fuzzy-logic based self adaptive grid architecture
TRANSCRIPT
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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
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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)
2. Level_index=2:IfofferingpricewithinP1toP3
thengoodsatisfaction(GS)
3. Level_index=3:IfofferingpricewithinP2toP4
thenmoderatesatisfaction(MS)
4. Level_index=4:IfofferingpricewithinP3toP5
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.