Applied Soft Computing

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ContentslistsavailableatSciVerseScienceDirect

Applied

Soft

Computing

jo u r n al h om epa g e :w w w . e l s e v i e r . c o m / lo c a t e / a s o c

Honey

bee

behavior

inspired

load

balancing

of

tasks

in

cloud

computing

environments

Dhinesh

Babu

L.D.

a,∗

,

P.

Venkata

Krishna

b

a_School_of_Information_Technology_and_Engineering,_VIT_University,_Vellore,_India b_School_of_Computing_Science_and_Engineering,_VIT_University,_Vellore,_India

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received30August2012

Receivedinrevisedform

28December2012

Accepted30January2013

Availableonline14February2013

Keywords:

Loadbalancing

Cloudcomputing

Prioritiesoftasks

Honeybeebehavior

Performanceevaluation

a

b

s

t

r

a

c

t

SchedulingoftasksincloudcomputingisanNP-hardoptimizationproblem.Loadbalancingof non-preemptiveindependenttasksonvirtualmachines(VMs)isanimportantaspectoftaskscheduling inclouds.WhenevercertainVMsareoverloadedandremainingVMsareunderloadedwithtasksfor processing,theloadhastobebalancedtoachieveoptimalmachineutilization.Inthispaper,wepropose analgorithmnamedhoneybeebehaviorinspiredloadbalancing(HBB-LB),whichaimstoachievewell balancedloadacrossvirtualmachinesformaximizingthethroughput.Theproposedalgorithmalso bal-ancestheprioritiesoftasksonthemachinesinsuchawaythattheamountofwaitingtimeofthetasks inthequeueisminimal.Wehavecomparedtheproposedalgorithmwithexistingloadbalancingand schedulingalgorithms.Theexperimentalresultsshowthatthealgorithmiseffectivewhencompared withexistingalgorithms.Ourapproachillustratesthatthereisasigniﬁcantimprovementinaverage executiontimeandreductioninwaitingtimeoftasksonqueue.

1. Introduction

Cloudcomputingisanentirelyinternet-basedapproachwhere alltheapplicationsandﬁlesarehostedonacloudwhichconsistsof thousandsofcomputersinterlinkedtogetherinacomplexmanner. Cloudcomputingincorporatesconceptsofparallelanddistributed computingtoprovidesharedresources;hardware,softwareand informationtocomputersorotherdevicesondemand.Theseare emergingdistributed systemswhichfollowsa “payasyouuse” model.Thecustomerneednotbuythesoftwareorcomputation platforms.Withinternetfacility,thecustomercanusethe compu-tationpowerorsoftwareresourcesbypayingmoneyonlyforthe durationhe/shehasusedtheresource.Thisforcestheconventional softwarelicensingpoliciestochangeandavoidsspendingofmoney forthefacilitiesthecustomerdoesnotuseinasoftwarepackage.

Thecustomerisinterestedin reducingtheoverall execution timeoftasksonthemachines.Theprocessingunitsincloud envi-ronmentsarecalledasvirtualmachines(VMs).Inbusinesspoint ofview,thevirtualmachinesshouldexecutethetasksasearlyas possibleandtheseVMsruninparallel.Thisleadstoproblemsin schedulingofthecustomertaskswithintheavailableresources. Theschedulershoulddotheschedulingprocessefﬁcientlyinorder to utilize the available resources fully. More than one task is

∗Correspondingauthor.Tel.:+9104162243091.

E-mailaddresses:[email protected](DhineshBabuL.D.),

[email protected](P.VenkataKrishna).

assignedtooneormoreVMsthatrunthetaskssimultaneously. Thiskindofenvironmentsshouldmakesurethattheloadsarewell balancedinallVMsi.e.,itshouldmakesurethatthetasksarenot loadedheavilyononeVMandsomeVMsdonotremainsidleand/or underloaded.Inthiscase,itistheresponsibilityforthescheduler tobalancetheloadsacrossthemachines.Aloadbalancing algo-rithmattemptstoimprovetheresponsetimeofuser’ssubmitted applicationsbyensuringmaximalutilizationofavailableresources. Themainobjectiveofloadbalancingmethodsistospeedup theexecutionofapplicationsonresourceswhoseworkloadvaries atruntimeinunpredictableway[10].Loadbalancingtechniques arewidelydiscussedinhomogeneousaswellasheterogeneous environmentssuchasgrids.Therearebasicallytwokindsofload balancingtechniques.Theyare(i)Staticand(ii)dynamic.

Staticalgorithmsworkproperlyonlywhennodeshavealow variationintheload.Therefore,thesealgorithmsarenotsuitable forcloudenvironmentswhereloadwillbevaryingatvaryingtimes. Dynamicloadbalancingalgorithmsareadvantageousoverstatic algorithms.Buttogainthisadvantage,weneedtoconsiderthe additionalcostassociatedwithcollectionandmaintenanceofthe loadinformation.

Dynamictechniquesare highlysuccessfulfor loadbalancing oftasksamongheterogeneousresources.Ourproposedload bal-ancingtechniqueisalsoadynamictechniquewhichwillnotonly balancetheloadbutalsotakeintoaccounttheprioritiesoftasksin thewaitingqueuesofVMs.

