Fuzzy-Logic Based Self Adaptive Grid Architecture

8/6/2019 Fuzzy-Logic Based Self Adaptive Grid Architecture

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JOURNAL OF COMPUTING, VOLUME 3, ISSUE 6, JUNE 2011, ISSN 2151-9617

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Fuzzy-Logic Based Self Adaptive GridArchitecture

Ashiqur Md. Rahman, Roksana Akter, and Rashedur M Rahman

AbstractGrid computing is a framework to meet the growing computational demands and offers the network of large scale

computing resources. This paper presents a survey to generalize the fuzziness in various sectors of Grid computing and

summarize research challenges. The Fuzzy Grid improved the efficiency of probabilistic interpretation of several Grid features.

Not all the Grid architectures provide same benefits for users in utilizing the resources. A thorough overview of Fuzzy-logic

based self adaptive Grid architecture with secure fault tolerant job scheduling, file replication and intelligent routing is studied in

this survey.

Index Termscaching, fuzzification, particle, path goodness, route goodness, security demand, trust index.

1 INTRODUCTION

RID computing [1] is an emerging technology that

focus on uniformly aggregating and sharing distributed heterogeneous collection of autonomous systems, resources geographically distributed and interconnectedby low latency and highbandwidth networks forsolving largescale applications in science, engineeringand commerce [2]. In a largescale grid, distributed resourcesbelongtodifferentadministrativedomains.DataGrids provides infrastructure for whom accessing, transferringandmanaginglargedatasetsstoredindistributedrepositories [3][4] that leads to a more decentralized approachtoaddresstheproblemofcomputingpower. Research drivenby this has promoted the exploration of anew architecture known as The Grid for high performancedistributedapplicationsystem. ThetermGridisdrivenfromananalogytotheelectricalpowersupply inthesensethatithaspervasiveaccesstothepowerandcandraw any resources from the distributed resource pool.Thus, a household draws electricity from power socketsirrespective of their physical location and the location ofaccesspoints[5].

Grid computing can coordinate resource sharing andproblem solving acrossdynamic multiinstitutional environments. High performance Grid architectures facilitatethese requirementsby applying the various technologiesrequired in a coordinated fashion to support data intensivepetabytescaleapplication.Thispaperdiscussesvarious methods of using fuzzy logic in different sector of

Grid architecture. Fuzzy logic [6] hasbeen successfullyapplied to manyareas such as control,scheduling, replicationetc.ThedevelopmentoffuzzygridsysteminvolvesacquiringIFTHENrulesthroughcongregationtheexpert

autonomous grid system.A key motivation of this paper

istoaggregatetheavailablefuzzytechnologiesandmoreimportantlythetheoryoffuzzinesstoarticulateaFuzzyGridinfrastructure.Classicalexpertsystemsemulatethereasoning process on a static trusted Grid environment.However, the method of handling imprecision mustbeexcellent for an expert system to measure the naturalprobabilistic perception accurately. This new feature isachievable into the Grid architectureby introducing fuzziness. The major areas for implementing fuzziness onGrid computing are, fuzzy trust integration for securityenforcement onJob Scheduling using Particle Swarmalgorithm, NeuroFuzzy hybrid negotiation model forresource allocation, and Fuzzy Replica Placement Strategies,etc.

Heterogeneousdatasources,most of thegridservicesthatareavailablearedesignedsuchawaythattheymustbe identical in schema definition for their smooth operation whereas there canbe situation where the grid sitesarealsoheterogeneous.Soitisimportantforsuchheterogeneous distributions of data are to be classified withmaximumsatisfactionwithrespecttoallconstraints.Section 2 describes the grid architecture forwarded withfuzzy trust integrated fault tolerant grid architecture forsecurity enforcement on resource allocation in Section 3.Section4illustratestheoptimizationofgridresourceallocation using NeuroFuzzy hybrid negotiation model.Fuzzyreplicareplacementalgorithmforoptimizingaver

ageresponsetimeisexplainedinSection5.Fuzzyroutingtuneup for dynamic maintainability is discussed in Section 6. Section 7 contains conclusion and provide futuredirection.

Motivation of this work is to generalize fuzzy gridconceptandhighlightongoingresearchinthisemergingarea. XMLbased technologies are involving in interoperability issues,whereaswearefindingsomeconceptsbywhich we can provide common specifications on fuzzygrid.

Ashiqur Md. Rahman is with the Department of Electrical Engineering &Computer Science, North South University, Bangladesh.

Roksana Akter is with the Department of Computer Science & Engineer-ing, University of Dhaka, Bangladesh.

Rashedur M. Rahman is with the Department of Electrical Engineering &Computer Science, North South University, Bangladesh.

G

2011 Journal of Computing Press, NY, USA, ISSN 2151-9617

http://sites.google.com/site/journalofcomputing/


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2 GRID ARCHITECTURE

Grid architecture continues to evolve as the overall

design concepts continue to improve and as it is em

ployedforadditionaltasks.However,becauseGridarchi

tecture is highly flexible, Grids have alsobeen adopted

for use by many other, less computationally intensive,

application areas. Today, many types of Grids exist, andnewGridsarecontinuallybeingdesignedtoaddressnew

informationtechnologychallenges.Gridscanbeclassified

invariousways,forexamplebyqualitiesofphysicalcon

figuration, topology, and locality. Grids within an enter

prisearecalledintragrids,interlinkedGridswithinmul

tipleorganizationsarecalled intergridsandGridsexter

nal to an organization are called extragrids. Grids can

haveasmallorlargespecialdistribution,i.e.,distributed

locally, nationally or worldwide. Grids can also been

classified by their primary resources and function, for

example computational Grids provide for high

performance or specialized distributed computing.Gridscan provide modest scale computational powerby inte

gratingcomputingresourcesacrossanenterprisecampus

or largescale computation by integrating computers

acrossanationsuchastheTeraGridintheUSA[3].Thebreadthandextensibilityofmultipleheterogene

ous resource types motivate the creation of the multi

tiered architecture shown in Fig. 1. The first tier contri

butesavirtualizationlayer.Thevirtualizationfunctionis

specific to each resource type and wraps around each

resource instantiation given a resource type. For ease of

programming, the ensuing logical representation for a

resource is typically first supportedby companion offtheshelfsoftwareconstructs.

