DAIET: a system for data aggregation inside the network

Handle URI:
http://hdl.handle.net/10754/626112
Title:
DAIET: a system for data aggregation inside the network
Authors:
Sapio, Amedeo; Abdelaziz, Ibrahim ( 0000-0003-1449-5115 ) ; Canini, Marco ( 0000-0002-5051-4283 ) ; Kalnis, Panos ( 0000-0002-5060-1360 )
Abstract:
Many data center applications nowadays rely on distributed computation models like MapReduce and Bulk Synchronous Parallel (BSP) for data-intensive computation at scale [4]. These models scale by leveraging the partition/aggregate pattern where data and computations are distributed across many worker servers, each performing part of the computation. A communication phase is needed each time workers need to synchronize the computation and, at last, to produce the final output. In these applications, the network communication costs can be one of the dominant scalability bottlenecks especially in case of multi-stage or iterative computations [1].
KAUST Department:
KAUST
Citation:
Sapio A, Abdelaziz I, Canini M, Kalnis P (2017) DAIET. Proceedings of the 2017 Symposium on Cloud Computing - SoCC ’17. Available: http://dx.doi.org/10.1145/3127479.3132018.
Publisher:
ACM Press
Journal:
Proceedings of the 2017 Symposium on Cloud Computing - SoCC '17
Conference/Event name:
2017 Symposium on Cloud Computing, SoCC 2017
Issue Date:
27-Sep-2017
DOI:
10.1145/3127479.3132018
Type:
Presentation
Additional Links:
https://dl.acm.org/citation.cfm?doid=3127479.3132018
Appears in Collections:
Presentations

Full metadata record

DC FieldValue Language
dc.contributor.authorSapio, Amedeoen
dc.contributor.authorAbdelaziz, Ibrahimen
dc.contributor.authorCanini, Marcoen
dc.contributor.authorKalnis, Panosen
dc.date.accessioned2017-11-06T07:09:04Z-
dc.date.available2017-11-06T07:09:04Z-
dc.date.issued2017-09-27en
dc.identifier.citationSapio A, Abdelaziz I, Canini M, Kalnis P (2017) DAIET. Proceedings of the 2017 Symposium on Cloud Computing - SoCC ’17. Available: http://dx.doi.org/10.1145/3127479.3132018.en
dc.identifier.doi10.1145/3127479.3132018en
dc.identifier.urihttp://hdl.handle.net/10754/626112-
dc.description.abstractMany data center applications nowadays rely on distributed computation models like MapReduce and Bulk Synchronous Parallel (BSP) for data-intensive computation at scale [4]. These models scale by leveraging the partition/aggregate pattern where data and computations are distributed across many worker servers, each performing part of the computation. A communication phase is needed each time workers need to synchronize the computation and, at last, to produce the final output. In these applications, the network communication costs can be one of the dominant scalability bottlenecks especially in case of multi-stage or iterative computations [1].en
dc.publisherACM Pressen
dc.relation.urlhttps://dl.acm.org/citation.cfm?doid=3127479.3132018en
dc.rightsArchived with thanks to Proceedings of the 2017 Symposium on Cloud Computing - SoCC '17en
dc.subjectIn-network processingen
dc.subjectOn-path aggregationen
dc.subjectP4en
dc.titleDAIET: a system for data aggregation inside the networken
dc.typePresentationen
dc.contributor.departmentKAUSTen
dc.identifier.journalProceedings of the 2017 Symposium on Cloud Computing - SoCC '17en
dc.conference.date2017-09-24 to 2017-09-27en
dc.conference.name2017 Symposium on Cloud Computing, SoCC 2017en
dc.conference.locationSanta Clara, CA, USAen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionPolitecnico di Torinoen
kaust.authorSapio, Amedeoen
kaust.authorAbdelaziz, Ibrahimen
kaust.authorCanini, Marcoen
kaust.authorKalnis, Panosen
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