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dc.contributor.authorGucciardo, Michele 
dc.contributor.authorAkem, Aristide Tanyi-Jong 
dc.contributor.authorBütün, Beyza 
dc.contributor.authorFiore, Marco 
dc.date.accessioned2023-03-29T15:03:35Z
dc.date.available2023-03-29T15:03:35Z
dc.date.issued2023-05-17
dc.identifier.urihttps://hdl.handle.net/20.500.12761/1678
dc.description.abstractExisting approaches for in-switch inference with Random Forest (RF) models that can run on production-level hardware do not support flow-level features and have limited scalability to the task size. This leads to performance barriers when tackling complex inference problems with sizable decision spaces. Flowrest is a complete RF model framework that fills existing gaps in the existing literature and enables practical flow-level inference in commercial programmable switches. In this demonstration, we exhibit how Flowrest can classify individual traffic flows at line rate in an experimental platform based on Intel Tofino switches. To this end, we run experiments with real-world measurement data, and show how Flowrest yields improvements in accuracy with respect to solutions that are limited to packet-level inference in programmable hardware.es
dc.description.sponsorshipEuropean Union Horizon 2020 research and innovation program under grant agreement no. 101017109 “DAEMON”es
dc.description.sponsorshipEuropean Union Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 860239 “BANYAN”es
dc.description.sponsorshipCHIST-ERA grant no. CHIST-ERA-20-SICT- 001 “ECOMOME”, via grant PCI2022-133013 of Agencia Estatal de Investigaciónes
dc.language.isoenges
dc.titleDemonstrating Flow-Level In-Switch Inferencees
dc.typeconference objectes
dc.conference.date17-20 May 2023es
dc.conference.placeNew York, United Stateses
dc.conference.titleIEEE International Conference on Computer Communications*
dc.event.typeconferencees
dc.pres.typedemoes
dc.type.hasVersionAMes
dc.rights.accessRightsopen accesses
dc.acronymINFOCOM*
dc.rankA**
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101017109/EU/Network intelligence for aDAptive and sElf-Learning MObile Networks/DAEMONes
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/860239/EU/Big dAta aNalYtics for radio Access Networks/BANYANes
dc.relation.projectIDces
dc.relation.projectNameBANYAN (Big dAta aNalYtics for radio Access Networks)es
dc.relation.projectNameDAEMON (Network intelligence for aDAptive and sElf-Learning MObile Networks)es
dc.relation.projectNameECOMOME (Energy COnsumption Measurements and Optimization in Mobile nEtworks)es
dc.description.refereedTRUEes
dc.description.statuspubes


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