Algorithms and estimators for summarization of unaggregated data streams

Edith Cohen*, Nick Duffield, Haim Kaplan, Carstent Lund, Mikkel Thorup

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Statistical summaries of IP traffic are at the heart of network operation and are used to recover aggregate information on subpopulations of flows. It is therefore of great importance to collect the most accurate and informative summaries given the router's resource constraints. A summarization algorithm, such as Cisco's sampled NetFlow, is applied to IP packet streams that consist of multiple interleaving IP flows. We develop sampling algorithms and unbiased estimators which address sources of inefficiency in current methods. First, we design tunable algorithms whereas currently a single parameter (the sampling rate) controls utilization of both memory and processing/access speed (which means that it has to be set according to the bottleneck resource). Second, we make a better use of the memory hierarchy, which involves exporting partial summaries to slower storage during the measurement period.

Original languageEnglish
Pages (from-to)1214-1244
Number of pages31
JournalJournal of Computer and System Sciences
Volume80
Issue number7
DOIs
StatePublished - Nov 2014

Keywords

  • Data streams
  • Flow size distribution
  • IP flows
  • NetFlow
  • Random sampling
  • Subpopulation queries

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