Proc. IEEE Int. Conf. on
June 20-24, 2004, vol. 4 pp. 2054-2059, Paris, France.
Inferring Path Sharing Based on Flow Level TCP Measurements
Gustavo de Veciana and
Brian L. Evans
Wireless Networking and Communications
Department of Electrical and
The University of Texas at Austin,
Austin, TX 78712-1084 USA
We develop methods to infer path or bottleneck
sharing among TCP flow classes based on flow level measurements
available from current traffic monitoring tools. Our
premise is that flows that temporally overlap on congested
resources will have correlated throughputs. We propose to use
factor analysis to explore the correlation structure of flow class
throughputs in order to hypothesize which flow classes might
share congested resources. The effectiveness of this black box
approach is studied using empirical data. We show that making
such inferences based on flow level statistics is viable in practice,
and can serve as an effective, novel tool for network design and
configuration decisions. Our work on inferring bottleneck sharing
differs significantly from previous work in that we consider flow
level instead of packet level statistics, and hence may potentially
influence research in that area. Possible applications of this
technique include network monitoring and root cause analysis
of poor performance.
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