)

anbealulatedas

Θ(E

i

, t, c) =

α(c)

·

Θ

p

(E

i

, t

i

, c)

·

Υ(t

−

t

i

, c) +

β(c)

·

T

(O

j

)

·

g

ij

(t, c)

α(c) +

β(c)

(7.1)

where

α(c), β(c)

≥

0

.

Equation7.1issimilartoEquation5.5fromSetion5.2. However,theparameters

α(c)

and

β(c)

reetthe

ontextimportaneofthelatestgradetheentityreeived.

IfanorganizationjustjoinedtheGrid,theinitialtrustvalueswillbeset toalowinitialvaluebeausethe

trustmustbeearnedrst. However,iftheentityforwhihweassignthetrustissuientlysimilartoothersin

thealreadyexistingGrid, aninitialvalueanbeobtainedfrom theseintegratedentities. Wehose thelowest

trustvalueandaddaspenaltyalinearorretionfuntion.

Let

Θ

0

(E

i

, t

0

, c)

denote theinitial trustvalueforanentity

E

i

within ourorganizationforaontext

c

. Let

Θ(E

i

, t

i

, c)

denote the umulative reputation value gathered from other entities (dened by equation (7.1)).

Thentheinitialtrustoftheentityistheweightedsumofthese twovalues:

Γ(E

i

, t, c) =

γ(c)

·

Θ

0

(E

i

, t

0

, c) +

δ(c)

·

Θ(E

i

, t

i

, c)

γ(c) +

δ(c)

(7.2)

where

γ(c), δ(c)

≥

0

.

7.1.2. Calulating the Reliability Trust between Organizations. Thereliabilitytrustof

organiza-tion

O

i

towardorganization

O

j

reetstheopinionoforganization

O

i

aboutthequalityandtrustworthinessof

informationorganization

O

j

supplies. Therefore,besidesmaintainingindividual ontexts,weintrodue global

ontext (ompareSetion 5.2). We usea similarnotation asin Setion 5.2, but we omitthe parameter

c

. If

wehaveaprioriknowledgeabouttheinitialtrustinformation,weassignthisvalueatinitializationtimeofour

algorithm.

Let the initial value of trust be represented as

C(O

j

)

. Reliability trust should be obtained through the

weightedsumofdiretexperieneandglobaltrustvalueoforganization

O

j

.

Diret experiene anbe alulatedin thesamewayasin equation 7.1. Itis anormalizedweightedsum

between

C(O

j

)

,theumulativegradefromthepreviousperiod

Θ

p

(O

i

, O

j

, t

ij

)

and thenewgrade

G(t)

.

Users within organization

O

i

grade the reputation of a ertain entity

E

j

within organization

O

j

with

grade

Φ(E

j

)

. Also, organization

O

j

advertises thequality-of-servieof this entity with grade

∆(E

j

)

. Then,

organization

O

i

willgradereliabilityofinformationgivenbyorganization

O

j

withgrade

G(t)

. Fordetermining

grade

G(t)

wehavethreeases:

•

If

Φ

∈

[∆

−

ǫ,

∆

−

ζ]

,thenewgrade

G(t)

is1.

•

If

Φ

>

∆

−

ζ

,thenewgrade

G(t)

isbiggerthan1.

•

If

Φ

<

∆

−

ǫ

,thenewgrade

G(t)

islessthan1,dependingonhowmuhthe

Φ

diersfrom

∆

Diret experiene that organization

O

i

has with

O

j

at sometime

t

,

Θ(O

i

, O

j

, t)

anbealulated in the

same way as in equation 7.1. It is a normalized weighted sum between

C(O

j

)

, umulative grade from the

previousperiod

Θ

p

(O

i

, O

j

, t

ij

)

andthenewgrade

G(t)

.

Θ(O

i

, O

j

, t) =

α

·

C(O

j

) +

β

·

Θ

p

(O

i

, O

j

, t

ij

)

·

Υ(t

−

t

ij

) +

γ

·

G(t)

α

+

β

+

γ

(7.3)

Global reliability trustoforganization

O

j

,

Ω(O

j

, t)

annowbealulatedwith theEigenTrust algorithm

explainedinSetion5.1. Ifwereplae

c

ij

with

Θ(O

i

, O

j

, t)

inSetion5.1,weobtainamatrix

C

= [Θ(O

i

, O

j

, t)]

,

and initial vetor

T

~

0

=

t

0

(i)

,

t

0

(i) =

C(O

i

)

. Nowwe haveall the ingredients to apply a poweriteration for

omputingtheprinipal eigenvetorof

C

T

, whih representsglobalreliabilitytrustvaluesfororganizationsin

Grids.

Weansummarizethebasistepsofthealgorithmasfollows:

Entity

E

i

withinorganization

O

1

wantstouseentity

E

j

within organization

O

2

intheontext

c

attime

t

.

•

Considerthereliabilitytrustof

O

2

omputedusingtheEigenTrustalgorithm,

Ω(O

2

, t)

.

