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FaRNet: fast recognition of high multi-dimensional network traffic patterns

Autor
Paredes Oliva, Ignasi; Barlet, P.; Dimitropoulos, X.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
2013 Joint International Conference on Measurement and Modeling of Computer Systems
Any de l'edició
2013
Data de presentació
2013
Llibre d'actes
SIGMETRICS 2013: Proceedings of the 2013 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems: June 17–21, 2013: Pittsburgh, PA, USA
Pàgina inicial
355
Pàgina final
356
Editor
ACM
DOI
https://doi.org/10.1145/2494232.2465743 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/20134 Obrir en finestra nova
URL
http://dl.acm.org/citation.cfm?id=2465743 Obrir en finestra nova
Resum
Extracting knowledge from big network trac data is a matter of foremost importance for multiple purposes ranging from trend analysis or network troubleshooting to capacity planning or trac classication. An extremely useful approach to prole trac is to extract and display to a network administrator the multi-dimensional hierarchical heavy hitters (HHHs) of a dataset. However, existing schemes for computing HHHs have several limitations: 1) they require signicant computational overhead; 2) they do...
Citació
Paredes Oliva, Ignasi; Barlet, P.; Dimitropoulos, X. FaRNet: fast recognition of high multi-dimensional network traffic patterns. A: Joint International Conference on Measurement and Modeling of Computer Systems. "SIGMETRICS 2013: Proceedings of the 2013 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems: June 17–21, 2013: Pittsburgh, PA, USA". Pittsburgh, PA, USA: ACM, 2013, p. 355-356.
Paraules clau
Data Mining, Network Operation and Management, Traffic Profiling
Grup de recerca
CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla

Participants

  • Paredes Oliva, Ignasi  (autor ponent)
  • Barlet Ros, Pere  (autor ponent)
  • Dimitropoulos, Xenofontas  (autor ponent)