Simulating a Poisson cluster process for Internet traffic packet arrivals

González-Arévalo, Bárbara; Roy, J

doi:10.1016/j.comcom.2009.11.004

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Renewal processes form a natural context for internet traffic and have previously been used extensively in traffic analysis (e.g. Pipiras, 2011, 2016;Antunes et al, 2017;González-Arévalo and Roy, 2010;Hohn et al, 2003;Hohn and Veitch, 2006;Williamson, 2001). However, Williamson (2001) argues that simple renewal processes are limited in their ability to jointly model packet-level burstiness and the interaction between flows.…”

Section: The Bartlett-lewis Traffic Modelmentioning

confidence: 99%

Likelihood-based inference for modelling packet transit from thinned flow summaries

Rahman¹,

Beranger²,

Roughan³

et al. 2020

Preprint

View full text Add to dashboard Cite

The substantial growth of network traffic speed and volume presents practical challenges to network data analysis. Packet thinning and flow aggregation protocols such as NetFlow reduce the size of datasets by providing structured data summaries, but conversely this impedes statistical inference. Methods which aim to model patterns of traffic propagation typically do not account for the packet thinning and summarisation process into the analysis, and are often simplistic, e.g. method-of-moments. As a result, they can be of limited practical use.We introduce a likelihood-based analysis which fully incorporates packet thinning and Net-Flow summarisation into the analysis. As a result, inferences can be made for models on the level of individual packets while only observing thinned flow summary information. We establish consistency of the resulting maximum likelihood estimator, derive bounds on the volume of traffic which should be observed to achieve required levels of estimator accuracy, and identify an ideal family of models. The robust performance of the estimator is examined through simulated analyses and an application on a publicly available trace dataset containing over 36m packets over a 1 minute period.

show abstract

Section: The Bartlett-lewis Traffic Modelmentioning

confidence: 99%

Likelihood-based inference for modelling packet transit from thinned flow summaries

Rahman¹,

Beranger²,

Roughan³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The motivating application in this work is the Internet traffic observed on a network link, where points are data packets and clusters are packet flows (essentially packetized document files, web pages, videos or other application contents). The use of PCPs in modeling data packet traffic was popularized by Hohn et al [18], see also Faÿ et al [11], Mikosch and Samorodnitsky [27], Fasen and Samorodnitsky [10], González-Arévalo and Roy [13], Antunes and Pipiras [3]. Models related to PCPs for modeling Internet traffic include the ON/OFF model (e.g.…”

Section: Introductionmentioning

confidence: 99%

Small and large scale behavior of moments of Poisson cluster processes

Antunes

Pipiras

Abry³

et al. 2017

ESAIM: PS

View full text Add to dashboard Cite

Poisson cluster processes are special point processes that find use in modeling Internet traffic, neural spike trains, computer failure times and other real-life phenomena. The focus of this work is on the various moments and cumulants of Poisson cluster processes, and specifically on their behavior at small and large scales. Under suitable assumptions motivated by the multiscale behavior of Internet traffic, it is shown that all these various quantities satisfy scale free (scaling) relations at both small and large scales. Only some of these relations turn out to carry information about salient model parameters of interest, and consequently can be used in the inference of the scaling behavior of Poisson cluster processes. At large scales, the derived results complement those available in the literature on the distributional convergence of normalized Poisson cluster processes, and also bring forward a more practical interpretation of the so-called slow and fast growth regimes. Finally, the results are applied to a real data trace from Internet traffic.

show abstract

Research on Cross-Connect Technology for Large-Capacity and High-Speed SDH Signal

Zuo

Zhang

et al. 2017

Advances in Computer and Computational Sciences

View full text Add to dashboard Cite

Simulating a Poisson cluster process for Internet traffic packet arrivals

Cited by 4 publications

References 10 publications

Likelihood-based inference for modelling packet transit from thinned flow summaries

Likelihood-based inference for modelling packet transit from thinned flow summaries

Small and large scale behavior of moments of Poisson cluster processes

Research on Cross-Connect Technology for Large-Capacity and High-Speed SDH Signal

Contact Info

Product

Resources

About