Investigating Self-Similarity and Heavy-Tailed Distributions on a Large-Scale Experimental Facility

Abstract--After the seminal work by Taqqu et al. relating selfsimilarity 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 ...

• Type: Journal
• Year: 2010
• Where: TON
• Authors: Patrick Loiseau, Paulo Gonçalves, Guillaume Dewaele, Pierre Borgnat, Patrice Abry, Pascale Vicat-Primet
• Keywords: Heavy-tailed Distributions, Prediction, Real Networks, TON 2010