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http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10021Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Jananie, J. | - |
| dc.contributor.author | LiChen | - |
| dc.contributor.author | FeiXu | - |
| dc.contributor.author | BoLi | - |
| dc.date.accessioned | 2023-12-29T07:19:33Z | - |
| dc.date.available | 2023-12-29T07:19:33Z | - |
| dc.date.issued | 2022 | - |
| dc.identifier.uri | http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10021 | - |
| dc.description.abstract | Withtheabilitytosimplifythecodedeploymentwithone-clickuploadandlightweightexecution,serverlesscomputinghas emergedasapromisingparadigmwithincreasingpopularity.However,thereremainopenchallengeswhenadaptingdata-intensive analyticsapplicationstotheserverlesscontext,inwhichusersofserverlessanalyticsencounterthedifficultyincoordinatingcomputation acrossdifferentstagesandprovisioningresourcesinalargeconfigurationspace.Thispaperpresentsourdesignandimplementationof Astrea,whichconfiguresandorchestratesserverlessanalyticsjobsinanautonomousmanner,whiletakingintoaccountflexibly-specified userrequirements.Astreareliesonthemodelingofperformanceandcostwhichcharacterizestheintricateinterplayamongmultidimensionalfactors(e.g.,functionmemorysize,degreeofparallelismateachstage).Weformulateanoptimizationproblembasedon user-specificrequirementstowardsperformanceenhancementorcostreduction,anddevelopasetofalgorithmsbasedongraph theorytoobtaintheoptimaljobexecution.WedeployAstreaintheAWSLambdaplatformandconductreal-worldexperimentsover representativebenchmarks,includingBigDataanalyticsandmachinelearningworkloads,atdifferentscales.Extensiveresults demonstratethatAstreacanachievetheoptimalexecutiondecisionforserverlessdataanalytics,incomparisonwithvariousprovisioning anddeploymentbaselines.Forexample,whencomparedwiththreeprovisioningbaselines,Astreamanagestoreducethejobcompletion timeby21%to69%underagivenbudgetconstraint,whilesavingcostby20%to84%withoutviolatingperformancerequirements. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.subject | Cloud computing | en_US |
| dc.subject | Serverless computing | en_US |
| dc.subject | Resource provisioning | en_US |
| dc.subject | Modeling | en_US |
| dc.subject | Optimization | en_US |
| dc.title | Astrea: Auto-Serverless Analytics Towards Cost-Efficiency and QoS-Awareness | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Computer Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| AstreaAuto-ServerlessAnalyticsTowards Cost-EfficiencyandQoS-Awareness.pdf | 2.73 MB | Adobe PDF | View/Open |
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