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A framework and a performance assessment for serverless MapReduce on AWS Lambda

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A framework and a performance assessment for serverless MapReduce on AWS Lambda

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dc.contributor.author Giménez-Alventosa, Vicent es_ES
dc.contributor.author Moltó, Germán es_ES
dc.contributor.author Caballer Fernández, Miguel es_ES
dc.date.accessioned 2020-03-25T07:21:04Z
dc.date.available 2020-03-25T07:21:04Z
dc.date.issued 2019-08 es_ES
dc.identifier.issn 0167-739X es_ES
dc.identifier.uri http://hdl.handle.net/10251/139362
dc.description.abstract [EN] MapReduce is one of the most widely used programming models for analysing large-scale datasets, i.e. Big Data. In recent years, serverless computing and, in particular, Functions as a Service (FaaS) has surged as an execution model in which no explicit management of servers (e.g. virtual machines) is performed by the user. Instead, the Cloud provider dynamically allocates resources to the function invocations and fine-grained billing is introduced depending on the execution time and allocated memory, as exemplified by AWS Lambda. In this article, a high-performant serverless architecture has been created to execute MapReduce jobs on AWS Lambda using Amazon S3 as the storage backend. In addition, a thorough assessment has been carried out to study the suitability of AWS Lambda as a platform for the execution of High Throughput Computing jobs. The results indicate that AWS Lambda provides a convenient computing platform for general-purpose applications that fit within the constraints of the service (15 min of maximum execution time, 3008 MB of RAM and 512 MB of disk space) but it exhibits an inhomogeneous performance behaviour that may jeopardise adoption for tightly coupled computing jobs. es_ES
dc.description.sponsorship This study was supported by the program "Ayudas para la contratacion de personal investigador en formacion de caracter pre-doctoral, programa VALid-d" under grant number ACIF/2018/148 from the Conselleria d'Educacio of the Generalitat Valenciana, Spain. The authors would also like to thank the Spanish "Ministerio de Economia, Industria y Competitividad" for the project "BigCLOE" with reference number TIN2016-79951-R. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation AEI/TIN2016-79951-R es_ES
dc.relation GENERALITAT VALENCIANA/ACIF/2018/148 es_ES
dc.relation.ispartof Future Generation Computer Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject MapReduce es_ES
dc.subject Serverless es_ES
dc.subject Cloud computing es_ES
dc.subject Elasticity es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A framework and a performance assessment for serverless MapReduce on AWS Lambda es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.future.2019.02.057 es_ES
dc.rights.accessRights Embargado es_ES
dc.date.embargoEndDate 2021-09-01 es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular es_ES
dc.description.bibliographicCitation Giménez-Alventosa, V.; Moltó, G.; Caballer Fernández, M. (2019). A framework and a performance assessment for serverless MapReduce on AWS Lambda. Future Generation Computer Systems. 97:259-274. https://doi.org/10.1016/j.future.2019.02.057 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.future.2019.02.057 es_ES
dc.description.upvformatpinicio 259 es_ES
dc.description.upvformatpfin 274 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 97 es_ES
dc.relation.pasarela S\380178 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES


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