| 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. |