Fig. 1.Multitier architecture of Grid environment. Graphical represen-tation adapted from [7] and Admela Jukans contribution to [8].

The upper tiers must handle the logical representa

tionoftheresourceandrefrainfromdirectaccesstoany

specific mechanism for resource lifecycle management

(e.g., to configure, provision, monitor the resource). For

portability and complexity management, it is important

toprovidetheuppertierswithonlyaminimalistviewof

the resource, yet without overlooking any of its core ca

pabilities. Although the first tier may still perceive indi

vidualresourcesassilos,thesecondtierprovidesafoun

dationforhorizontalintegrationamongresources(silos).

Within this tier, the SOA property to compose autonom

ous services is most relevant. Conforming to SOA prin

ciples, a service is capable of engaging with other ser

vice(s) at either the same tier or at thebottom tier, in a

peertopeer fashion. The ensuing pool of services fea

turedinthesecondtierisadeparturefromstrictsoftware

layering techniques, which have shown severe limits in

reflecting complex synapses across entities. The Global

GridForumsOpenGridServicesArchitecture(OGSA)[6]

isablueprintwithwhichtostructureservicesthatbelong

tothesecondtierandexhibitsmultivendorinteroperabili

ty.

TherearetwobasicbuildingblocksforDataGrid[1]:

(i) a high performance data transfer system that enables

securecopingofmassivedatasets;and(ii)ascalabledis

covery and management system for replicas of datasets.

Other services that are required to provide the complete

functionalityofDataGridincludemanagementofshared

dataset collections, resource allocation for processing,

transferandstorageoperationandfinegainedaccesscon

trolsfordatasets.Inthispaper,wepresentanarchitecture

and design of a Data Grid simulation infrastructure

named GridSim [9], [10] shown in Fig. 1. GridSim has a

complete set of feature for simulating realistic Grid test

beds. Such features are modeling heterogeneous compu

tational resources of variable performance, scheduling

jobs based on time or spacedshared policy, differen

tiatednetworkserviceandworkload tracebasedsimula

tion from real super computers. More importantly, Grid

Sim allows the flexibility and extensibility to incorporate

newcomponentsintoitsexistinginfrastructure.GridSimisimplementedinJavaontopofanexisting

discrete event simulation engine: SimJava. Interactionsbetween GridSim entities are implemented using events(internal, external, synchronous and asynchronous).GridSim provides other primitives for application taskcreation, task mapping to resources and their manage

ment, scheduling task farming applications on heterogeneous Grids, considering economybased distributed resource management, dealing with deadline andbudgetconstraints[11].AllcomponentsinGridSimcommunicatewith each other through message passing operation. Thesecond layer models are the core elements of the distributed infrastructure, namely Grid resource such as clusters,storagerepositoriesandnetworklinks.Thethirdandfourth layers are concerned with modeling and simulation of services specific to computational and Data Gridrespectively.Informationaboutavailableresourceandjobmanagement also incorporates managing data transfersbetween computational and storage resources. Replica


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catalogs, information services for files and data are alsospecificallyimplementedforDataGridsinthislayer.Thefifth layer contains components that aid users in implementing their own schedulers and resourcebrokers. Thelayer above this helps users define their own scenariosand configurations for validating their algorithms andstrategies.

3 FUZZY TRUST INTEGRATED RESOURCEALLOCATION

The job scheduling problem is known to be NP

complete.AGridisusedforexecutingalargenumberof

jobs as dispersed resource sites. An optimization model

forfuzzyresourceallocationis,ParticleSwarmOptimiza

tion (PSO), a populationbased stochastic optimization

tool. PSO couldbe implemented, applied easily to solve

various function optimization problems, or the problem

that canbe transformed to function optimization prob

lems [12]. The system is initialized with a population of

random solutions and searches for optimaby updatinggenerations.Fuzzymatricesareusedtorepresentthepo

sition and velocity of the potential solution named par

ticlesinthePSOalgorithmformappingthejobschedules

and the particles. The system dynamically generates an

optimal schedule so as to complete the tasks within a

minimum period of time as well as utilizing all site re

source [13]. To formulate the problemJj denotes inde

pendent userjobs on Gi heterogeneous trusted grid sites

withanobjectiveofminimizingthecompletiontimeand

effectively utilizing trusted computing nodes only. The

fuzzyschedulingrelationfromGtoJcanbeexpressedas

(1).

Sij= R(Gi,Jj),i{1,2, ,m},j{1,2, ,n} (1)

Here Sij represents the degree of membership of the ith

element Gi domain G and thejth elementJj in domainJ

with reference to S. R is the membership function, the

valueofSijmeansthedegreeofmembershipthatthegrid

node Gi would process thejobJj in the feasible schedule

solutionandmisthetotalnumberofGridsiteandn

isthetotalnumberofavailablejobs.Inthegridjobsche

dulingproblem,theelementsofthesolutionmustsatisfy

the conditions (2) and (3).According to fuzzy matrix re

presentationofthejobschedulingproblem,thepositionX

andvelocityVareredefinedin(4)and(5).

Sij[0,1],i{1,2, ,m},j{1,2, ,n} (2)

Sij=1,i{1,2, ,m},j{1,2, ,n} (3)

Xij[0,1],i{1,2, ,m},j{1,2, ,n} (4)

Vij[0,1],i{1,2, ,m},j{1,2, ,n} (5)

The elements in the matrix X above have the same

meaningas(1).Accordingly,theelementsofthematrixX

must satisfy the constraint conditions given in (2) & (3).

