Understanding Patterns of TCP Connection Usage with Statistical Clustering

F. Hernandez-Campos, A.B. Nobel, F.D. Smith, and K. Jeffay
IEEE MASCOTS 2005 -- The 13th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Atlanta, GA, September 2005
pages 35-44.

ABSTRACT: We describe a new methodology for understanding how applications use TCP to exchange data. The method is useful for characterizing TCP workloads and synthetic traffic generation. Given a packet header trace, the method automatically constructs a source-level model of the applications using TCP in a network without any a priori knowledge of which applications are actually present in a network. From this source-level model, statistical feature vectors can be defined for each TCP connection in the trace. Hierarchical cluster analysis can then be performed to identify connections that are statistically homogeneous and that are likely exert similar demands on a network. We apply the methods to packet header traces taken from the UNC and Abilene networks and show how classes of similar connections can be automatically detected and modeled.

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