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Probabilistic Parallel Measurement of Network Traffic at Multiple Locations

Author(s): Alexander Marold, Peter Lieven, Björn Scheuermann.
Title: Probabilistic Parallel Measurement of Network Traffic at Multiple Locations
Published: IEEE Network (), 2011
Keyword(s):
Abstract: Measuring the per-flow traffic in large networks is very challenging due to the high performancerequirements. In addition to that, if traffic is measured at multiple points in the network at the sametime, it becomes necessary to merge the observations in order to obtain network-wide statistics. Whendoing so, packets must be accounted for only once, even if they traversed more than one measurementpoint. Today's standard technique, sampling-based traffic accounting, results in large approximationerrors. Here, we describe an approach named Distributed Probabilistic Counting (DPC). DPC is basedon a probabilistic data representation. It provides accurate traffic statistics at very low per-packet effort,and is able to merge measurement from multiple network locations while counting each distinct packetonly once.
Note: in press
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