dc.creatorMonge, David A.
dc.creatorBělohradský, Jiří
dc.creatorGarcía Garino, Carlos
dc.creatorŽelezný, Filip
dc.date2011-08
dc.date2011
dc.date2021-10-04T14:44:44Z
dc.date.accessioned2023-07-15T03:39:21Z
dc.date.available2023-07-15T03:39:21Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/126141
dc.identifierhttps://40jaiio.sadio.org.ar/sites/default/files/T2011/HPC/1102.pdf
dc.identifierissn:1851-9326
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7466540
dc.descriptionThrough the years, scientific applications have demanded more powerful and sophisticated computing environments and management techniques. Workflows facilitated the design and management of scientific applications. The complexity of to day's workflows demand a high amount of resources and mechanisms for provisioning them. The execution of scientific workflow applications is a complex task and depends on how the resources are assigned. Scheduling is the name given to the process that assigns computing resources to the tasks comprised in a workflow. This work presents a scheduling algorithm (PPSA) for workflows tightly coupled to a performance prediction module (PEM). A set of experiments was developed for measuring the performance of the algorithm using the information provided by the proposed performance module. The proposed algorithm is compared with an algorithm included in the well-known workflow middlewares Condor DAGMan and ASKALON.
dc.descriptionSociedad Argentina de Informática e Investigación Operativa
dc.formatapplication/pdf
dc.format130-144
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.subjectCiencias Informáticas
dc.subjectWorkflow
dc.subjectScheduling
dc.subjectPerformance Prediction
dc.subjectNonparametric Regression
dc.titleA Performance Prediction Module for Workflow Scheduling
dc.typeObjeto de conferencia
dc.typeObjeto de conferencia


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