FaRNet: Fast recognition of high-dimensional patterns from big network traffic data
Extracting knowledge from big network traffic data is a matter of foremost importance for multiple purposes including trend analysis, network troubleshooting, capacity planning, network forensics, and traffic classification. An extremely useful approach to profile traffic 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 significant computational resources; (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 traffic data much more efficiently and scales to much higher dimensional data than present schemes. Based on generalized FIM, we build and thoroughly evaluate a traffic profiling 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. Finally, we describe experiences on how generalized FIM is useful in practice after using FaRNet operationally for several months in the NOC of GEANT, the European backbone network.