•

Ask

E

i

about

Γ(E

j

, t, c)

,thetrustvalueoforganization

E

j

within organization

O

2

.

•

Inalulating theoveralltrust valuefor entity

E

j

, in formula(5.4) replae

Ω(E

j

, t, c)

with

Ω(O

2

, t)

·

Γ(E

j

, t, c)

•

Computetheoveralltrustfortheentity

Γ(E

i

, E

j

, t, c)

withformulas(5.4)and(5.5).

Afteromputingthetrustvalues,weanomparethemtosuggesttheresourewiththehighestreputation.

Modiations, suh as the introdution of a statistial seletion algorithm based on random variables, are

possible.

Thisombinedapproahhasseveraladvantages. First,thealgorithmonvergesrapidlyandintroduesless

overhead thanomputing global trustvalues forindividual entities within everyontext. One of thereasons

isthat thenumberofvaluesforomputation isnottoolargebeauseweareomputing globaltrustvaluesof

organizationsthrough hierarhies,not an overallpool of individual entities. Seond, organizations will make

aneorttoreportauratetrustinformationabouttheirentitiesbeausewronginformationwillbepenalized,

loweringtheglobaltrustoftheorganization.

8. ReputationServie Arhiteture. Thearhitetureofanindividualreputationservieisshownin

Figure 8.1. It onsists of a olletion manager, omputation manager, storage and olletion manager, and

reporter. Theolletion manageris responsiblefor evaluatingthe quality statementdesribingthe requested

reputation, and olleting relevant data from the entities suh asresoures and users. It givesthe olleted

datatotheomputationmanager. Theomputationmanageromputesthereputationvaluesofentitiesbased

on the ontext speied and gives the result to the storage manager, whih stores the values to maintain a

globalandhistorialview. Thereporterontatsthestoragemanagertoreportthereputationvalueswhenever

queriedbysomeentityintheGrid.

Fig.8.1.Arhitetureofareputationservie.

Hene,whenanappliationsubmitsarequestforaservieastinaqualitativestatementtothereputation

servie,thereputationservieevaluatesthestatementandomputesthereputationforalltheentitiesproviding

therequiredservieusingtheheuristisexplainedinSetion7.1. Itontatsotherreputationserviesifrequired

and returnstheinformation regardingthe serviesand theirreputation bakto the requester. Therequester

9. Conlusion and Future Work. In thispaper,wehavedesribed aframework foralulating

repu-tationinGrid-basedsystem. Thepaperwasmostlyfousedonissuesthathavetobeaddressedwhileworking

towardGrid serviesthat integratereputation oneptsinto their funtionality. Wehaveidentied several of

theseissues. Seondwehaveexperimentedwithanarhitetureandalgorithmtogainexperienewiththisnew

areaofresearhfortheGridommunity. Wehaveidentiedaframeworkandalgorithm,thatisaombination

ofotherresearheorts. Theunderlyingalgorithmisbasedonintroduingaglobaltrustvaluethatisupdated

withaneigenvaluebasedtrustalulationalgorithm. Atpresentweareenhaningandevaluatingourframework

byintroduingavarietyofreputationmeasurementsthatare ontrolledthroughadaptiveparameters.

Aknowledgment. ThisworkwassupportedbytheMathematial,Information,andComputational

Si-ene Division subprogram of the Oe of Advaned Sienti Computing Researh, Oe of Siene, U.S.

Department of Energy, under ContratW-31-109-Eng-38. DARPA, DOE, and NSF support Globus Alliane

researhand development. The JavaCoGKit Projetis supported by DOE SiDACand NSF Alliane. We

thankDr. PaulE. Plassmanand KaizarAminfor detailed and insightfulomments onthepaper. The

algo-rithmdesribedin thispaperwasrstpresentedbyGregorvonLaszewskiin theWorkshop onAdaptiveGrid

Middleware, in New Orleans, LA, September 28, 2003. A draft publiation of partof thematerial that was

presented isauthored by B.K. AlunkalI. Veljkovi,G. vonLaszewski, and K.Amin. This draftis available

fromhttp://www.ms.anl.gov/gregor

REFERENCES

[1℄ EvolvingandManagingTrustinGridComputingSystems,HotelFortGarry,Winnipeg,Manitoba,Canada,May12-152002,

IEEE Computer SoietyPress. Available fromWorld WideWeb: http://www.s.mgill.a/~ anrl /PUB S/ ee 200 2_

farag.pdf.

[2℄ R. Al-Ali, K.Amin, G. von Laszewski, O. Rana,andD. Walker,An OGSA-based Qualityof Servie Framework,

inGCC2003,Shanghai,2003. AvailablefromWorldWideWeb:

http://www.ms.anl.gov/~g reg or/p aper s/v onLa szew ski --q os. pdf.

[3℄ amazon.Webpage. AvailablefromWorldWideWeb: http://www.amazon.om.

[4℄ K.Amin,M.Hategan,G.vonLaszewski,A.Rossi,S.Hampton,andN.J.Zaluze,GridAnt: AClient-Controllable

GridWorkowSystem,in37thHawai'iInternationalConfereneonSystemSiene,IslandofHawaii,BigIsland,5-8Jan.

