| 1. |
- Guerin, Charles-Antoine, et al.
(author)
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Empirical testing of the infinite source poisson data traffice model
- 2003
-
In: Stochastic models. ; 19, s. 156-196
-
Journal article (peer-reviewed)abstract
- The infinite source Poisson model is a fluid queue approximation of network data transmission that assumes that sources begin constant rate transmissions of data at Poisson time points for random lengths of time. This model has been a popular one as analysts attempt to provide explanations for observed features in telecommunications data such as self-similarity, long range dependence and heavy tails. We survey some features of this model in cases where transmission length distributions have (a) tails so heavy that means are infinite, (b) heavy tails with finite mean and infinite variance and (c) finite variance. We survey the self-similarity properties of various descriptor processes in this model and then present analyses of four data sets which show that certain features of the model are consistent with the data while others are contradicted. The data sets are 1) the Boston University 1995 study of web sessions, 2) the UC Berkeley home IP HTTP data collected in November 1996, 3) traces collected in end of 1997 at a Customer Service Switch in Munich, and 4) detailed data from a corporate Ericsson WWW server from October 1998.
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| 2. |
- Guerin, Charles-Antoine, et al.
(author)
-
Empirical testing of the infinite source poisson data traffice model
- 2003
-
In: Stochastic models. ; 19, s. 156-196
-
Journal article (peer-reviewed)abstract
- The infinite source Poisson model is a fluid queueapproximation of network data transmission thatassumes that sources begin constant rate transmissions of data at Poissontime points for random lengths of time. This model has been a popularone as analysts attempt to provide explanations for observed featuresin telecommunications data such as self-similarity, long rangedependence and heavy tails. We survey some features of this modelin cases where transmission length distributions have (a) tails so heavythat means are infinite, (b) heavy tails with finite mean andinfinite variance and (c) finite variance. We survey theself-similarity properties of various descriptor processes in thismodel and then present analyses of four data sets which show thatcertain features of the model are consistent with the data whileothers are contradicted. The data sets are 1) the Boston University1995 study of web sessions, 2) the UC Berkeley home IP HTTPdata collected in November 1996, 3) traces collectedin end of 1997 at a Customer Service Switch in Munich, and 4) detailed data from a corporate Ericsson WWW serverfrom October 1998.
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