(6) & (7) for updating the positions and velocities of the

particlesbasedonthematrixoperations.

V(t+1)=wV(t)(c1r1)(X#(t)X(t))(c2r2)(X*(t)X(t))(6)

Here X# is thebest position of each particle and X* is the

bestpositionamongtheswarm.Bothareobtained inthe

timet.c1andc2arelearningfactor,usuallyc1=c2=2andr1

andr2arerandomnumberbetween[0,1].

X(t+1)=X(t)V(t+1) (7)

Thepositionmatrixmayviolatetheconstraintsgiven

in (2) and (3) after some iteration, so it is necessary to

normalizethepositionmatrix.First,makeallthenegative

elementsinthematrixtobecomezero.Ifallelementsina

columnofthematrixarezero,theyneedbereevaluated

using aseries of random numbers within the interval [0,

1]andthenthematrix undergoes thefollowingtransfor

mation without violating the constraints 0,1/ wherei{1,2, ,m},j{1,2, ,n}andk{1,2, ,m}.Nowusingdefuzzificationalongthecolumn

vector in Xij select the highest membership degree. The

corresponding i is the Grid index forjob placement.

PSO is not trustworthy in selecting sites depending on

defensecapability.

Trusted Grid Computing demands robust resource

allocation with security assurance at all resource sites.

Largescale Grid applications arebeinghinderedby lack

ofsecurityassurancefromremoteresourcesites.Asecuri

tybinding scheme through site reputation assessment

and trust integration across Grid sites hold fuzziness or

uncertaintiesbehind all trust attributes. Thebinding is

achievedby periodic exchange of site security informa

tion and matchmaking to satisfy userjob demands [14].

Fuzzy trust integration reduces platform vulnerability

andguidesthedefenseacrossGridsites.

PKI (Public Key Infrastructure)based trust model

supports Grids in multisite authentication and single

signonoperations.However,crosscertificatesare inade

quatetoassesslocalsecurityconditionsatGridsites.The

trust index of a Grid site determines the site reputation

from its track record and selfdefense capability attri

butedtotheriskconditionsataGridSite.ASecureGrid

Outsourcing(SeGO)[14]systemprovidessecureschedul

ingalargenumberofautonomousandindividualjobsto

Grid site. SeGO scheduler optimizes the aggregate com

putingpowerwithsecurityassuranceunderfixedbudget

constraints.

Each site executes not only localjobsbut alsojobssubmitted fromremotesites.Gridsite mayexhibitunac

ceptable security measures and system vulnerabilities

[15], [16]. In mapping autonomous and indivisible user

jobs, it demands resource site to provide security assur

anceby issuing a security demand (SD) whereas the site

needstorevealitstrustworthinessreferredastrustindex

(TI). These two time variant dynamic parameters must

satisfyasecurityassurancecondition:TI SDduringthe

jobmappingprocess.SDiscomputedas(8).


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(8)

HereeijExpectedtimetocomputewhenscheduletaskti

to host mj. The estimated expected task execution times

oneachmachineareknowninthegridsites.Theassump

tioniscommonlymadewhenstudyingschedulingprob

lems for grids or Heterogeneous Computing (HC) systems [17], [18], [19] and qi refers the number of hosts

thatsatisfySD TLfortaskti.ThejobSDissuppliedby

the user programs as a single parameter only. The trust

indexisnormalizedasasinglenumberrealnumberwith

0representingtheconditionwithhighestriskatasiteand

1 representing the condition which is totally riskfree or

fullytrusted.TIiscomputedas(9).

9 Herethepjisthespeedofhostmj(MFlops).Thevariation

oftheTIofaresourcesitedependsuponsuccessrateand

site defense capability. The trust index increases with theincrease ofboth contributing factors helps to allocate re

sourceswithhighdegreeofsecurityassurance.Thefuzzy

inferenceisdoneformatchmakingbyfourstapes:fuz

zification,inference,aggregationanddefuzzification[21].

Trust model could deduce detailed security features to

guidethesitesecurityandupdateasaresultoftuningthe

fuzzysystem.Fuzzyrulesextractionfromnumericaldata

directly for function approximation is used to tune the

fuzzysystem[22].

EachSeGOagentcontainsaresourcemanageranda

trustmanager. Theresourcemanager maintainsresource

status and monitorjob execution. The trust manager assesses sites trust index through fuzzy inference system.

In this architecture the resource manager maintains its

owntrustvector,whichisupdatedperiodically. TheDis

tributed Hash Table(DHT)offersafast hashingprotocol

to exchange critical information in the trust integration

process. The whole Grid is describedby a trust matrix

definedby an m m square matrix M = (V1, V2, V3, ,

Vm),thetrustvectormaintainedatsiteSjisdonatedbyVj

= (t1j, t2j, , tmj) wherej m which represents the trust

indexofsiteSjwithallavailablesite.

This model applies two levels of trust inference: the

lower level fuzzy inference system collects all input parametersfromasinglesite,thuscalledintrasitelevel.The

output of the intrasite level provides the inputs to the

upper level. The upper level collects inputs from all re

source sites, thus called intersite level. There are two

fuzzy inference systems applied in the intrasite level.

Oneevaluates theselfdefense capability, and theother

one evaluates the site reputation. Each site reports its

assessedselfdefensecapabilitytoallothersites.Thereis

only one fuzzy inference system at the intersite level,

whichcollectsinputsfromintrasitelevels,andinfersthe

sitetrustindicestoformthetrustvectorforeachsite.The

intersitefuzzyinferenceprocessusingfivestepsissum

marizedinAlgorithm1.Allselectedrulesareinferred in

parallel. Initially, the membership is determinedby as

sessing all terms in the premise. The fuzzy operator

AND is applied to determine the support degree of the

rules. The AGGREGATE superimposes two AND re

sultscurveswhichisfollowedbydefuzzification.