Incloudcomputingenvironments,wheneveraVMisheavily loadedwithmultipletasks,thesetaskshavetoberemovedand

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submittedtotheunderloadedVMsofthesamedatacenter.Inthis case,whenweremovemorethanonetaskfromaheavyloadedVM andifthereismorethanoneVMavailabletoprocessthesetasks, thetaskshavetobesubmittedtotheVMsuchthattherewillbe agoodmixofprioritiesi.e.,notaskshouldwaitforalongtimein ordertogetprocessed.Loadbalancingisdoneatvirtualmachine leveli.e.,atintra-datacenterlevel.

Ourapproachsuggeststhatloadbalancingincloudcomputing canbeachievedbymodelingtheforagingbehaviorofhoneybees. Thisalgorithmisderivedfromadetailedanalysisofthebehavior thathoneybeesadopttoﬁndandreapfood.Inbeehives,thereis aclassofbeescalledthescoutbeeswhichforageforfoodsources, uponﬁndingone,theycomebacktothebeehivetoadvertisethis usingadancecalledwaggle/tremble/vibrationdance.Thedisplay ofthisdance,givestheideaofthequalityand/orquantityoffood andalsoitsdistancefromthebeehive.Foragerbeesthenfollow theScoutBeestothelocationoffoodandthenbegintoreapit. Theythenreturntothebeehiveanddo awaggleortrembleor vibrationdancetootherbeesinthehivegivinganideaofhow muchfoodisleftandhenceresultingineithermoreexploitation orabandonmentofthefoodsource.

Inthesamemanner,theremovedtasksfromoverloadedVMs areconsideredasthehoneybees.Uponsubmissiontotheunder loadedVM,thetaskwillupdatethenumberofvariousprioritytasks andloadofthatparticularVMtoallotherwaitingtasks.Thiswill behelpfulforothertasksinchoosingtheirvirtualmachinebased onloadandpriorities.Wheneverahighprioritytaskhastobe sub-mittedtootherVMs,itshouldconsidertheVMthathasminimum numberofhighprioritytaskssothattheparticulartaskwillbe executedattheearliest.SinceallVMswillbesortedinascending orderbasedonload,thetaskremovedwillbesubmittedtounder loadedVM.Inessence,thetasksarethehoneybeesandtheVMs arethefoodsources.LoadingofatasktoaVMissimilartoahoney beeforagingafoodsource(aflowerorapatchofflowers).When aVMisoverloadedi.e.,similartothehoneygettingdepletedat afoodsource,thetaskwillbescheduledtoanunderloadedVM similartoaforagingbeefindinganewfoodsource.Thisremoved taskupdatestheremainingtasksabouttheVMstatussimilarto thewaggle/tremble/vibrationdanceperformedbythehoneybees toinformotherhoneybeesinthebeehive.Thistaskwillupdate thestatusoftheVMi.e.,howmanytasksarebeingprocessedby theVMandaboutthenumberanddetailsofhighprioritytasks cur-rentlyprocessedbytheVMinamannersimilartothebeesfinding anabundantfoodsourceupdatingtheotherbeesinthebeehive throughitswaggledance.Thisupdatingwillgiveaclearideain decidingwhichtaskshouldbeassignedtowhichVMbasedonthe availabilityandloadoftheVMssimilartowhichhoneybeesshould visitwhichfoodsourcebasedonwhetherhoneyisavailableata flowerpatchornot.Theproposedalgorithmworkswellforload balancingoftasksincloudcomputingenvironments.

Thespeciﬁccontributionsofthispaperinclude

•An algorithm for scheduling and load balancing of non-preemptiveindependenttasksincloudcomputingenvironments inspiredbyhoneybeebehavior

•Aliteraturesurveyaboutvariousexistingloadbalancing algo-rithmsandthemerits/demeritsofthesetechniques

•CorrelationoftheproposedHBB-LBalgorithmwithactual forag-ingbehaviorofhoneybeesusingaclearﬂowdiagramshowing thebehavioralcontrolstructuresofhoneybeesandHBB-LB.

•Ananalysisandsystematicstudywithmathematicalevidenceto showhowthehoneybeebehaviorinspiredloadbalancingcan workforcloudcomputingenvironments

•Performanceanalysisoftheproposedalgorithmandan evalua-tionofthealgorithmwithrespecttootherexistingalgorithms.

Restofthispaperisorganizedasfollows:Section2discusses abouttherelatedworksonexistingloadbalancingtechniques. Sec-tion3describestheforagingbehaviorofhoneybeesandhowit relatestotheproposedtechnique.Section4focusesonour pro-posedapproach withdetailed algorithm andSection5 presents the experimentalresultsalong withperformance evaluation of the algorithm in comparison with existing algorithms. Finally weconcludethispaperhighlightingthecontributionsandfuture enhancementsinSection6.

2. Relatedworks

Loadbalancingis removingtasksfromoverloadedVMsand assigningthemtounderloadedVMs. Loadbalancingcan affect theoverallperformanceofasystemexecutinganapplication.Load balancingalgorithmscanbeclassiﬁedintwodifferentways[2]:

Staticloadbalancingalgorithms:Thedecisionsrelatedtobalancing ofloadwillbemadeatcompiletimewhenresourcerequirements areestimated.Theadvantageofthisalgorithmisthesimplicity withrespecttobothimplementationandoverhead,sincethereis noneedtoconstantlymonitorthenodesforperformance statis-tics.Staticalgorithmsworkproperly onlywhenthere isa low variationintheloadfortheVMs.Therefore,thesealgorithmsare notwellsuitedforgridandcloudcomputingenvironmentswhere theloadwillbevaryingatvariouspointsoftime.

