printlogo
ETH Zuerich - Homepage
Computer Engineering and Networks Laboratory (TIK)
 
ETH Zurich - ITET - TIK - Publications
print
  

Publication Details for Inproceedings "FaRNet: Fast Recognition of High Multi-Dimensional Network Traffic Patterns"

 

 Back

 New Search

 

Authors: Ignasi Paredes-Oliva, Pere Barlet-Ros, Xenofontas Dimitropou
Group: Communication Systems
Type: Inproceedings
Title: FaRNet: Fast Recognition of High Multi-Dimensional Network Traffic Patterns
Year: 2013
Month: June
Book Titel: ACM SIGMETRICS
Abstract: Extracting knowledge from big network trac data is a mat- ter of foremost importance for multiple purposes ranging from trend analysis or network troubleshooting to capac- ity planning or trac classication. An extremely useful approach to prole trac is to extract and display to a net- work 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 not scale to high dimensional data; and 3) they are not easily extensible. In this paper, we introduce a fundamentally new approach for extracting HHHs based on generalized frequent item-set mining (FIM), which allows to process trac data much more eciently and scales to much higher dimensional data than present schemes. Based on generalized FIM, we build and evaluate a trac proling system we call FaRNet. Our comparison with AutoFocus, which is the most related tool of similar nature, shows that FaRNet is up to three orders of magnitude faster.
Location: Pittsburgh, PA
Resources: [BibTeX] [Paper as PDF]

 

 Back

 New Search

top
© 2019 Laboratory TIK, ETH Zurich | Imprint | Last updated on Wed, 16 May, 2018 17:35 | t=0.0034s | Valid HTML 4.01