Document (#30156)

Author

Hinich, M.J.
Molyneux, R.E.

Title

Predicting information flows in network traffic

Source

Journal of the American Society for Information Science and technology. 54(2003) no.2, S.161-168

Year

2003

Abstract

Hinich and Molyneux review the literature of internet measurement and note three results consistently to be found in network traffic studies. These are "self-similarity," "long-range dependence," by which is meant that events in one time are correlated with events in a previous time and remain so through longer time periods than expected, and "heavy tails" by which they mean many small connections with low byte counts and a few long connections with large byte counts. The literature also suggests that conventional time series analysis is not helpful for network analysis. Using a single day's traffic at the Berkeley National Labs web server, cumulated TCP flows were collected, log transforms were used with the adding of .01 to all values allowing log transforms of the zero values, and providing a distribution that overcomes the heavy tail problem. However, Hinich's bicorrelation test for nonlinearity using overlapping moving windows found strong evidence of nonlinear structures. Time series analysis assumes linear systems theory and thus additivity and scalability. Spectral analysis should provide large peaks at the lowest frequencies if long range dependence is present since the power spectrum would go to infinity if the frequency goes to zero. This does not occur and so long range dependence must be questioned, at least until it is determined what effect other OSI layers may have on the TCP data.

Theme

Internet
Datenfernübertragung

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