Dynamicloadbalancingalgorithms:Dynamicloadbalancing algo-rithmsmakechangestothedistributionofworkloadamongnodes atrun-time;theyusecurrentloadinformationwhenmaking dis-tributiondecisions[16].

Houleetal.[9]consideralgorithmsforstaticloadbalancingon treestreating thatthetotal loadis aﬁxedone.In[6],Huetal. proposeanoptimaldatamigrationalgorithmindiffusivedynamic loadbalancing throughthecalculationofLagrangemultiplierof theEuclideanformoftransferredweight.Thisworkcaneffectively minimizethedatamovementinhomogenousenvironments,butit doesnotconsiderheterogeneousenvironments.Genaudetal.[12]

enhancedtheMPI Scattervprimitivetosupportmaster-slaveload balancingbytakingintoconsiderationtheoptimizationof compu-tationanddatadistributionusingalinearprogrammingalgorithm. However,thissolutionislimitedtostaticloadbalancing.

In[7],aNewTimeOptimizingProbabilisticLoadBalancing Algo-rithminGridComputingispresented.Thisalgorithmchoosesthe resourcesbasedonbetterpaststatusandleastcompletiontime. Themainpurposeofthisalgorithmistoestablishloadbalancing andreducetheresponsetime.In[1],aTaskLoadBalancingStrategy forGridComputingispresented.Inthispaper,ahierarchicalload balancingstrategyandassociatedalgorithmsbasedon neighbor-hoodpropertyisdiscussed.Thisstrategyprivilegeslocalbalancing inﬁrst(loadbalancewithinsiteswithoutcommunicationbetween sites).Then,upperhierarchicalbalancingwilltakeplaceand so on.Themainbeneﬁtofthisideaisthedecreaseintheamountof messagesexchangedbetweenGridresources.Thissystemcreates ahierarchicalarchitecturethatistotallyindependentofGrid archi-tecture.In[5],titled“DynamicLoadBalancinginGridComputing”, likethepreviouspaper,thispaperpresentsataskloadbalancing modelinGridenvironment.Italsodetailsthesystemina man-nersimilartothepreviousreference.Themaincharacteristicsof thisstrategyare:(i)Itusestask-levelloadbalancing;(ii)It priv-ilegeslocaltaskstransfertoreducecommunicationcosts;(iii)It isadistributedstrategywithlocaldecisionmaking.Thissystem transformstheGridtotreestructure independentofGrid topo-logicalstructurecomplexity.Thesystemwillusethistreeforload balancing.

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In[8],A ComparativeStudy intoDistributed LoadBalancing AlgorithmsforCloudComputingispresented.Thispaperconsiders threepotentiallyviablemethodsforloadbalancinginlargescale cloudsystems.Firstly,anature-inspiredalgorithmmaybeusedfor self-organization,achievinggloballoadbalancingvialocalserver actions.Secondly,self-organization canbeengineeredbasedon randomsamplingof thesystemdomain,giving abalancedload acrossallsystemnodes.Thirdly,thesystemcanberestructuredto optimizejobassignmentattheservers.Recentlynumerous nature-inspirednetworkingand computingmodelshavereceiveda lot ofresearchattentioninseekingdistributed methodstoaddress increasingscaleandcomplexityinsuchsystems.

Thehoney-beeforagingsolutionin[3],isinvestigatedasadirect implementationof a natural phenomenon. Then, a distributed, biasedrandomsamplingmethodthatmaintainsindividualnode loadingnearaglobalmeanmeasureisexamined.Finally,an algo-rithmforconnectingsimileservicesbylocalrewiringisassessed asameansofimprovingloadbalancingbyactivesystem restructu-ring.Incaseofloadbalancing,asthewebserversdemandincreases ordecreases,theservicesareassigneddynamicallytoregulatethe changingdemandsoftheuser.Theserversaregroupedunder vir-tualservers(VS),eachVShavingitsownvirtualservicequeues. Eachserverprocessingarequestfromitsqueuecalculatesaproﬁt orreward,whichisanalogoustothequalitythatthebeesshowin theirwaggledance.

In[4],DynamicLoadBalancingStrategyforGridComputingis presentedaddressingtheproblemofloadbalancinginGrid com-puting.Asin[1,5]thispaperalsoproposesaloadbalancingmodel basedonatreerepresentationofaGrid.Thisloadbalancing strat-egyhastwomainobjectives:(i)Reductionofthemeanresponse timeoftaskssubmittedtoaGrid;and,(ii)Reductionofthe com-municationcostsduringtasktransferring.Thisstrategydealswith threelayersofalgorithms(intra-site,intra-clusterandintra-grid). Loadbalancingalgorithmscanbedeﬁnedbasedonthe imple-mentationofthefollowingpolicies[11]:

•Informationpolicy: speciﬁeswhat workloadinformation tobe collected,whenitistobecollectedandfromwhere.

•Triggeringpolicy:determinestheappropriateperiodtostartaload balancingoperation.

•Resourcetypepolicy:classiﬁesaresourceasserverorreceiverof tasksaccordingtoitsavailabilitystatus.

•Locationpolicy:usestheresultsoftheresourcetypepolicytoﬁnd asuitablepartnerforaserverorreceiver.

