Publication:
Improving the predictability of distributed stream processors

Simple item page
dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)es
dc.contributor.authorBasanta Val, Pablo
dc.contributor.authorFernández García, Norberto
dc.contributor.authorWellings, A. J.
dc.contributor.authorAudsley, N. C.
dc.date.accessioned2018-02-23T08:22:30Z
dc.date.available2018-02-23T08:22:30Z
dc.date.issued2015-11-01
dc.description.abstractNext generation real-time applications demand big-data infrastructures to process huge and continuous data volumes under complex computational constraints. This type of application raises new issues on current big-data processing infrastructures. The first issue to be considered is that most of current infrastructures for big-data processing were defined for general purpose applications. Thus, they set aside real-time performance, which is in some cases an implicit requirement. A second important limitation is the lack of clear computational models that could be supported by current big-data frameworks. In an effort to reduce this gap, this article contributes along several lines. First, it provides a set of improvements to a computational model called distributed stream processing in order to formalize it as a real-time infrastructure. Second, it proposes some extensions to Storm, one of the most popular stream processors. These extensions are designed to gain an extra control over the resources used by the application in order to improve its predictability. Lastly, the article presents some empirical evidences on the performance that can be expected from this type of infrastructure.en
dc.description.sponsorshipThis work has been partially supported by HERMES (Healthy and Efficient Routes in Massive open-data basEd Smart cities). It has been also partially financed by Distributed Java Infrastructure for Real-Time Big Data (CAS14/00118). It has been also partially funded by eMadrid (S2013/ICE-2715) and by European Union’s 7th Framework Programme ​under Grant Agreement FP7-IC6-318763.en
dc.format.extent14
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationFuture Generation Computer Systems, (2015), v. 52, pp. 22-36
dc.identifier.doihttps://doi.org/10.1016/j.future.2015.03.023
dc.identifier.issn0167-739X
dc.identifier.publicationfirstpage22
dc.identifier.publicationlastpage36
dc.identifier.publicationtitleFuture Generations Computer Systemsen
dc.identifier.publicationvolume52
dc.identifier.urihttps://hdl.handle.net/10016/25432
dc.identifier.uxxiAR/0000017156
dc.language.isoeng
dc.publisherElsevier
dc.relation.projectIDGobierno de España. CAS14/00118
dc.relation.projectIDComunidad de Madrid. S2013/ICE-2715
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/FP7-IC6-318763
dc.rights© 2015 Elsevier B.V. All rights reserved
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaInformáticaes
dc.subject.otherReal-timeen
dc.subject.otherDistributed stream processingen
dc.subject.otherPredictable infrastructureen
dc.titleImproving the predictability of distributed stream processorsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
improving_FGCS_2015_ps.pdf
Size:
3.93 MB
Format:
Adobe Portable Document Format
Download
Collections
DIT - GAST - Artículos de Revistas
OpenAIRE: Open Access Infrastructure for Research in Europe