Managing Workflows on top of a Cloud Computing Orchestrator for using heterogeneous environments on e-Science

dc.creatorCarrión, Abel
dc.creatorCaballer, Miguel
dc.creatorBlanquer, Ignacio
dc.creatorKotowski, Nelson
dc.creatorJardim, Rodrigo
dc.creatorDàvila, Alberto Martin Rivera
dc.date2018-05-30T12:41:30Z
dc.date2018-05-30T12:41:30Z
dc.date2018
dc.date.accessioned2023-09-26T20:39:45Z
dc.date.available2023-09-26T20:39:45Z
dc.identifierCARRIÓN, Abel et al. Managing Workflows on top of a Cloud Computing Orchestrator for using heterogeneous environments on e-Science. International Journal of Web and Grid Services, v. 13, n. 4, p. 1-38, Mar. 2018.
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/26682
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8861541
dc.descriptionVersão preprint do autor: O autor pode arquivar a versão preprint (i.e. antes do peer-review)
dc.descriptionScientific Workflows (SWFs) are widely used to model processes in e-Science. SWFs are executed by means of Workflow Management Systems (WMSs), which orchestrate the workload on top of computing infrastructures. The advent of cloud computing infrastructures has opened the door of using ondemand infrastructures to complement or even replace local infrastructures. However, new issues have arisen, such as the integration of hybrid resources or the compromise between infrastructure reutilization and elasticity. In this article we present an ad-hoc solution for managing workflows exploiting the capabilities of cloud orchestrators to deploy resources on demand according to the workload and to combine heterogeneous cloud providers (such as on-premise clouds and public clouds) and traditional infrastructures (clusters) to minimize costs and response time. The work does not propose yet another WMS, but demonstrates the benefits of the integration of cloud orchestration when running complex workflows. The article shows several configuration experiments from a realistic comparative genomics workflow called Orthosearch, to migrate memory-intensive workload to public infrastructures while keeping other blocks of the experiment running locally. The article computes running time and cost suggesting best practices.
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier
dc.rightsopen access
dc.subjectWorkflow
dc.subjectWorkflow Management Systems
dc.subjectCloud Orchestrator
dc.subjectMulti-plataform
dc.subjecte-Science
dc.subjectCloud Computing
dc.subjectComparative genomics
dc.titleManaging Workflows on top of a Cloud Computing Orchestrator for using heterogeneous environments on e-Science
dc.typePreprint


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