•Selectionpolicy:deﬁnesthetasksthatshouldbemigratedfrom overloadedresources(source)tomostidleresources(receiver). LoadBalancingcanalsobeclassiﬁedintomorecategoriesbasedon differentbehaviorsofloadbalancingalgorithms[5]:Centralizedvs. distributedloadbalancingandapplication-levelvs.system-level loadbalancing

In[13],ARoutingLoadBalancingPolicy forGridComputing Environmentsispresented.Itusesroutingconceptsfromcomputer networks to deﬁne a neighborhood and search the most ade-quatecomputerstodivideapplications’workload.Thisalgorithm isdesignedtoequallydistributetheworkloadoftasksofparallel applicationsoverGridcomputingenvironments.Routealgorithmis indicatedforenvironmentswherethereareseveralheterogeneous computers and parallel applications are composed of multiple tasks.Whendealingwithlargescalesystems,anabsolute mini-mizationofthetotalexecutiontimeisnottheonlyobjectiveofa loadbalancingstrategy.Thecommunicationcost,inducedbyload redistribution,is also a criticalissue. For this purpose, Yagoubi proposesin [14],a hierarchicalloadbalancing model asa new frameworktobalancecomputingloadinaGrid.Thismodelsuffers frombottlenecks.In[23],aMathematicalmodelofcloud comput-ingframeworkusingfuzzybeecolonyoptimizationtechniqueis

presented.HoneyBeecolonyalgorithmisusedforwebservicesin webserversthatarescattered.

3. Honeybeeforagingbehavior

Theartiﬁcialbeecolonyalgorithm(ABC),anoptimization algo-rithmbased onthe intelligent foraging behavior of honey bee swarm was proposed by Karaboga in 2005 [26,38]. This new Metaheuristicisinspiredbytheintelligentforagingbehaviorof honey bee swarm. The algorithm presented in the work is for numericalfunctionoptimization.TheadvantageofABCisthatthe globalsearchabilityinthealgorithmisimplementedby introduc-ingneighborhoodsourceproductionmechanism [27].Raoetal.

[27]dealswithradialdistributionsystemnetworkreconfiguration problem.Thispaperpresentsamethodfordeterminingthe sec-tionalizingswitchtobeoperatedinordertosolvethedistribution systemlossminimizationproblem. ABCalgorithmwereusedin manyfieldssuchasdigitalsignalprocessing[28],leaf-constrained minimumspanningtreeproblem[29],flowshopscheduling prob-lem[30], blockmatchingalgorithm formotionestimation [39], optimization[42] andinverseanalysisproblems[31].Tsaietal. presentaninteractiveartificialbeecolonysupportedpassive con-tinuousauthenticationsystem[41].

In[32],anewpopulation-basedsearchalgorithmcalledtheBees Algorithm(BA)ispresented.Thealgorithm mimicsthefood for-agingbehaviorofswarmsofhoneybees.Initsbasicversion,the algorithmperformsakindofneighborhoodsearchcombinedwith randomsearchandcanbeusedforbothcombinatorial optimiza-tionandfunctionaloptimization.Honeybeeshavedevelopedthe abilitytocollectivelychoosebetweennectarsourcesbyselecting theoptimalone:Thissourceprovidesa maximumratioofgain comparedtocosts[34].Thewholedecentralizeddecisionprocess isbasedoncompetitionamongdancingbees,which guidenew (naive)beestotheirforagingtargets.In[37],authorshaveproposed Loadbalancingusingbeesalgorithm.

Honeybeebehaviorisalsousedinwebservices.In[19],“On HoneyBeesandDynamicServerAllocationinInternetHosting Cen-ters”,authorsproposeanewhoneybeeallocationalgorithmbased onself-organizedbehaviorofforagersinhoneybeecolonies. Host-ingcentersthenmustallocateserversamongclientstomaximize revenue.Theallocation ofserverstocollectrevenue inInternet hostingcentersparallelstheallocationofforagerstocollectnectar inhoneybeecolonies.Ahostingcenterwithacertainnumberof servershostingmultipleInternetclientsisanalogoustoahoney beecolonywithacertainnumberofbeesforagingatmultiplesites inthesurroundingcountryside.

Amodelofself-organizationthattakesplacewithinacolonyof honeybeeshasbeenpresentedin[33].ThisInsectforaging tech-niqueisusedintheﬁeldofrobotics.Themainprinciplesofsocial insectforagingbehaviorcanﬁndanapplicationinaswarmof inex-pensiveinsect-likerobots[35,36].

AccordingtoJohnsonandNieh,HoneyBeesaresocialinsects wherecollectivedecisionsaremadeviafeedbackcyclesbasedon positiveandnegativesignaling[24].Fig.1showsasimpliﬁedﬂow diagramofbehavioral controlstructureforforaging honeybees aspresented by Brian R.Johnson& JamesC. Nieh.This behav-ioralmodelisapowerfulandtestedmodeldescribingtheforaging behavior ofhoney bees. Thismodel is basedonthe behavioral structureofforagerbeedevelopedbyHandeVries&JacobusC. Biesmeijer[25].Animportantmeansforcommunicationamong honeybeesisthroughwaggledance,adancewhichwillgivean ideatothewaitingbeesinthenestaboutapotentialfoodsource,its distancefromthebeehiveetc.Inaddition,honeybeesusetremble andvibrationdancesalso.