Thereismanyotherfuzzyinferencerulesthatcanbe

designed using various combination of the fuzzy va

riables considered. The fuzzy rule extraction methodde

velopedbyAbeandLan[22]toderiverulesfromnumeri

cal data is used intofuzzy trust system. Fuzzy trust sys

tem needed tobe tuned to satisfy the securityassurance

index.

Algorithm1:Intersitefuzzyinferenceprocedure

1. Calculatesitereputation ,andobtainthereportedselfdefensecapability;

2. Use and smembershipfunctionstogeneratethe

membershipdegreesfor and ;

3. Applythefuzzyruleset,mapthe spacetoTI

spacethroughfuzzyAND,ORandIMPLYoper

ations;

4. Aggregatethefuzzyoutputsfromallrules;

5. Derive the trust indexs numerical value through a

defuzzificationprocess

Fig. 2. Fuzzy trust aggregation at the intra- and inter-site levels.

There are two tuning process: (1) Fuzzy system cali

bration and(2) Site security attributetuning.To set upa

fuzzysystem foraGrid, initiallythetuningprocessmay

notbe accurate due to the lack of accumulated data.An

accurate fuzzysystemshouldbe able to inferthecorrect

site trust indices from collected security and behavior

information.As the environment changes, the fuzzy sys

tem need to update its configuration setting repeatedly


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known as system calibration. The site security upgrade

process isguidedthroughatopdownsystemtuningthe

securityattributestoyieldthetargettrustindex.Thistun

ingprocesshastwo steps: intersitetuningand intrasite

tuning,asillustratedinFig.3.

The goal of the intersite tuning is to upgrade self

defensecapability,toelevatesiteTItomatchwithjobSD

asspecified inAlgorithm2.The intersitetuningsetsthe

target selfdefense capability for the intrasite tuning to

achieve security upgrades at individual sites. Trust up

date and trust propagation is specified in Algorithm 3

andAlgorithm 4 which helps to reduce the site vulnera

bility.

Fig. 3. Fuzzy system tuning process to upgrade site trust index.

Algorithm2:Intersitefuzzysystemtuningprocess

1. targetouput *=averageusersecuritydemand;

2. observedoutput =currentsitetrustindex;

3. error= * ;

4. while(||error||> ){

5. Adjust selfdefense capability to quantifiedby

thefuzzysystem;

6. =Intersiteinference(, );

7. error= * ;}

8. Send tointrasitefuzzysystemtuningprocess.

Algorithm3:Trust_Update(index_TTLreports,i,j)

1. Ri calculate success rate of Rj: = number of suc

cessjobs/index_TTL;

2. Riassessdefenserate ofRj;

3. Calculate the stimulus value:

Sij=Fuzzy_inference(, );

4. Calculatethenewtrustindex:tnewij=toldij+(1)Sij;

5. if((tnewij


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4 AUCTION BASED RESOURCE ALLOCATION

Inexistingeconomybasedmodelsofgridresourceal

location and management,just as a commodity market

modelandpostedpricemodel,shareresourcesarebased

on negotiating about the usage duration or time, the

usagefee,QoS(QualityofServices)andsomeotheritems

between the owner or hisbroker and consumer of gridresources.Thatwillcostsomuchtimeforagreatdealof

gridusersnomatterwhethertheyaregridresourceown

eror grid resource consumers,which reducethesharing

efficiencyinthegridenvironmentandsometimeiseven

unexpected. In this section, three auctionbased resource

allocationmodelsaredescribed.

Fig. 4. Fuzzy logic inference between job success rate and self-

defense capability to induce the trust index of a resource site.

Fig. 5. Membership functions for different levels of the trust index,

job success rate and site defense capability.

4.1 Auction Framework for Resource Allocation

In this section a model of an auction in Grid computingandthedesignoftheauctionframework[23]aredis

cussed. A descending Dutch auction that follows thestandardsprovidedbyFIPA[24,25],whichdefinesstandards for multiagent systems and for communicationamong agents in multiagent systems. The main participantsinanordinaryauctionaretheseller,theauctioneerand thebuyers orbidders. In reverse auction for Gridcomputing, the users arebuyers,brokers are auctioneersand resource providers are sellers. Thebuyer starts theauctionandthesellersbidtosellaservicetothebuyer.Insuchacase,aDutchauctionbecomesascending.Initially,theusersubmitsjobstothebroker.IntheGrid,abrokerisresponsible for submitting and monitoring jobs on theusersbehalf.Thebrokercreatesanauctionandsetsaddi

tional parameters of the auction such asjob length, thequantityofauctionrounds,thereservepriceandthepolicy tobe used (e.g. English or Dutch auction policy). Asthebroker also plays the role of auctioneer, it posts theauction to itself; otherwise, the auction wouldbe post toan external auctioneer. The auctioneer informs thebiddersthataDutchauctionisabouttostart.Then,theauctioneer creates a call for proposals (CFP), sets its initialprice,andbroadcaststheCFPtoallthebidders.Resourceproviders formulatebids for selling a service to the usertoexecuteitsjob.

The first time thatbidders evaluate the CFP, they decide not tobidbecause the price offered isbelow whattheyarewillingtochargefortheservice.ThismakestheauctioneertoincreasethepriceandsendanewCFPwiththis increase in the price. Meanwhile, the auctioneerkeepsupdatingthe informationabouttheauction.Inthesecond round, abidder decides tobid. The auctioneerclears the auction according to the policy specifiedbeforehand.Oncetheauctionclears,itinformstheoutcometotheuserandthebidders.Basedonthisgeneralmodelof

auctions, which generalized auction framework that allows users todevelopandevaluate auctionprotocolsforresource management in Gridsby using GridSim Gridsimulator[9].

