Identifying Heavy-Hitter Flows from Sampled Flow Statistics

• DOI: 10.1093/ietcom/e90-b.11.3061
• Publication Date: 2007-11-10T03:59:42Z
• Authors: T. MORI; T. TAKINE; J. PAN; R. KAWAHARA; M. UCHIDA; S. GOTO
• Keywords: 0101 mathematics; 01 natural sciences

SUMMARY With the rapid increase of link speed in recent years, packet sampling has become a very attractive and scalable means in collecting flow statistics; however, it also makes inferring original flow characteristics much more difficult. In this paper, we develop techniques and schemes to identify flows with a very large number of packets (also known as heavy-hitter flows) from sampled flow statistics. Our approach follows a two-stage strategy: We first parametrically estimate the original flow length distribution from sampled flows. We then identify heavy-hitter flows with Bayes’ theorem, where the flow length distribution estimated at the first stage is used as an a priori distribution. Our approach is validated and evaluated with publicly available packet traces. We show that our approach provides a very flexible framework in striking an appropriate balance between false positives and false negatives when sampling frequency is given.