Statistical modeling of the Internet traffic dynamics: To which extent do we need long-term correlations?

Oleg Markelov, Viet Nguyen Duc and Mikhail Bogachev

Physica A: Statistical Mechanics and its Applications, 2017, vol. 485, issue C, 48-60

Abstract: Recently we have suggested a universal superstatistical model of user access patterns and aggregated network traffic. The model takes into account the irregular character of end user access patterns on the web via the non-exponential distributions of the local access rates, but neglects the long-term correlations between these rates. While the model is accurate for quasi-stationary traffic records, its performance under highly variable and especially non-stationary access dynamics remains questionable. In this paper, using an example of the traffic patterns from a highly loaded network cluster hosting the website of the 1998 FIFA World Cup, we suggest a generalization of the previously suggested superstatistical model by introducing long-term correlations between access rates. Using queueing system simulations, we show explicitly that this generalization is essential for modeling network nodes with highly non-stationary access patterns, where neglecting long-term correlations leads to the underestimation of the empirical average sojourn time by several decades under high throughput utilization.

Keywords: Internet traffic; Long-term correlations; Superstatistics; q-exponential; Return intervals (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:485:y:2017:i:c:p:48-60

DOI: 10.1016/j.physa.2017.05.023

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