OurproposedHoneyBeeBehaviorinspiredLoadBalancingis basedontheaboveBrianR.JohnsonandJamesC.Niehbehavioral

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switch? Experience? Recon? Delay Scout? Dance perceived? Dance info confirming? Fight? Delay Lose? Stop Signal Successful? Dance perceived? Scout? OUT Scout Store Loss Win? Feed Store site Store value Store time Return to nectar source? Return to nectar source? Store new position FLY Sound Found BEE IN Load? Find r Unload Vibration Tremble Waggle

Fig.1. Flowdiagramofthebehavioralcontrolstructureforforaginghoneybees

adaptedfromJohnsonandNieh[25]withminorchanges.

modelofhoneybees.InFig.1,arrowswithablackﬁlledstartcircle representpositive signals andarrows withhollowcircle repre-sentnegativesignals.Bothsituations,positiveandnegativerequire somesortofcommunicationamonghoneybeeswarms.Wehave mappedtheaboveﬂowdiagramshowingthebehavioralcontrol structurefor foraging honeybeesintotheproposedHoney Bee BehaviorinspiredLoadBalancingalgorithm.Theproposed algo-rithmiscompletelyinspiredbythenaturalforagingbehaviorof honeybeesandisillustratedinFig.2.

AtaskremovedfromoverloadedVMhastofindsuitableunder loadedVMsitcanbeallocatedto.Ithastwopossibilitiesi.e.,either itfindstheVMset(Positivesignal)oritmaynotfindthesuitable VM(negativesignal).TherecouldbemorethanoneVM(asetof VMsforallocation)whichcanacceptthistask.Nowthetaskhas tofindbestamongtheseVMsbasedontheQoScriteriacalledtask priorityi.e.,taskfindstheVMwhichhasalessnumberoftaskswith samekindofpriority(highprioritytasksfindstheVMwhichhas lessnumberofhighprioritytasks).WecallitasfightfortheVM amongtasks.Whenthisfightisover,thewinningtaskisallocated totherespectiveVMfoundandthedetailsareupdated.

IfataskdoesnotfindasuitableVM,itgoesfordelayin alloca-tion,duringwhichitgetsexperienceanditwillstartlisteningthe informationupdatesbyothertasks(similartohoneybeeslistening variousdances).Onceitconfirmstheinformation,theprocessstarts fromfindingtheVMsetsandaftersuccessfulVMsetidentification, itwillfightwithothercompetingtaskstofindit’smostsuitableVM andgetsallocatedtoit.Informationisupdatedonceitendsitsfight withothertasks(whetheritlosesorwins).Asnewertasksarrive, thecyclestartsuntilalltasksareallocatedtoVMsandthe schedul-ingsystemiswellbalancedbasedonloadaswellaspriorities.

4. Honeybeebehaviorinspiredloadbalancing(HBB-LB) algorithm

Cloudcomputingdealswithassigningcomputationaltaskson adynamicresourcepoolofvirtualmachinesonlineaccordingto

Find Suitable VMs by Load VM set Found Return to VM set Info confirming? Successful? Delay Experience? Info perceived? Scout? Recon? Scout? Info perceived? Return to VM set switch? Store new Info FLY Dance Vibration Tremble Waggle Delay Lose? OUT Scout Store Loss Win?

Fight for the VM based on QoS Feed •Store VM load •Store Task priorities •Store VM groups TASK IN Load? Inform next Task about VMs Allocate task to the respective VM

Fig.2.FlowdiagramofthebehavioralcontrolstructureforHoneyBeeBehavior

inspiredLoadBalancingofTasksinCloudComputinginspiredbyforagingbehavior

ofrealhoneybeesadaptedfromJohnsonandNieh[25]withminorchanges.

differentrequirementsfromuserorthesystem[15].Theservice requestsfromtheclientsfordiverseapplicationscanberouted atanydatacentertoanyendserverinthecloud.Theroutingof servicerequeststothediverseserversisbasedoncloud manage-mentpoliciesdependingonloadofindividualservers,closeness todatabasesetc.Thetwo frequentlyusedscheduling principles inanonpre-emptivesystemaretheFirst-in-First-out(FIFO)and WeightedRoundRobin(WRR)policies.Thesepoliciesmayendup withdifferentdegreesofloadsoneachandeveryVM.Thismay leadtoloaddifferencebetweenVMscomputinginparallel.This createsadditionalproblemsofreductioninresponsetime,wastage ofresourcesandsoon.

Thesekindsofsituationsleadsustogivemoreimportanceto thedynamicloadbalancingtechniqueswhichsolvestheproblem ofloadimbalancebetweenVMs. LoadBalancingtechniquesare effectiveinreducingthemakespanandresponsetime.

Makespancanbedeﬁnedastheoveralltaskcompletiontime. WedenotecompletiontimeoftaskTionVMjasCTij.Hence,the

makespanisdeﬁnedasthefollowingfunction[17]: Makespan=max{CT_ij|i∈T, i=1,2,...nandj∈VM,

j=1,2,...m} (1)

Responsetimeistheamountoftimetakenbetweensubmissionof arequestandtheﬁrstresponsethatisproduced.Thereductionin waitingtimeishelpfulinimprovingresponsivenessoftheVMs.

