Title:
Building a Better Mousetrap
Building a Better Mousetrap
Author(s)
Ramachandran, Anirudh
Seetharaman, Srinivasan
Feamster, Nick
Vazirani, Vijay V.
Seetharaman, Srinivasan
Feamster, Nick
Vazirani, Vijay V.
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Abstract
Routers in the network core are unable to maintain detailed
statistics for every packet; thus, traffic statistics are often
based on packet sampling, which reduces accuracy. Because
tracking large ("heavy-hitter") traffic flows is important both
for pricing and for traffic engineering, much attention has
focused on maintaining accurate statistics for such flows, often
at the expense of small-volume flows. Eradicating these
smaller flows makes it difficult to observe communication
structure, which is sometimes more important than maintaining
statistics about flow sizes.
This paper presents FlexSample, a sampling framework
that allows network operators to get the best of both worlds:
For a fixed sampling budget, FlexSample can capture significantly
more small-volume flows for only a small increase in
relative error of large traffic flows. FlexSample uses a fast,
lightweight counter array that provides a coarse estimate of
the size ("class") of each traffic flow; a router then can sample
at different rates according to the class of the traffic using
any existing sampling strategy. Given a fixed sampling rate
and a target fraction of sampled packets to allocate across
traffic classes, FlexSample computes packet sampling rates
for each class that achieve these allocations online. Through
analysis and trace-based experiments, we find that FlexSample
captures at least 50% more mouse flows than strategies
that do not perform class-dependent packet sampling. We
also show how FlexSample can be used to capture unique
flows for specific applications.
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Date Issued
2007
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Text
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Technical Report