International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
A review of round robin algorithm on cloud computing task allocation.
Kishan
Raj1, Shubham kumar2
1Assistant Professor, Meerut Institute Of Technology, Meerut, India
2Assistant Professor, Department of computer science MGCU, Bihar, India
Abstract - Thecloudcomputinginfrastructurehasmanyissuesincludingscheduling,budgetingloadbalance.Thegoalisto useresourceefficientlyanddecreaseresourceconsumptioninthecloudenvironment.Thiscanbeachievedbyincreasingthe LBratewhenselectingthebestresourcesforlowworkfailuresrateswithlowleadtimes.Theroundrobinisasimplestand mostefficientalgorithm.ItgivesmoreaccurateresultthananyotherAlgorithmtoscheduleVMtask’sincloudcomputing. ManyresearchscholarsproposedvarioustechniqueinordertoimprovetheRRalgorithmandselecteddynamictimequantum whichplaysmajorroleinimprovingthisalgorithm.Becauseoftakinglongertimeinordertoswapcontextsmanyresearchers provideusadynamictimequantumstrategytoovercomethistypeoflimitations.ManydynamictimeSliceisproposed,soin thispaperwehavecomparedsomeoftheapproacheswithconcernsofitswaitingtime,turnaroundtime,andefficiencyofthe algorithm.Inreality,thearrivedjobsaremadeupofavarietyofinterconnectedtasks,someofwhichmayrunondifferentVMs orondifferentcoresofthesameVM.Additionally,thejobsarriveatrandomintervalsthroughouttheserver’soperationunder differentloadsituations[2].
Key Words: CC,RR,VM,Timequantum
1.INTRODUCTION
CloudcomputinghasgainedincreasedimportanceintherealmofITservicesduetoitsremarkableadaptabilityandtheproper distributionof resources.Theeffectivenessofa cloudcomputing environmentiscloselytied tohowtasksarescheduled. Efficienttaskschedulingguaranteestheappropriateallocationofresourcesforthetasks’associatedrequests.Cloudcomputing isanemergingtechnologyincurrentera. Itprovideutilityascomputinglikeelectricity,water.Cloudcomputingprovide serviceslikevirtualizationwithoperatingsystem,softwaredevelopment,storagewithoutanyphysicaldevices.Anyonecan accesstheseserviceswithouthardware,basicallycloudisachain ofnetworks thatcanbeaccessedfromanywhereusingonly internetconnections.Thistechnologyreducedcostofcomputingandmadeanyoneaccesslargefilesorcomputingirrespective ofhardwaredevices.CloudwasfirstintroducedbySalesforce.cominlate1990s.Todaymajorproviderofcloudservicesare Amazon.com,Googleetc.Amazonislargestgiantincloudtechnologywithit’sheadquarterinSanFranciscoCloudcomputing provideusservicesthatanyCompanyhavefacilitiestorentaccesstoanything.Inthistechnologyusercansimplypayforwhat theyusewhentheyuse.ForexampleGmail,Netflix,cloudbackupphotosusesfeaturesofcloud.Theconceptofrentingaccess tocomputingpowerhasresurfacedagainandagainintheapplicationservice.Cloudcomputingisapopularoptionforreasons suchascostsaving,increasedproductivity,speedandefficiency,performanceandsecurity.Cloudcomputingisanabstractof computing, memory and network infrastructure collected as a stage on which applications and systems can be deployed swiftly.ItisoneofthebestandemergingtechnologythathaschangedactivitiesofHumanlifeandfastinabigwayandalso providingvariousservices,resourcesusingtheInternetandcloud-basedresourcesinrecenttimesthroughvirtualization .Cloudcomputinginsimplewordsmeansallocationofresources,retrievalofdataandprogramsovertheInternetwithout physicalstorageratherthanourcomputer’sharddrive.Thoseusersthatareworkingoutsidetheworkingplacesareableto accessresourcesthroughinternetconnectingdevices.NIST(NationalInstituteofStandardsandTechnology)haslistedfive essentialrequiredcharacteristicsofcloudcomputingfromvirtualmachinethatincludeon-demandofcloudcomputingofselfservice,broadnetworkaccess,resourcepooling,rapidelasticityandmeasuredservice.Italsoinvolvestheconceptsofparallel processing and distributed computing providing the distributed the resources in many consumers by means of Virtual Machines (VMs) hosted by physical servers. It