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dc.contributor.authorBousselmin, K.
dc.contributor.authorBen Hamida, Sana
dc.contributor.authorRukoz, Marta
dc.date.accessioned2020-09-04T10:41:44Z
dc.date.available2020-09-04T10:41:44Z
dc.date.issued2020
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20969
dc.language.isoenen
dc.subjectScientific Workflowen
dc.subjectData intensiveen
dc.subjectCat Swarm Optimizationen
dc.subjectMulti-objective Schedulingen
dc.subjectLIGOen
dc.subject.ddc005en
dc.titleBi-objective CSO for Big Data ScientificWorkflows scheduling in the Cloud: case of LIGO workflowen
dc.typeCommunication / Conférence
dc.description.abstractenScientific workflows are used to model scalable, portable, and reproducible big data analyses and scientific experiments with low development costs. To optimize their performances and ensure data resources efficiency, scientific workflows handling big volumes of data need to be executed on scalable distributed environments like the Cloud infrastructure services. The problem of scheduling such workflows is known as an NP-complete problem. It aims to find optimal mapping task-to-resource and data-to-storage resources in order to meet end user’s quality of service objectives, especially minimizing the overall makespan or the financial cost of the workflow. In this paper, we formulate the problem of scheduling big data scientific workflows as bi-objective optimization problem that aims to minimize both the makespan and the cost of the workflow. The formulated problem is then resolved using our proposed Bi-Objective Cat Swarm Optimization algorithm (BiO-CSO)which is an extension of the bio-inspired algorithm CSO. The extension consists of adapting the algorithm to solve multi-objective discrete optimization problems. Our application case is the LIGO Inspiral workflowwhich is a CPU and Data intensive workflow used to generate and analyze gravitational waveforms from data collected during the coalescing of compact binary systems. The performance of the proposed method is then compared to that of the multi-objective Particle Swarm Optimization (PSO) proven to be effective for scientific workflows scheduling. The experimental results show that our algorithm BiO-CSO performs better than themulti-objective PSO since it provides more and better final scheduling solutions.en
dc.identifier.citationpages615-624en
dc.relation.ispartoftitleProceedings of the 15th International Conference on Software Technologies - Volume 1: ICSOFTen
dc.relation.ispartofpublnameSciTePressen
dc.subject.ddclabelProgrammation, logiciels, organisation des donnéesen
dc.relation.ispartofisbn978-989-758-443-5en
dc.relation.conftitle15th International Conference on Software Technologies (ICSOFT 2020)en
dc.relation.confdate2020-07
dc.relation.confcityParisen
dc.relation.confcountryFranceen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2020-09-04T10:38:40Z
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hal.person.labIds989
hal.person.labIds989


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