2004. AvailablefromWorldWideWeb: http://www.ms.anl.gov/~ greg or/ pape rs/v onL asze wski --grid ant -hi s.p df.

[5℄ F.Berman,G.C.Fox,andT.Hey,eds.,GridComputing: MakingTheGlobalInfrastrutureaReality,Wiley,2003.

[6℄ S. Brinand L. Page, TheAnatomy of a Large-Sale Hypertextual WebSearh Engine, Computer Networks and ISDN

Systems,30(1998),pp.107117.AvailablefromWorldWideWeb:http://www-db.stanford .edu /~b akr ub/g oog le.h tml.

[7℄ Internetworkingtehnologyhandbook,qualityofservie.WebPage,visitedDe.2004. AvailablefromWorldWideWeb:

http://www.iso.om/univ er d/ /td/ do /is intw k/i to_d o/ qos. htm.

[8℄ Cnet. WebPage.AvailablefromWorldWideWeb: http://www.net.om.

[9℄ ebay. Webpage. AvailablefromWorldWideWeb: http://www.ebay.om.

[10℄ I. Foster and C. Kesselman, eds., The Grid 2: Blueprint for a New Computing Infrastruture, Morgan Kaufmann

Publishers,De.2003.

[11℄ D.Gambetta,ed.,Trust,Blakwell,1990,h.Chapter4:TrustasaCommodity,pp.4972.

[12℄ T.H. Haveliwalaand S.D. Kamvar,TheSeondEigenvalue of theGoogle Matrix. Webpage. AvailablefromWorld

WideWeb: http://www.stanford.edu/ ~sdk amv ar/p aper s/s eon deig env alue .pd f.

[13℄ W.Heisenberg,Uber quantentheoretisheumdeutungkinematisher undmehanisher beziehungen,ZeitshriftfrPhysik,33

(1925),pp.879893.

[14℄ TheInternationalAssoiationforComputersandCommuniations,IntegratingTrustintoGridResoure

Manage-mentSystems,Vanouver,B.C.,Canada,Aug.18-212002,IEEEComputerSoietyPress. AvailablefromWorldWide

Web: http://www.s.umanitoba. a/~ anrl /PU BS/i pp2 002 _far ag.p df.

[15℄ S.D.Kamvar,T.H.Haveliwala,andG. H.Golub,AdaptiveMethodsfortheComputationofPageRank. Webpage.

AvailablefromWorldWideWeb: http://www.stanford.edu/ ~sdk amv ar/p aper s/a dapt ive. pdf .

[16℄ S.D.Kamvar,M.T.Shlosser,andH.Garia-Molina,Theeigentrustalgorithmforreputationmanagementinp2p

networks,inTwelfthInternationalWorldWideWebConferene,2003,Budapest,Hungary,May20-242003,ACMPress.

AvailablefromWorldWideWeb: iteseer.nj.ne.om/arti le/ kam var0 3eig ent rust .htm l.

[17℄ A.Mowshowitz,Virtualorganization: avisionofmanagementintheinformationage,TheInformationSoiety,10(1994),

pp.267288.

[18℄ G.vonLaszewskiandK.Amin,MiddlewareforCommniations,inGridMiddleware,Wiley,2004,pp.109130.Available

fromWorldWideWeb: http://www.ms.anl.gov/ ~gre gor /pap ers/ von Lasz ewsk i--gri d-m idd lewa re.p df.

[19℄ G.vonLaszewski,S.Fitzgerald,I.Foster,C.Kesselman,W.Smith,andS.Tueke,ADiretoryServiefor

Con-guringHigh-PerformaneDistributedComputations,inProeedingsofthe6thIEEESymposiumonHigh-Performane

DistributedComputing,5-8Aug.1997,pp.365375.AvailablefromWorldWideWeb:

http://www.ms.anl.gov/~g reg or/p aper s/f itzg eral d--hpd 97 .pdf .

24-26July2002,pp.333342.AvailablefromWorldWideWeb:

http://www.ms.anl.gov /~gr egor /pa pers /von Las zews ki--in fogr am. ps.

[21℄ G. von Laszewski and P. Wagstrom, Gestalt of the Grid, in Tools and Environments for Parallel and Distributed

Computing,SeriesonParallelandDistributedComputing,Wiley,2004,pp.149187. AvailablefromWorldWideWeb:

http://www.ms.anl.gov /~gr egor /pa pers /von Las zews ki--ge stal t.p df.

[22℄ G.vonLaszewski,M.Westbrook,I.Foster,E.Westbrook,andC.Barnes,UsingComputationalGridCapabilities

toEnhanetheAbilityofanX-RaySoureforStruturalBiology,ClusterComputing,3(2000),pp.187199. Available

fromWorldWideWeb: http://www.ms.anl.gov/~ greg or/p ape rs/v onLa sze wski --dtre k.pd f.

Editedby: WilsonRivera,JaimeSeguel.

Reeived: July8,2003.