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Investigating self-similarity and heavy-tailed distributions on a large scale experimental facility

Author: Loiseau, Patrick and Gonçalves, Paulo and Dewaele, Guillaume and Borgnat, Pierre and Abry, Patrice and Vicat-Blanc Primet, Pascale
EntryType: article
Resotos: web
Journal: IEEE/ACM Transactions on Networking
Year: 2010
Month:
Note: to appear
X-editorial-board: yes
X-proceedings: no
X-international-audience: yes
Abstract: After the seminal work by Taqqu et al. relating self-similarity to heavy-tailed distributions, a number of research articles verified that aggregated Internet traffic time series show self-similarity and that Internet attributes, like Web file sizes and flow lengths, were heavy-tailed. However, the validation of the theoretical prediction relating self-similarity and heavy tails remains unsatisfactorily addressed, being investigated either using numerical or network simulations, or from uncontrolled Web traffic data. Notably, this prediction has never been conclusively verified on real networks using controlled and stationary scenarii, prescribing specific heavy-tailed distributions, and estimating confidence intervals. With this goal in mind, we use the potential and facilities offered by the large-scale, deeply reconfigurable and fully controllable experimental Grid5000 instrument, to investigate the prediction observability on real networks. To this end we organize a large number of controlled traffic circulation sessions on a nation-wide real network involving two hundred independent hosts. We use a FPGA-based measurement system, to collect the corresponding traffic at packet level. We then estimate both the self-similarity exponent of the aggregated time series and the heavy-tail index of flow size distributions, independently. On the one hand, our results complement and validate with a striking accuracy some conclusions drawn from a series of pioneer studies. On the other hand, they bring in new insights on the controversial role of certain components of real networks.

Bibtex:
@Article{Loiseau2010_TON,
 RESOTOS = "web",
author = {Loiseau, Patrick and Gonçalves, Paulo  and  Dewaele, Guillaume and  Borgnat, Pierre and  Abry, Patrice  and Vicat-Blanc Primet, Pascale},
title = {Investigating self-similarity and heavy-tailed distributions on a large scale experimental facility},
journal = {IEEE/ACM Transactions on Networking},
year = {2010},
month = jan,
note={to appear},
x-editorial-board = {yes} ,
x-proceedings = {no} ,
x-international-audience = {yes} ,
abstract = {After the seminal work by Taqqu et al. relating self-similarity to heavy-tailed distributions, a number of research articles verified that aggregated Internet traffic time series show self-similarity and that Internet attributes, like Web file sizes and flow lengths, were heavy-tailed.
However, the validation of the theoretical prediction relating self-similarity and heavy tails remains unsatisfactorily addressed, being investigated either using numerical or network simulations, or from uncontrolled Web traffic data. Notably, this prediction has never been conclusively verified on real networks using controlled and stationary scenarii, prescribing specific heavy-tailed distributions, and estimating confidence intervals.

With this goal in mind, we use the potential and facilities offered by the large-scale, deeply reconfigurable and fully controllable experimental Grid5000 instrument, to investigate the prediction observability on real networks.
To this end we organize a large number of controlled traffic circulation sessions on a nation-wide real network involving two hundred independent hosts. We use a FPGA-based measurement system, to collect the corresponding traffic at packet level. We then estimate both the self-similarity exponent of the aggregated time series and the heavy-tail index of flow size distributions, independently.
On the one hand, our results complement and validate with a striking accuracy some conclusions drawn from a series of pioneer studies.  On the other hand, they bring in new insights on the controversial role of certain components of real networks. }
} 

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Shared by: Paulo Goncalves, Patrick Loiseau, Pascale Primet
Last update: 2010-07-27 21:12:46
Publication #762

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