4.2 Grid Resource Allocation with GeneralizedAssignment

OnbehalfofGRM(GridResourceManager)thegeneralized assignment algorithm meets the service of gridresource sharing [26]. Two key players driving the GridResourceSupermarket(GRS)areGSPs(GridServiceProviders) and GRBs (Grid Resource Broker). In the commodity market model, resource providers specify their

service price and charge users according the amount ofresourcetheyconsume.Thepricingpolicycanbederivedfrom various parameters and canbe flat or variable dependingontheresourcesupplyanddemand.Ingeneral,servicesarepricedinsuchawaythatsupplyanddemandequilibrium ismaintained.LogicstructureofcommoditymodelisjustlikeFig.6(a).Thepostedpricemodelissimilarto the commodity market model except that it advertisesspecialoffers inordertoattract(new)consumerstoestablishmarketshareormotivateuserstoconsiderusingcheaperslots.LogicstructureofpostedpricemodelisjustlikeFig.6(b).

Fig. 6(a) Interaction between GSPs and users in a commodity mar-

ket Grid for resource trading (b) Posted price model and resource is

trading in a computational market environment.


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In this case,brokers need not negotiate directly withGSPsforflatfee,usagedurationortime,QoSetc.butuseposted prices as they are generally cheaper compared toregular prices. The postedprice offers will have usageconditions,but they mightbe attractive for some users.The scheme includes two parts. Part one is Posted pricebased GRS model. In this part, grid consumer will sharetheGRSresourcejustlikepostedpricemodel.Parttwoisgrid resource optimizationbased GAP (Generalized Assignment Problem) in order to maximize the profits forthe GRS manager. GRS have n pieces of resources, eachresourcehavehisID,resourcename,bankaccountofhisowner, access time for sharing, resource amount, priceetc.signingasGRSRi =(RiID,Riname,Riaccount,time,amount, Ripricein, , RiIP) i = 1, 2, , n and GRSRi isshared by some grid consumer and homologous eachitem sign as SellRi = ( RiID, Riname, Riaccount, time,amount, Ripriceout, , RiIP) i = 1, 2, , n. Therefore, themanagerofGRSgainstheprofitsaccordingtothefollowings:Profits=i=1n((Ripricein)(Ripriceout)).Ingeneral,Ripriceinisbigger than Ripriceout, so the manager of GRS can get

profitsastheirgrossprofits.Thereareobviousdifferencesbetween Posted price model and Posted pricebasedGRS model.That is, all thedetail aboutresourcesharingsuch as cost fee, usage duration or time, QoS and otheritems in our approach was negotiated about while theGRS was constructed. That means a foreground task ischangedintobackgroundtask.

The problems are merely divided into the following

two cases [27]. Case 1: There are n pieces of resources

shouldbe scheduledby mjobs, m n, only onejob is

arrangedtooneresource,butjobjcanbearrangedbybj

resources cooperating withjob j, here bj is an un

known number, andj=1n

bj

= m. We might as well supposethatallocationshouldthinkaboutpfactorsuchas

router,bandwidth,price,etc.Assumefactork(k=1,2,

,p)thatresourcejarrangedtojobicanmakeGRS

economyefficiencyeij(i=1,2,,m ;j=1,2,,n),the

problem ishow toallocate theassignment and make the

managerofGRSgetthemaximumprofits.

Model1: maxProfitk= i=1mj=1neijkxij(k=1,2,,p)

s.t.{

j=1nxij=1(i=1,2,,m)

j=1ni=1mxij=m xij{0,1}

(i=1,2,,m;j=1,2,,n)}Case 2: If there are n pieces of resources should bescheduled by m jobs, mn, only one resource is ar

rangedtoonejob,butjobjisarrangedwithairesources

which satisfy job j together, here ai is an unknown

number, and i=1m ai = n. We might as well suppose that

allocation should think about p factor such as router,

bandwidth,price,etc.Assumefactork(k=1,2,,p)that

resource j arranged tojob i can make GRS economy

efficiencyeij(i=1,2,,m;j=1,2,,n),theproblemis

how to allocate the assignment and make the GRS man

agergetthemaximumprofits.

Model2maxProfitsk= i=1m j=1neijkxij(k=1,,p)

s.t.{

i=1mxij=1(j=1,2,,n)

i=1mj=1nxij=m xij=0,1

(i=1,2,,m;j=1,2,,n)}According to the procedure of multiobject composi

tivematrixR isdevelopedwithfuzzyrelationship.Afterthat,expandedbenefitmatrixAisproduced.Byus

ingHungaryalgorithm[27]thematrixA~iscalculated.Combining the fuzzy theory with Hungary algorithmwhich is applied to solve conventional assignment problem,thelastallocationofresourceiscalculated[26].

4.3 Neuro-Fuzzy Hybrid Negotiation Model

The restriction of the grid resource allocation with

generalized assignment algorithmbrings a disadvantage

position of application andthe system is not adaptive in

naturewithresponsetodynamicbehaviorofthegridsite.

A neurofuzzy hybrid model for autonomous agent to

negotiatethatallowsagentstoshoweffectiveandintelli

gentbehaviorsofrealgridenvironmentwheretheagents

areabletolearnfromtheenvironment[28].Thenegotia

tionprocessisdrivenbythefuzzylogic,wherethisfuzzy

logic is incorporated with the agents satisfaction consi

dering intelligent.Hereknowledgebase isusedwhichis

updatedby thebackpropagation neural network model

fromhistoricalinstancesoftargetdomain.Blockdiagram

of negotiation mechanism is given in Fig. 7. where the

negotiation attribute is price like in Dutch auction. Here

buyer is the resource allocator, seller is the grid site and

price refers resource amount. This model classifies price

intosixsubclasses,theyareverypoor(P5),poor(P4),av

erage(P3),good(P2),verygood(P1)andexcellent(P0).

