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Identifying HeavyHitter Flows from Sampled Flow Statistics
Tatsuya MORI Tetsuya TAKINE Jianping PAN Ryoichi KAWAHARA Masato UCHIDA Shigeki GOTO
Publication
IEICE TRANSACTIONS on Communications
Vol.E90B
No.11
pp.30613072 Publication Date: 2007/11/01
Online ISSN: 17451345
DOI: 10.1093/ietcom/e90b.11.3061
Print ISSN: 09168516 Type of Manuscript: Special Section PAPER (Special Section on Next Generation Network Management) Category: Keyword: network measurement, packet sampling, flow statistics, a priori distribution, Bayes' theorem,
Full Text: FreePDF
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 heavyhitter flows) from sampled flow statistics. Our approach follows a twostage strategy: We first parametrically estimate the original flow length distribution from sampled flows. We then identify heavyhitter 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.