4.1. MathematicalModel

LetVM={VM1,VM2,...VMm}bethesetofmvirtualmachines

which shouldprocess ntasks representedby thesetT={T1,T2, ...,Tn}.Allthemachinesareunrelatedandparallelandaredenoted asRinthemodel.Weschedulenon-preemptiveindependenttasks totheseVMs.Non-preemptivetasksaredenotedasnpmtn. Non-preemptionofataskmeansthatprocessingofthattaskonavirtual

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machinecannot beinterrupted (assumingthat failure doesnot occur).

WedenoteﬁnishingtimeofataskTibyCTi.Ouraimistoreduce

themakespan which canbedenotedasCTmax.Soourmodel is

R|npmtn|CTmax.

Processingtime of a taskTi onvirtual machineVMj canbe

denotedasPij.

ProcessingtimeofalltasksinaVMjcanbedeﬁnedbyEq.(2).

Pj= n

i=1

Pij j=1,...,m (2)

ByminimizingCTmax,wegetEq.(3).FromEq.(2)and(3)wecan

implyEq.(4).

i=1

Pij≤CTmax j=1,...,m (3)

⇒Pj≤CTmax j=1,... ,m (4)

Atthetimeofloadbalancing,thetaskswillbetransferredfrom oneVMtootherinordertoreduceCTmaxaswellasresponsetime.

ProcessingtimeofataskvariesfromoneVMtootherbasedonVM’s capacity.Incaseoftransferring,completiontimeofataskmayvary becauseofloadbalancing.Optimally,

CTmax=

maxn_i₌₁CT_i,maxn_j₌₁ n

i=1 Pij

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Our load balancing technique, HBB-LB is a dynamic technique whichnotonlybalancestheloadbutalsoconsidersthepriorities oftasksinthewaitingqueuesofVMs.Ouralgorithmisan exten-sionofexistingdynamicloadbalancingtechniquesmergedwith theconceptofhoneybeebehavior.

ThetasksremovedfromoverloadedVMsactas HoneyBees. UponsubmissiontotheunderloadedVM,itwillupdatethenumber ofvariousprioritytasksandloadoftasksassignedtothatVM.This informationwillbehelpfulforothertasksi.e.,wheneverahigh pri-orityhastobesubmittedtoVMs,itshouldconsidertheVMthathas minimumnumberofhighprioritytaskssothattheparticulartask willbeexecutedearlier.SinceallVMsaresortedinanascending order,thetaskremovedwillbesubmittedtounderloadedVMs.

CurrentworkloadofallavailableVMscanbecalculatedbased ontheinformationreceivedfromthedatacenter.Basedonthis, standarddeviationhastobecalculatedtomeasuredeviationsof loadonVMs.

4.1.1. CapacityofaVM

Cj=penumj×pemipsj+vmbwj (6)

whereprocessingelement,penumjisthenumberprocessorsinVMj,

pemipsjismillioninstructionspersecondofallprocessorsinVMj

andvmbwjisthecommunicationbandwidthabilityofVMj.

4.1.2. CapacityofallVMs C= m

i=1 Ci (7)

SummationofcapacityofallVMsisthecapacityofdatacenter.

4.1.3. LoadonaVM

TotallengthoftasksthatareassignedtoaVMiscalledload.

LV,M_i,t= N

(T,t)

S(VM_i,t) (8)

LoadofaVMcanbecalculatedastheNumberoftasksattimeton servicequeueofVMidividedbytheservicerateofVMiattimet.

LoadofallVMsinadatacenteriscalculatedas

L= m

i=1 LVM_i (9) ProcessingtimeofaVM: PT_i=LVMi Ci (10) ProcessingtimeofallVMs:

PT= L

C (11)

Standarddeviationofload:

=

1 m m

i=1 (PT_i−PT)2 (12)

4.1.3.1. Load balancing decision. After ﬁndingthe workload and standarddeviation,thesystemshoulddecidewhethertodoload balancingornot.Forthis,therearetwopossiblesituationsi.e.,(1) Findingwhetherthesystemisbalanced(2)Findingwhetherthe wholesystemissaturatedornot(Thewholegroupisoverloaded ornot).Ifoverloaded,loadbalancingismeaningless.

1.FindingStateoftheVMgroup

IfthestandarddeviationoftheVMload()isunderorequal tothethresholdconditionset(Ts)[0–1]thenthesystemis bal-anced[13].Otherwisesystemisinanimbalancestate.Itmaybe overloadedorunderloaded.

If≤Ts

Systemisbalanced

Exit

2.FindingOverloadedGroup

WhenthecurrentworkloadofVMgroupexceedsthe maxi-mumcapacityofthegroup,thenthegroupisoverloaded.Load balancingisnotpossibleinthiscase.

IfL>maximumcapacity

Loadbalancingisnotpossible

Else

Triggerloadbalancing.

4.1.3.2. VMgrouping. Thevirtualmachineswillbegroupedbased ontheirloads.ThegroupsareOverloadedVMs,underloadedVMs and balancedVMs. Each setcontains thenumber of VMs.Task removedfromoneofoverloadedVMsethastoamakedecision togetplacedinoneofseverallowloadedVMsbasedontheload andtasksavailableintheunderloadedVM.Inourtechnique,this taskisconsideredasahoneybeeandlowloadedVMsare consid-eredasthedestinationofthehoneybees.Theinformationthebees (tasks)updateareloadonaVM,loadonallVMs,numberoftasksin eachVM,thenumberofVMsineachVMgroup(underloadedVM, overloadedVM,etc.,)andtaskprioritiesineachVM.Loadbalanced VMsarenotusedinswitchingoftasks.Oncethetaskswitchingis over,thebalancedVMsareincludedintotheloadbalancedVMset. OncethissethasalltheVMs,theloadbalancingissuccessfuli.e., alltasksarebalanced.

