Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9956
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dc.contributor.authorJananie, Jarachanthan-
dc.contributor.authorLi, Chen-
dc.contributor.authorFei, Xu-
dc.date.accessioned2023-12-19T04:19:43Z-
dc.date.available2023-12-19T04:19:43Z-
dc.date.issued2023-
dc.identifier.issn979-8-3503-9973-8-
dc.identifier.urihttp://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9956-
dc.description.abstract—Serverlesscomputinghasbecomeincreasinglypopularforcloudapplications,duetoitscompellingpropertiesof high-levelabstractions,lightweightruntime,highelasticityand pay-per-usebilling.Inthisrevolutionarycomputingparadigm shift,challengesarisewhenadaptingdataanalyticsapplications totheserverlessenvironment,duetothelackofsupport forefficientstatesharing,whichattractever-growingresearch attention.Inthispaper,weaimtoexploittheadvantagesoftasklevelorchestrationandfine-grainedresourceprovisioningfor dataanalyticsonserverlessplatforms,withthehopeoffulfilling thepromiseofserverlessdeploymenttothemaximumextent. Tothisend,wepresentACTS,anautonomouscost-efficient taskorchestrationframeworkforserverlessanalytics.ACTS judiciouslyschedulesandcoordinatesfunctiontaskstomitigate cold-startlatencyandstatesharingoverhead.Inaddition,ACTS explorestheoptimizationspaceoffine-grainedworkloaddistributionandfunctionresourceconfigurationforcostefficiency. WehavedeployedandimplementedACTSonAWSLambda, evaluatedwithvariousdataanalyticsworkloads.Resultsfrom extensiveexperimentsdemonstratethatACTSachievesupto 98%monetarycostreductionwhilemaintainingsuperiorjob completiontimeperformance,incomparisonwiththestate-ofthe-artbaselines.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectServerless computing,en_US
dc.subjectCost-efficiency,en_US
dc.subjectData analytics,en_US
dc.subjectCloud resource provisioningen_US
dc.titleACTS:AutonomousCost-EfficientTask OrchestrationforServerlessAnalyticsen_US
dc.typeArticleen_US
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