is developing rapidly and it will be the next generation technology where humanscanuseresourcesremotelywithoutconcernoftime.Thistechnologyhaschangedhumanlifeinallareaswiththehelp oftechnology.Thistechnologyhasexpandedrapidlyduringcovidtimeduetoextensivelyuseoftechnicalresources. Itis emerginginternetbasedtechnologyusedtostoretheinformationdataandaccessingresourceandapplicationthroughwebon internet.Itstoresveryhugeamountofdataandinformationwithoutanyphysicalstoragedeviceanditworksundercertain terms and conditions. Virtual machine (VM) is one the main element of virtualization of cloud computing, when a single resourceofcloudcomputingappearedasmultipleresourcethisprocesscanbeachievedwiththehelpofvirtualmachine. Cloudtechnologyisemergingrapidlyandusedindifferentfieldoflifelikee-commerce,AI,education,engineeringtechnology, dataintensiveapplications,health,geospatialsciencesandmanymoredifferentscientificandbusinessdomainsusescloudbasedserver.Cloudcomputinghasbecomeanrespectedtechnologyinworldwideduetoofferingahugeamountofstorageand
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
resource without any physical storage to different companies and organization to access this resource with proper management,ruleandregulations.Someofthekeyfeaturesarevirtualization,viability,widenetworkaccess,largeinformation storage,automatedsystem,economicalsecurity,scalabilityandmanymore.Cloudresourcesarevariablewithrentingand releasingforeachuserspecificrulesandregulationswiththehelpoftheInternet.According toNIST,cloudcomputingisa modelthatsupportson-demandnetworkaccesstoasharedpoolofconfigurablecomputingsoftwareresources.inacloud computingenvironment,therearethreemaincategoriesoftaskschedulingalgorithmswhichareconventionalalgorithmssuch asFIRSTCOMEFIRSTSERVE,SHORTESTJOBFIRST,ROUNDROBIN,HEURISTICALGORITHM,MIN-MAXandmetahieuristic algorithmssuchasANTCOLONYOPTIMIZATIONandPARTICLESWARMOPTIMIZATION.
1.1 Models in cloud computing
Therearetwotypesofmodelsincloudcomputing–
1.Servicemodel
2.Deploymentmodel
1.1.1 CLOUD SERVICE MODELS
(1)Infrastructureasaservice
(2)Platformasaservice
(3)Softwareasaservice
1.1.2 CLOUD DEPLOYMENT MODELS
(1)Publiccloud
(2)Privatecloud
(3)Hybridcloud
(4)Communitycloud
1.2 Round robin in cloud computing
[2]Thisalgorithmemploysasequentialmethodtoselectserversforupcomingrequests.However,asignificantissuewiththis approachliesintheinconsistentperformanceofservers.Toaddressthischallenge,innovativestrategieshavebeendeveloped. Itisoneofsimplest,old,fairestandmostpopularlyusedschedulingalgorithmsdesignedespeciallyfortimesharingsystems.In thisalgorithm,
Table-1 :Descriptionofterms.
The criteria
The Description
Minimizingthecontextswitch(CS) It is the process of changing the task’s status from oneactivitytoanother.
MinimizingTurnaroundtime(TAT) Itisthetimeperiodbetweenthestartingtimeofthe processanditscompletiontime
Minimizingthewaitingtime(WT) Itisthetotaltimespentbytheprocessinwaitingin betweentasks.
MinimizingResponsetime(RT) It is the time period in between queue submission anditsfirstreply.
MaximizingCPUutilization
Maximizingthroughput
TryingtomaketheCPUasbusyaspossible.
It is the number of tasks which completed in a specifictimeunit.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
Timequantumisdefinedwhichissmallestunitoftime.Alltherunableprocessesarekeptinacircularqueueand theCPUschedulergoesthroughalltheprocesseswithrespecttotimequantumoftheCPUscheduler.Newjobsare keptintailofthequeue.CPUschedulerpicksthefirstprocessesfromthequeueandexecuteitwithrespecttotime quantum.Iftheprocessdoesnotfinishinthestatictimequantum,theCPUispreemptedandtheprocessisaddedto lastofthequeue.IftheprocessisfinishinthegiventimequantumitwillreleaseCPUautomatically.CPUscheduler startexecutingthenextprocessofthereadyqueue.Everytimewhennextprocessstartexecuting,acontextswitch occurs,whichaddoverheadtotheprocessexecutiontime.