4.1.3.3. Tasktransfer. If thedecision is tobalance theload,the schedulershouldtriggertheloadbalancingaspect.Inorderto per-formloadbalancing,wehavetofindoverloadedVMs,demand(load requirement),low-loadedVMsandsupply(availableload).After this,removethetasksfromoverloadedVMs.Inordertofindthe bestVMtoqueuetheremovedtask,wehavetofindthetask prior-ity.Taskswhichareremovedearlier(Scoutbee)fromoverloaded VMsarehelpfulinfindingthecorrectlowloadedVMforcurrent task(Foragerbee).ThisForagerbeethenbecomesScoutbeefor

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nexttask.Thisprocesscontinuesuntiltheloadbalancingtaskis successful.VMselectionisdoneasfollows:

4.1.3.4. VMSelectionofdifferentprioritizedtasks.

Th→VMd|min

Th ∈VMd (13) Tm→VMd|min

Th+

Tm ∈VMd (14) Tl→VMd|min

T ∈VM_d (15)

whereTh,Tm,Tlarethetasksofhigh,middleandlowprioritycadres

respectively.

Theprioritiesoftaskscanbecategorizedin3cadres(high, mid-dle,andlow).Whenahighprioritytaskhastobesubmittedtoone oftheunderloadedmachines,ithastoconsiderthehighpriority tasksalreadysubmittedtothatmachine.Thiswillensurethatthe highprioritytaskwillﬁndthemachinewhichhaslessnumberof highprioritytasks.

4.1.3.5. AlgorithmHBB-LB. WorkloadinformationabouteachVMs insetVM.

HerewedeﬁnethreesetsbasedonloadoftheVMs.Theyare LVM(LowloadedVM)—ThesetcontainstheVMsofloadloaded. OVM(OverloadedVM)—ThesetcontainsalloverloadedVMs BVM(BalancedVM)—RemainingallVMsarebalancedandthey areavailableinthisset.

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Table1

MakespaninsecondsbeforeloadbalancingandafterloadbalancingwithHBB-LB.

No.oftasks Beforeloadbalancing(s) Afterloadbalancing(s)

10 11.4 5.7

20 27.5 15.5

30 36 19.6

40 57 28

5. Experimentalresults

Acloudcomputingsystemhastohandleseveralhurdleslike networkﬂow,loadbalancingonvirtualmachines,federationof

clouds,scalabilityandtrustmanagementandsoon.Researchin cloud computing generally focus on these issues with varying importance.Cloudsofferasetofservices(softwareandhardware) onan unprecedentedscale. Cloud Serviceshave tohandle the temporalvariationindemandthroughdynamicprovisioningor de-provisioningfromclouds.Consideringallthese,wecannotdirectly usethecloudcomputingsystem.Experimentingnewtechniques orstrategiesinrealcloudcomputingoperationsisnotpractically possibleassuchexperimentswillcompromisetheendusersQoS requirementslikesecurity,cost,speed.Changetal.discussabout fastaccesssecurityinUbuntuclouds[40].Thereis aneedfora goodsimulatorforexperimentalpurposes.Onesuchasimulator

Fig.3. ComparisonofmakespanbeforeandafterloadbalancingusingHBB-LB.

Fig.4.ResponsetimeofVMsinsecondsforHBB-LB,DLB,FIFOandWRR.

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isCloudSim [20–22].Thissimulator isa generalized simulation framework that allows modeling, simulation and experiment-ingthecloudcomputinginfrastructure andapplicationservices

[20].

Inthissection,wehaveanalyzedtheperformanceofour algo-rithmbasedontheresultsofsimulationdoneusingCloudSim.We haveextendedtheclassesofCloudSimsimulatortosimulateour algorithm.In thefollowingillustrations,wehave comparedthe

makespanof WeightedRoundRobin(WRR),FIFO,DynamicLoad Balancing(DLB)[1,4]andouralgorithm(HBB-LB)indifferentlow andoverloadedratios.

Table1illustratesthemakespanbeforeloadbalancingandafter loadbalancingwithHBB-LB.

Fig.3illustratesthecomparisonofMakespanbeforeandafter LoadbalancingusingHBB-LB.TheX-axisrepresentsthenumber oftasksandtheY-axisrepresentstheMakespan(taskexecution

Fig.6.DegreeofimbalancebetweenVMsbeforeandafterloadbalancingwithHBB-LB.

Fig.7.Comparisonbetweenalgorithms(HBB-LB,FCFS,WRRandDLB)basedondegreeofimbalance.

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andcompletion time)inseconds. Withdynamic loadbalancing usinghoneybeebehaviorinspiredloadbalancing(HBB-LB),the makespanisreducedconsiderably.Withmorenumberoftasks,the differenceinmakespantimeisquitehighandHBB-LBprovidesthe bestresults.Fig.4illustratestheresponsetimeofVMsinseconds forHBB-LB,DLB,FIFOandWRRAlgorithms.TheX-axisrepresents numberoftasksandtheY-axisrepresentstimeinseconds.It is

evidentthatHBB-LBismoreefﬁcientcomparedwithotherthree methods.