In thismodelthe negotiation process is going on se

quentially. Following fuzzy membership functions re

gardingsatisfactionagainsttheofferingpriceoftheseller

agentareusedreferredasLevel_indexforbuyerprice,

1. Level_index=1:IfofferingpricewithinP0toP2

thenverygoodsatisfaction(VGS)


thengoodsatisfaction(GS)


thenmoderatesatisfaction(MS)


thenbadsatisfaction(BS)

5. Level_index = 5: If offering price within P4 to

abovethenverybadsatisfaction(VBS)

For the fuzzification of thebuyer agent input that is to

compute degree of membership for the antecedents is,

If (1 0) or (2 0) then the degree of membership = 0;

Else degree of membership B = (1 S1) ^ (2 S2) ^ 1

here,thepointofinputistheofferingpricereferasxof

thebuyerand1=distance(x,lowerLevel)and2=dis

tance(x,higherLevel).S1andS2 isslopofpricefunction

lower and higher Level respectively. For the seller agent

the offering value level satisfactions are {0.2, 0.4, 0.6, 0.8,


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1}.

In this model, the negotiation process goes on using

an intelligent utility function. Thebuyer agent checks its

satisfaction level and updates itsbeliefs about its oppo

nents and then tries to maximize its own expected out

comebased on its own subjectivebeliefs in single itera

tion.Twoparametersaretobeprovidedtoabuyeragent.

Theyare leastpriceandmaximumvalue.Thebuyerand

thesellerbothgeneratetheirownofferingpricedepend

ingupontheseparameters.Ifsatisfactionishighforbuy

er against the sellers offering price thebuyer agent tries

tobemoresatisfiedbyusingautilityfunction(10).

Pi+1=Pi+((Pdiff/Ps)Level_sats)(Degree_of_mebership)Level_i

ndexb (10)

Where Pi+1 = Next price offeredbybuyer agent, Pi= Last

priceofferedbythebuyeragent,Pdiff = (Lastpricebythe

sellerLastPriceofferedbythebuyer),Ps=Pricereduc

tionbythesellerintwoconsecutiveiteration.Itimproves

the concept of zero of agreement [29] in negotiation

dynamically.Naturally Pi+1 isproportional toPdiff and in

versely proportional toPs since huge reductionby the

sellercreatesdoubtinrealprice,qualityetc.So,thebuyer

increaselowersinprice.Onthecontrary,scantyreduction

bythesellercreatesconfidentonrealprice,qualityresults

the increase inpricebythebuyeragent.Lavel_satshelps

to the effect of same degree of membership in different

satisfactionlevel.level_indexbcontrolsthestrategyofthe

buyer for what manner he should negotiate. At initial

stagethebuyer agent increasesthepricerapidlyas it re

mains in higher satisfaction level. But as the satisfaction

decreasesthebuyeragentchangesitsattitudeandgoesto

increasepriceoftheproductslowly.

Fig. 7. Neuro-Fuzzy based negotiation mechanism flowchart.

Thebuyeragentwillcompletethetransactionandfinaldecisionsaretakenwiththehelpofhistoricalinstanceof a target domain by using back propagation neuralnetwork. With one hidden layer the summation of divisors of negotiating values is taken from particular selleragentsimultaneouslyusingSn=Iij/Wij.ThenSnwillbeinput of the next layer. The offer of the selected seller iscalculatedby the equation Sn/Wn where W is used forprevious experience for that seller. The output value in

everylayerisdeterminedbytheequationO=1/(1+e1(ST)) and the error is Er = 1/2(T O)2 where T is the threshold value lies between ranges. After completing thehidden layer operations, the final decision willbe takendividingbytheweightoftheselleragentwhichisconsideredfortheiroverallperformanceandprevioustransaction.

5 FUZZY REPLICA REPLACEMENT

The large popularity of Grid Computing and its ap

plications makes their performance very critical. Data

replication is an excellent technique to move and cache

dataclosetouser.Replicationreducesaccesslatencyand

bandwidthconsumption.Italsofacilitatesloadbalancing

andimprovesreliabilitybycreatingmultipledatacopies.

Replica placement algorithms are based on heuristic

wherereplicascanbemanagedandallocatedeitherstati

cally or dynamically. Static replication is an offline

process whereby replicas are placed using a snapshot of

thesystematdesigntimeevenifthesystemchangessignificantly. Therefore, dynamic approach is more natural

as it adapts to change in userbehavior and system dy

namicsandreallocatesreplicastonewcandidatesites.

Totaljobexecutiontimemeasureseffectivenessofthe

replicationstrategies.JobsintheDataGridmayrequesta

numberoffiles.Ifthefileisatalocalsite,responsetimeis

assumedtobezero;otherwisethefilemustbetransferred

fromthenearestreplicationsite.Thus,jobexecutiontime

includes the latency required to transfer a file. Thebest

replicationstrategyminimizesthetotaljobexecutiontime

and the total response time. The identified problem is

closelyanalogoustothepmedian[30]modelusedexten

sively for facility location problem in urban planning.

User requests and networkbandwidth plays a vital role

in large file transfers. The current network state and file

requests produce better results than file request alone.

The replication algorithm selects one site per iteration to

hostreplicabyoptimizingriskorutilityindexes[31].Fur

thermore, locating p candidate sites simultaneously ra

therthanonesiteperiterationiselaboratelydescribedin

[32].Thismultiobjectiveapproachcombinespcenterand

pmedian objective to decide where to place a replica.

Thismodelminimizesthemedianobjectivewithoutkeep

ing any requesting site too far from a candidate replica

tion site. The goal of replacement polices is to make the

best use of available resourcesby dynamically selecting

the files tobe cached or evicted. In this section an algo

rithm that applies a set of fuzzy control rules to identify

thefilestoevictisdescribed.AccordingtoEUDataGrid

Testbed [20], the users are directly connected with the

regionalresourceallocatorwhereastheresourceallocator

accumulates the file replica. So, from ovservation, it is

wise to evict files in the resource allocator rather than

selectingsitenodeforindexing.


