Marold, Alexander; Lieven, Peter; Scheuermann, Björn:
Distributed Probabilistic Network Traffic Measurements
In: 17th GI/ITG Conference on Communication in Distributed Systems / Luttenberger, Norbert; Peters, Hagen (Hrsg.). - KiVS 2011; Kiel, Germany; 8 - 11 March 2011 - Dagstuhl: Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2011 - (OpenAccess Series in Informatics (OASIcs) ; 17), S. 133 - 144
2011Buchaufsatz/Kapitel in TagungsbandOpen Access
MathematikForschungszentren » Institut für Experimentelle Mathematik (IEM) Essen
Titel in Englisch:
Distributed Probabilistic Network Traffic Measurements
Autor*in:
Marold, AlexanderUDE
LSF ID
52379
Sonstiges
der Hochschule zugeordnete*r Autor*in
;
Lieven, Peter
;
Scheuermann, Björn
Open Access?:
Open Access
Scopus ID
Sprache des Textes:
Englisch
Schlagwort, Thema:
network measurement; flow monitoring; probabilistic techniques

Abstract in Englisch:

Measuring the per-flow traffic in large networks is very challenging due to the high performance requirements on the one hand, and due to the necessity to merge locally recorded data from multiple routers in order to obtain network-wide statistics on the other hand. The latter is nontrivial because traffic that traversed more than one measurement point must only be counted once, which requires duplicate-insensitive distributed counting mechanisms. Sampling-based traffic accounting as implemented in today's routers results in large approximation errors, and does not allow for merging information from multiple points in the network into network-wide total traffic statistics. Here, we present Distributed Probabilistic Counting (DPC), an algorithm to obtain duplicate-insensitive distributed per-flow traffic statistics based on a probabilistic counting technique. DPC is structurally simple, very fast, and highly parallelizable, and therefore allows for efficient implementations in software and hardware. At the same time it provides very accurate traffic statistics, as we demonstrate based on both artificial and real-world traffic data. © Alexander Marold, Peter Lieven, and Björn Scheuermann.