Thewaitingtimeforeachtask
WT=turnaroundtime–bursttime
P1:12-5=7
P2:10-4=6
P3:4-2=2
P4:5-1=4
Average wait time : 4.7
Figure 1:RRalgorithmprocessincloudcomputing
Figure 2: GanttchartforRR
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
2. Literature Reviews
InpaperP.Sangwanetal.suggestedanewmodificationonRRalgorithm.Theprocesseshavebeenstoredinthereadyqueuein thesameorderastheyarrived.Thequantumtimehasbeencalculatedbasedonthemeanofthebursttimeoftheprocesses. Theevaluationresultsofthisapproachconductedusingjavalanguageanditshowsagoodperformanceintermofwaitingtime andturnaroundtime.InanewalgorithmwasbuiltforRRbasedonthefollowingalgorithmsMRRAandSRBRR.Ithasbeen proposedtoenhanceresourceallocationincloudcomputing.ItisadynamicRRwherethequantumtimeischangingineach roundanditisequaltothesumofmeanandmedianofprocessesdividedbytwo.Theexperimentalanalysisshowstheout performance of the proposed algorithm in term of the number of context switches, average waiting time and average turnaroundtime.[10]
InapaperAbdulrahmanAbdulkarimetal.suggestedthatTimequantumissettobeequaltotheaverageofbursttimeof requestsforcontextswitchingwhichisdeclaredasthevariable‘round’.Thefirstrequestinthereadyqueuewouldbeassigna VM,theretheVMisallocatedtoexecutetherequestandthestateoftheVMischangedtoBusy.Afterrequestisbeingexecuted, VMisde-allocatedandthestatechanged back toAvailable.Thenwehavea context switchwitha newbursttimeforthe request.
InapaperPandabaPradhanetal.suggestedthatthisalgorithmbeginswiththetimeequalstothetimeoffirstrequest,which changesaftertheendoffirstrequest.Whenanewrequestisaddedintothereadyqueueinordertobegranted,thealgorithm calculatestheaverageofsumofthetimesofrequestsfoundinthereadyqueueincludingthenewarrivalrequest.Thisneeds tworegisters:(i)SR:Tostorethesumoftheremainingbursttimeinthereadyqueue(ii)AR:Tostoreavg.ofthebursttimesby dividingthevaluefoundintheSRbythecountofrequestsfoundinthereadyqueue.Afterexecution,ifrequestfinishesits bursttime,thenitwillberemovedfromreadyqueueorelseitwillmovetotheendofthereadyqueue.SRwillbeupdatedby subtract.[11]
In a paper D.Gritto et al. suggested that the time quantum selection in Round Robin algorithm is a big challenge. The inappropriatetimeslicemaydegradethecloudsystemperformance.WhenthetimesliceislargertheRoundRobinalgorithm behaveslikeFirstComeFirstServer(FCFS)algorithm,similarlyforthesmallertimeslicethenumberofcontextswitchingis more.ContextswitchingimpliesthemethodofVMswitchingbetweenonecloudlettotheothercloudlet.Whileswitching,the
Figure 3: Designofresourcescheduling
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
statusofthecloudletisstoredinVMbufferinordertorestoreorresumebackforthecloudletexecutioninnearfuture.More contextswitchingdegradestheperformance.Sotheproposedworkmakesanattempttoestimateanoptimaltimequantum thatreducestheresponsetime,waitingtime,turnaroundtimeandthenumberofcontextswitching.Thetimequantumis allocatedusingmidandaverageremainingexecutiontimeapproximationmethod.TheEnhancedTimeQuantumRoundRobin Algorithm(ETQRRA)arrangesthecloudletsinascendingorderaspertheirexecutiontime.Whenthenumberofcloudletsin thereadyqueueRQoftheVMisoddthenthemidapproximationmethodisused.Ifthenumberofcloudletinthereadyqueue iseventhentheaverageremainingexecutiontimeapproximationmethodisused.[4]
ElamraniChakenetal.suggestedthatIncaseofawaitinglistwiththreeorlesstasks.TheproposedalgorithmusestheShorter JobFirstAlgorithmdesignandallocatesthetimenecessaryforeachtaskuntilitsexecution-Incaseofawaitinglistwithmore thanthreetasks.IfthetotalcountoftasksonthelistisapairnumberoTheproposedalgorithmdynamicallycalculatesatime quantumastheaverageoftasksbursttime.IfthetotalcountoftasksonthelistisanimpairnumberoTheproposedalgorithm dynamicallycalculatesatimequantumasthemedianoftasksbursttimeForeachoftheconditionsthattreatsthecaseofa waitinglistwithmorethanthreetasks,theproposedalgorithminjectsatesttoverifyiftherearemorethanthreereoccurring taskswiththesamebursttime.Ifitisthecase,thetimequantumissettothevalueoftherepetitivetaskwithhighestburst timeonthewaitinglist[5].