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Deriving PostModel from resource allocator logs is

oneofthechallengingissues.Modeldiscoveryfromevent

logs isacoherentsubsetofarchitecture that typicallyre

volves aroundparticular aspects of the overall architec

ture [33]. The techniques are based on a probabilistic

analysisoftheeventtraces.Usingmetricsforthenumber,

frequency,sizeoffilesandregularityofeventoccurrence,

adeterminationismadeofthelikelyconcurrentbehavior

beingmanifestedby the system.Discovering thisbeha

viorhelpsthefuzzysystemtoapplyrules.Whenacache

missoccurandthecache isfullFuzzy12rule[34]deter

mines the files toevictbycomputing foreach file in the

cacheafigureofmerit,namely, itsprobabilityofreplica

tion (RP). Among the files ranked according to their

probabilityofreplication, thealgorithmchooses the files

with the highest rank. By understanding the properties

andbehavior of theirworkload three variables are cho

sen.These are (1) files in size, (2) access frequency, i.e.,

numberofaccessand(3)accesstime.Thefuzzysetswith

membership functionsdescribing thedegreeofmember

shipareassociatedwiththesevariables.Size(s)andFre

quency(f)holdsLOW,MEDIUM,HIGHlinguisticvalues

whereasTime(t)representsVERYLOW,LOW,MEDIUM,

HIGH,VERYHIGHmembership functions.The indexes

ofthesevariablesareshowinFig.8(a),(b),(c)and(d).The

ifthenFuzzy12conditionalrulesare,

If(fisLOW)and(tisVHI)and(sisMED)then(RPisVHI)

If(fisLOW)and(tisHIG)and(sisHIG)then(RPisVHI)

If(fisMED)and(tisVHI)and(sisHIG)then(RPisVHI)

If(fisLOW)and(tisVHI)and(sisHIG)then(RPisVHI)

If(fisLOW)and(tisHIG)and(sisLOW)then(RPisHIG)

If(fisMID)and(tisHIG)and(sisLOW)then(RPisMED)

If(fisMED)and(tisVHI)and(sisMED)then(RPisHIG)

If(fisMED)and(tisHIG)and(sisHIG)then(RPisHIG)

If(fisHIG)and(tisVHI)and(sisHIG)then(RPisLOW)

If(fisHIG)and(tisHIG)and(sisHIG)then(RPisLOW)

If(fisLOW)and(tisMID)and(sisHIG)then(RPisHIG)

If(fisMED)and(tisHIG)and(sisMED)then(RPisMED)

Once thedesignparameterhavebeendefined the fuzzy

algorithmproceedsasfollows,

1. Measurement of the values of the input data

fromtheresourceallocatorserver;

2. Fuzzificationoftheinputdataintofuzzysets;

3. Inferencefromfuzzyrules;

4. Aggregationacross therulesanddefuzzification

of the fuzzyoutput intoanon fuzzycontrolac

tion.The fuzzification has effect of scaling andmapping

crisp inputdata into fuzzy setsbymeansof the correspondingmembership function.The inputvalues relatedto each page are translated into linguistic concepts. Foreachrule,theantecedentsareevaluatedandthedegreeoftruthiscomputedbyapplyingthefuzzyandoperator,that is, the product. The aggregation process combinestheoutputsoftherulesbyapplyingthemaximumop

erator toeachdescriptive levelof theoutputvariableRP(i.e.,probabilityofreplication).Thedefuzzificationtransforms these four values into a nonfuzzy control actioncorrespondingtotheprobabilityofreplicationofthefile.Thedefuzzificationused themethodofcentroidand themassesareobtainedasaresultofaggregationprocess.Asafinalstep,thefilesarerankedaccordingtotheirprobabilityofreplication.

Fig. 8. Membership function of the variable (a) Size (b) Time (c) Fre-

quency and (d) Replication

6 FUZZY ROUTING

Toexchangecriticalinformation,amongtheuserandthe grid site, GridSim simulator uses java socket programmingoverTCP/IPnetworkmodel.Efficient routingincommunicationnetwork isbecoming increasinglydif

ficultduetotheincreasingsize,rapidlychangingtopologyandcomplexityofcommunicationnetwork.Thecomplexityinvolvedinthenetworksmayrequiretheconsiderationofmultipleconstraints tomake the routingdecision.A novel approach named FLAR (Fuzzy LogicAntbased Routing) inspiredby swarm intelligence and enhancedbyfuzzy logictechniqueasadaptiveroutingthatallowsmultipleconstraints tobeconsidered ina simpleandintuitiveway[35].

In the AntNet algorithm, routing is determinedthrough complex interactions of network explorationagents, called ants. These agents are divided into twoclasses, theforward antsand the backward ants.The ideabehind this subdivision of agents is to allow thebackward ants to utilize the useful information gatheredbytheforwardantsontheirtripfromsourcetodestination.Basedonthisprinciple,nonoderoutingupdatesareperformedbytheforwardants,whoseonlypurposeinlifeistoreportnetworkdelayconditionstothebackwardants.This information appears in the form of trip timesbetweeneachnetworknode.Thebackwardantsinheritthisrawdata anduse it toupdate the routing tables of thenodes.Thedetailed informationaboutdifferentversionsofAntNetalgorithmscanbefoundin[36].

FLAR is constructedwith the communicationmodelobserved in ant coloniesand fuzzy logic technique.The


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FuzzyInferenceSystem(FIS)forFLARisamamdanitypesystem with two inputs and one output. The system inputs are route (or link) delay and route utilization. Theboth inputs are characterizedby the fuzzy membershipfunctionsasshowninFig.9.andFig.10.Themembershipfunctionsforthefuzzysetsofinputsarechosentobetriangular.Bothofinputsarenormalizedbetween(0,1)beforeapplyingtoFIS.AsshowninFig.9andFig.10,bothinput variables route delay and utilization have fivemembership functions titled as VL, L, M, H, and VHwhich mean Very Low, Low, Medium, High, and VeryHighrespectively.

Fig. 9. Membership function of Link Delay (X1).

Fig. 10. Membership function of Link Utilization (X2).

