dc.contributor.author | Gucciardo, Michele | |
dc.contributor.author | Akem, Aristide Tanyi-Jong | |
dc.contributor.author | Bütün, Beyza | |
dc.contributor.author | Fiore, Marco | |
dc.date.accessioned | 2023-03-29T15:03:35Z | |
dc.date.available | 2023-03-29T15:03:35Z | |
dc.date.issued | 2023-05-17 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12761/1678 | |
dc.description.abstract | Existing 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.sponsorship | European Union Horizon 2020 research and innovation program under grant agreement no. 101017109 “DAEMON” | es |
dc.description.sponsorship | European Union Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 860239 “BANYAN” | es |
dc.description.sponsorship | CHIST-ERA grant no. CHIST-ERA-20-SICT- 001 “ECOMOME”, via grant PCI2022-133013 of Agencia Estatal de Investigación | es |
dc.language.iso | eng | es |
dc.title | Demonstrating Flow-Level In-Switch Inference | es |
dc.type | conference object | es |
dc.conference.date | 17-20 May 2023 | es |
dc.conference.place | New York, United States | es |
dc.conference.title | IEEE International Conference on Computer Communications | * |
dc.event.type | conference | es |
dc.pres.type | demo | es |
dc.type.hasVersion | AM | es |
dc.rights.accessRights | open access | es |
dc.acronym | INFOCOM | * |
dc.rank | A* | * |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/101017109/EU/Network intelligence for aDAptive and sElf-Learning MObile Networks/DAEMON | es |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/860239/EU/Big dAta aNalYtics for radio Access Networks/BANYAN | es |
dc.relation.projectID | c | es |
dc.relation.projectName | BANYAN (Big dAta aNalYtics for radio Access Networks) | es |
dc.relation.projectName | DAEMON (Network intelligence for aDAptive and sElf-Learning MObile Networks) | es |
dc.relation.projectName | ECOMOME (Energy COnsumption Measurements and Optimization in Mobile nEtworks) | es |
dc.description.refereed | TRUE | es |
dc.description.status | pub | es |