InapaperNermeenGhazyetal.suggestedthatmanyimprovementshavebeendevelopedtheRoundRobinalgorithmfortask schedulingtobecompatiblewithtimesharingsystemsandreal-timesystems.ThemostimportantissueintheRRalgorithmis thetimequantum.ThemaincontributionofthispaperisenhancingtheefficiencyoftheRRalgorithmbyproposingaMedian MeanRoundRobinalgorithm(MMRR).Itfoundanintelligentdynamictimequantumwhichiscalculatedas(median+mean)/2 andtakingtheremainingbursttimeofthecurrentprocessintoaccount.Iftheremainingbursttimeofthecurrentprocessis lessthanhalfofthetimequantum,itallocatedagain.Otherwise,itisplacedattheendofthereadyqueue.Eachcyclewillhave itsTQbasedontheirbursttimefortheseprocesses.ByusingavariableTQdependsonthebursttime,itleadtominimizethe averagewaitingtime,turnaroundtimeandnumberofcontextswitches.Theexperimentalresultsshowedthattheproposed algorithmenhancestheperformanceofthesystembyreducingtheaveragewaitingtime,turnaroundtimeandnumberof contextswitches.[6]
V.RhymendUthariaraj etal.observedthatTimequantum=squarerootofsumofthemedian+highestbursttime.Time quantum=infirstroundhighestbursttimeinreadyqueue.Calculate0.8proportionofthebusttimeandsetitasthevalueof quantumtime.Iftheprocessinthequeue<quantumtimetaskisallottedandcompletionofthetasktimequantumisthevalue ofhighestbursttime.[8]
MaysoonA.mohammadetal.proposedamethodofdynamictimequantuminwhichProcessesarearrangedinincreasing orderinthereadyqueue.NewprioritiesandquantumtimesareassignedaccordingtotheCPUburstsoftheprocesses;the processwiththelowestbursttimeissetwiththehighestpriority.ChoosethefirstprocessintheRRfashionandassignitsburst timeasquantumtimeforthisround,andallocateCPUtothisprocessonlyforonequantumtime.Calculatequantumtimesofall theprocessesinthisrounddependingontheirprioritiesandthequantumtimeofthiscyclebyusingasimpleformula,whichis q=k+p–1,whereqisthenewquantumtime,kistheoldquantumtimeandpisthepriorityoftheprocessesintheready queue.Setdifferentquantumtimesfortheprocessesaccordingtotheirpriorities.Thehighestpriorityprocesswillgetthe largestquantumtime,whichisq,andthelowestpriorityprocesswillgetthesmallestquantumtime,whichisk.Eachprocess getsthecontroloftheCPUuntiltheyfinishtheirexecution.Repeatthestepsaboveuntilallprocessesarecompleteandready queue is assigned NULL. Apply the original RR and IRRVQ and our PDQT with the priorities and new, different quantum times.[7]
InapapersuggestedbyMd.Sohrawordietal.proposedamethodthattheproposedRRDTQalgorithmusesdynamictime quantuminCPUscheduling.Ittakestheintegeraveragevalueofthebursttimeoftheremainingwaitingprocessesintheready queueastimequantum.Aftereachcycle,thetimequantumisupdatedbycalculatingtheintegeraveragevalueofthebursttime oftheprocessesinthereadyqueue.Ifthereadyqueuecontainsonlyonewaitingprocess,thenthetimequantumwillbethe bursttimeofthatprocesses.Whentheexecutionoftheprocessesiscompleted,thenitwillberemovedfromreadyqueueand otherwiseitwillbeaddedatthetailofthereadyqueuewiththeremainingrequiredbursttime.[1]
[2]Niraj Patel, Sandip Chauhan observe and used a new approach with 5 Server for Clustering. It can be run on dedicatedmachineorsinglemachine.4ofthemworkasClusterMember(S01,S02,S03,orS04).Sameapplication(client’s request)willdeployintoall4Serve.Remaining1workasLoadBalancer.Server(LBS).CalculateWeightofEachServerbasedon ProcessingPower,BandwidthandMemory.Forexample:Thefirstservercanhandle100request/sec,thesecondcanhandle 300request/sec,thethirdcanhandle200request/secandthefourthcanhandleonly25request/sec.AnyoneServerreserve