TherulesoftheFISaredesignedforanoptimalperformance.Table1showsrulebasefortheFIS.Inthistablethe Values for the amount of goodness from lowest tohighestaredefinedasLL(VeryLow),LM,LH,ML,MM(Medium),MH,HL,HM,andHH(VeryHigh).

TABLE 1

RULE BASE FOR FIS

RouteGoodnessRouteUtilization(%)

VL L M H VH

RouteDelay(ms)

VL HH HM HL MH MML HM HL MH MM MLM HL MH MM ML LHVL HH HM HL MH MML HM HL MH MM MLM HL MH MM ML LHH MH MM ML LH LM

VH MM ML LH LM LL

TheoutputofFISwhichisroutegoodnessisapplied

to the software simulation for evaluations. Design of

FuzzyInferenceSystemistheprocessofformulatingthemapping from a given input to an output using fuzzylogic.

The defuzzification is the process of conversion offuzzy output set into a single number. The method usedfor the defuzzification is, mean of centers as shown in(11).Then,theoutputoffuzzysystemafterdenormalization is applied to the FLAR algorithm as theRoute_Goodnesswhichcanbeusedasacriterionforgood

Fig. 11. Membership function of Route Goodness (Y). ness of aroute(orlink).

_

(11)

Where,i isthenodewhereanant isgoingfrom,jisreferred the node where an ant wants to move, M is thenumber of fuzzy rule, i.e. M = 25, nf is the number ofmembership functions for input variables, i.e. nf = 2 andAi(xi) is the Fuzzy value of membership functions. The

sequenceofFLARalgorithmisoutlinedasfollows:1. Eachsourcenodelaunchesforwardantstodesti

nationsatregulartimeintervals.

2. The ants find a path to the destination randomly

basedonthecurrentroutingtables,butthedata

packetschoosethepathtodestinationwithhigh

estprobability.

3. Theforwardantscreateastack,pushingindelay

time and percentage of buffer utilization for

everytraversedroute(orlink)toanode.Thede

lay canbe the sum of wait time in queue and

transmissiontimeforeachvisitednoden.

4. When the destination is reached, thebackwardantsinheritthestack.

5. Thebackward ants pop the stack entries (delay

time and utilization percentage) and follow the

pathinreverse.

6. Those entries are given to fuzzy inference sys

tem. The output of fuzzy system is used as the

goodnessvaluetoupdatetheroutingtableofthe

node.

7. The routing tables of each traversed route (or

link)areupdatedwithequation(12)onthebasis


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ofthegoodnessvalue.

_, 1 _, 112Whereisthelearningrate.TheestimationPath_Goodnessnj,dwhich means the amount of goodness to go fromnode nto destinationdvianeighbor node j, is ex

pressedinequation(13).ThisequationstatesthatnPath_Goodnessj,disthesumofallRoute_Goodnessvaluesofthetraversed links in the path that are obtainedby equation(11).

_, _ (13)Where tisthenumber oftraversedroutes(or links) inthe path starting with node n (l=1) and finished withnode d (l=t) via neighbor node j. Afterward routingtableprobabilitiesareupdatedbyequation(14).

, _

,

_, 14WherelNeighbor(n).Theadvantagesofsuchanintelligent algorithm include increased flexibility in the constraintsthatcanbeconsideredinmakingtheroutingdecisionefficientlyandthesimplicityintakingintoaccountmultipleconstraints.

The fuzzy control ant routing system showsbetterperformancethanOSPF.Sothisnovelapproachindicatesanencouraging characteristic for dynamicnetmesseginginfuzzyGridenvironment.

7 CONCLUSIONThe vision of this survey is to make Fuzzy Grid more

comprehensive. We try to come up with some common

features which are desirable for assembling Fuzzy Grid.

As the problem is not trivial, there are lots of factors in-

side, if we really want to establish our arguments of this

paper. Here we highlight the most popular contributions

in this area with the motivation to provide a generic plat-

form to work with a complete fuzzy system of Grid com-

puting environment.

The Grid sites do not share a common memory or the

computing capability among themselves even if the site

remains inoperative. Distributed service Grid manage-ment architecture [37] is capable of performing auto-

mated resource-to-service assignations. Divisible load

balancing among the sites using parallel algorithm is our

future focus.

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Ashiqur Md. Rahman received his B.Sc. Degree in ComputerScience and Engineering from American International UniversityBangladesh, Dhaka in January, 2004. He is currently perusing hisM.Sc. degree from North South University, Dhaka since January2006. He has authored in 5 national and international journal andconference papers in the area of Data Mining, VHDL, Cryptographyand PVc module design. His current research interest is in GridComputing especially in large Grid Environment.

Roksana Akter obtained the degree of Master of Science (M. Sc.)and Bachelor of Science (B. Sc.) in Computer Science and Engi-

neering from the University of Dhaka, Bangladesh in 2004 and 2003respectively. She is currently working as a senior lecturer in the de-partment of Computer Science and Engineering, Southeast Universi-ty, Dhaka, Bangladesh. Her current research interest is in computernetworks, network simulators, MANET, digital systems, data com-munications, cryptography, information security and published sevenresearch papers in national and international journals and confe-rence proceedings.

Rashedur M. Rahman received his Ph.D. Degree in Computer

Science from University of Calgary, Canada in November, 2007. Hehas received his M.Sc. degree from University of Manitoba, Canadain 2002 and Bachelor degree from Bangladesh University of Engi-neering and Technology (BUET) in 2000 respectively. He is currentlyworking as an Assistant Professor in North South University, Dhaka,Bangladesh. He has authored more than 25 international journal andconference papers in the area of parallel, distributed, grid computingand knowledge and data engineering. His current research interest isin data mining especially on financial, educational and medical sur-veillance data, data replication on Grid, and application of fuzzy logicfor grid resource and replica selection.

Fuzzy-Logic Based Self Adaptive Grid Architecture

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