Identifying heavy-hitter flows from sampled flow statistics

Tatsuya Mori, Tetsuya Takine, Jianping Pan, Ryoichi Kawahara, Masato Uchida, Shigeki Goto

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)3061-3072
Number of pages12
JournalIEICE Transactions on Communications
VolumeE90-B
Issue number11
DOIs
Publication statusPublished - 2007 Jan 1
Externally publishedYes

Keywords

  • A priori distribution
  • Bayes' theorem
  • Flow statistics
  • Network measurement
  • Packet sampling

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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