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
forlongcompletiontimerequestandother3serverfornormalrequest.Iflongcompletiontimerequestfound.Firstcheck Server(S01)status,iffreeallocatetoit.Ifserverisbusy,thencheckotherserversstatus,ifanyoneisfreethenallocatetoit.Ifall serversarebusy,waitforanyoftheserverbecomefree.Ifshortrequestfound,itwillfirstcheckstatusofS02,S03,andS04any oftheseserverisfreethenallocatetoit.OtherwisecheckS01serverstatus.Iftheseserversisfree thenallocatetoit.Ifall serversareheavilyloaded,thenonlylongcompletiontimerequestallocatetoS01andallnormalrequestallocatebetweenS02, S03,S04.Nowhere,stepofourproposedalgorithmshownasbelow:1.CreateMap(weightMap)andaddserverandaccording weight.2.CreateMap(loadMap)totrackcurrentloadofeachServer.3.CreateMap(avgTimeMap)totrackaveraggetimeof eachrequest.4.CreateMap (waitingMap)totrack notcompleted request.5.Wheneverclient’srequestscome,first itwill forwardtotheLoadBalancer.6.BalancerlooksupinavgTimeMapforaveragetimeofRequestandputitinwaitingMap. 7.CheckforServerstatusaspertypeofrequest.8.LoadBalancerredirectrequesttoServerandincrementcurrentpointerof Server. 9. After Processing of request decrement pointer of server, update Average time of request in Map (AvgTimeMap).10).LoadBalancerwilltakenextrequestfromMap(waitingMap)andrepeat5throughLoadBalancingusing DWARRAlgorithminCloudComputing.
Paper
DWRRalgorithm(nirajpatel, sandeepchauhan)
DTQA(Bhavinfataniya)
DRSS(m.satish,A.prakash)
ANPDQ(Abidurrehman khattak) 2010
Cloudanalysttools Throughputincreasesandresourceutilizes efficiently
Eclipse lessavgTAT,lessavgWT
Euclyptus averageTATandno.ofcontextswitch reducedto20percent
Uniprocessor environment drasticallydecreasecontextswitching
RPJS(k.sutha) 2016 satisfybetterinallparameter
NRRTSA 2023
asharedapproachofdynamic loadbalancing(snehlata mishra)
m-Throttled:DLBA (AmrutanshuPanigrahi)[9]
2016
2020
Notebookkernel reducingtheaverageturn-aroundtime, minimizingaveragewaitingtime,and lesseningthenumberofcontextsswitching
Cloudsimsimulator costofeachdatacenterismoreaccurately thanthrottledandequalloadsharing algorithm
Cloudsimsimulator increasedUBresponsetimeandtheDC processingtime
ISRBRR(P.S.Varma) 2013 C++language decreasedthenumberofCS,averageWT andaverageTAT
SRBRR(S.Saeidi) 2012
ProposedAlgorithm (S.Banerjee)
2018
TSPBRR(S.Elmougy) 2017
3. CONCLUSIONS
Lingo8.0software TheexperimentalshowsthatSRBRR achievesagoodperformancecompared withtheothers
Cloudsim outperformanceoftheproposedalgorithm intermofthenumberofCS,averageWT andaverageTAT
Cloudsim goodperformanceinreducingtheWTand RTcomparedwithSJF,RRandTimeSlice PriorityBasedRR
Cloudcomputingisemergingtechnologywithlotsofconcernsliketaskcongestion.Itisnecessarytoresolvethisproblemwith bestmethod.Afterobservingalltheparametersandreviewingmanydynamicapproachesusedbyresearchersregardingtask scheduling using round robin CPU scheduling methods we concluded in this paper that a bit of changes in conventional approachesgivesusbetterresultincontextwithimprovedturnaroundtime,improvedwaitingtime,decreasenumberof contextswitches,minimumthroughput.Therearealsomanyapproachesotherthanroundrobintoallocatetasksininvirtual
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
machineincloudcomputing.wehaveonlyconcludedroundrobinmethodinthispaper,infuturewewillobserveothermethod withrespecttoallparameters.
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