Fig.5showsthecomparisonofMakespanforHBB-LB,FIFOand WRR,DLB.TheX-axisshowsthenumberoftasksandtheY-axis showsmakespaninseconds.Itisclearlyevidentfromthegraph thatHBB-LBismoreefﬁcientwhencomparedwithother3 algo-rithms.Weusedaround500tasksforourcomparisons.Wehave

Fig.9.Comparisonofnumberoftaskmigrationswhenthereare4VMs.

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alsocomparedthedegreeofimbalance[18]inloadbetweenVMs forall4algorithms.

6. Degreeofimbalance

DI=Tmax−Tmin Tavg

(16)

whereTmaxandTminarethemaximumandminimumTiamong

allVMs,TavgistheaverageTiofVMs.Ourloadbalancingsystem

reducesthedegreeofimbalancedrastically.

Fig.6showsthedegreeofimbalancebetweenVMsbeforeand afterloadbalancingwithHBB-LB.TheX-axisrepresentsnumber oftasksandtheY-axisrepresentsthedegreeofimbalance.Itis clearlyevidentthatafterloadbalancingwithHBB-LB,thedegreeof

imbalanceisgreatlyreduced.Fig.7showsthecomparisonofdegree of imbalancebetween HBB-LB,FIFO, DLBand WRR Algorithms. TheX-axisrepresentsnumberoftasksandtheY-axisrepresents thedegreeofimbalance.HBB-LBismoreefﬁcientandhasalesser degreeofimbalancewhencomparedwithotherthreealgorithms. Wehavealsocomparedthetaskmigrationbalancingbetween VMsﬁnally.Taskmigrationisnumberoftasksreassignedbetween VMs.Alltheseresultsshowthatouralgorithmperformsbetterthan theDLBandHDLBalgorithms.

Figs.8–12showstaskmigrationwhennumbersofVMsare var-iedfrom3to7forHBB-LB,DLBandHDLBtechniques.Inallthe ﬁvecasesitisclearlyevidentthatthetaskmigrationisveryless comparedwithothertwopopulartechniquesirrespectiveofthe numberofVMs.Fig.13(a)–(d)showsthecomparisonoftask migra-tionvs. number ofvirtual machineswhennumber of tasksare

Fig.13.(a)Comparisonofnumberoftaskmigrationsvs.numberofvirtualmachinesforasetof10tasks.(b)Comparisonofnumberoftaskmigrationsvs.numberofvirtual

machinesforasetof20tasks.(c)Comparisonofnumberoftaskmigrationsvs.numberofvirtualmachinesforasetof30tasks.(d)Comparisonofnumberoftaskmigrations

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variedfrom10to40.ResultsillustratethatHBB-LBismore efﬁ-cientwithlessernumberoftaskmigrationswhencomparedwith DLBandHDLBtechniques.

7. Conclusion

Inthispaper,wehaveproposedaloadbalancingtechniquefor cloudcomputingenvironmentsbasedonbehaviorofhoney bee foragingstrategy.Thisalgorithmnotonlybalancestheload,but alsotakesintoconsiderationtheprioritiesoftasksthathavebeen removedfromheavilyloadedVirtualMachines.Thetasksremoved fromtheseVMsaretreatedashoneybees,whicharethe informa-tionupdatersglobally.Thisalgorithmalsoconsidersthepriorities ofthetasks.Honeybeebehaviorinspiredloadbalancingimproves theoverallthroughputofprocessingandprioritybasedbalancing focusesonreducingtheamountoftimeataskhastowaitonaqueue oftheVM.Thus,itreducestheresponseoftimeofVMs.Wehave comparedourproposedalgorithmwithotherexistingtechniques. Resultsshowthatouralgorithmstandsgoodwithoutincreasing additionaloverheads.Thisloadbalancingtechniqueworkswellfor heterogeneouscloudcomputingsystemsandisforbalancing non-preemptiveindependenttasks.Infuture,weplantoextendthis kindofloadbalancingforworkﬂowswithdependenttasks.This algorithmconsiderspriorityasthemainQoSparameter.Infuture, weplantoimprovethisalgorithmbyconsideringotherQoSfactors also.

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DhineshBabuL.D.receivedB.EinElectricaland

Elec-tronicsEngineeringandM.EdegreeinComputerScience

andEngineeringfromtheUniversityofMadrasin1998

and2001,respectively.HeisworkingtowardhisPhD

atVITUniversity.HeiscurrentlyafacultyinSoftware

EngineeringDivisionoftheSchoolofInformation

Tech-nologyandEngineeringatVITUniversity,Vellore,India.

HehasservedasDivisionLeaderofSoftwareEngineering

Division.HisresearchinterestsincludeCloudComputing,

GridandDistributedComputing,ComputerandSoftware

Security,SoftwareEngineering,ERP,BusinessInformation

SystemsandSupplychainManagement.

P. Venkata Krishna received the B.Tech. degree in

electronics and communication engineering from Sri

VenkateswaraUniversity,Tirupathi,India, theM.Tech.

degreeincomputerscienceandengineeringfromthe

RegionalEngineeringCollege,Calicut,India,andthePh.D.

degreefromVITUniversity,Vellore,India.Heiscurrently

aProfessorintheSchoolofComputingSciences,VIT

Uni-versity.Hisresearchinterestsincludemobile,wireless

SciVerse

w w w . e l s e v i e r . c o m / l

http://dx.doi.org/10.1109/JSYST.2012.2208153