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

Author
Paredes Oliva, Ignasi; Barlet, P.; Dimitropoulos, X.
Type of activity
Presentation of work at congresses
Name of edition
2013 Joint International Conference on Measurement and Modeling of Computer Systems
Date of publication
2013
Presentation's date
2013
Book of congress proceedings
SIGMETRICS 2013: Proceedings of the 2013 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems: June 17–21, 2013: Pittsburgh, PA, USA
First page
355
Last page
356
Publisher
ACM
DOI
https://doi.org/10.1145/2494232.2465743 Open in new window
Repository
http://hdl.handle.net/2117/20134 Open in new window
URL
http://dl.acm.org/citation.cfm?id=2465743 Open in new window
Abstract
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...
Citation
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.
Keywords
Data Mining, Network Operation and Management, Traffic Profiling
Group of research
CBA - Communications and Broadband Architectures Lab

Participants

  • Paredes Oliva, Ignasi  (author and speaker )
  • Barlet Ros, Pere  (author and speaker )
  • Dimitropoulos, Xenofontas  (author and speaker )