A Poisson Based Bursty Model of Internet Traffic

Kresch, Edward; Kulkarni, Sarvesh

doi:10.1109/cit.2011.95

Cited by 7 publications

(2 citation statements)

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“…Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks [2]. Internet traffic has been extensively analyzed in the past two decades [3], [4], [5], [6], [7] and, despite the community has not agreed on a universal model, there is consensus that in general aggregated traffic shows strong autocorrelation and a heavy-tail [8].…”

Section: Introductionmentioning

confidence: 99%

RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation

Ferriol-Galmés¹,

Rusek²,

Suárez‐Varela³

et al. 2022

Preprint

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Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to build data-driven models that can learn complex and non-linear behavior. In this paper, we present RouteNet-Erlang, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates in networks not seen in the training phase. We benchmark RouteNet-Erlang against a stateof-the-art QT model, and our results show that it outperforms QT in all the network scenarios.

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Section: Introductionmentioning

confidence: 99%

RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation

Ferriol-Galmés¹,

Rusek²,

Suárez‐Varela³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Such optimizations have been studied successfully when analyzing bit-errors in telecommunication systems (e.g. wireless systems) (Wilhelm, 1976;Wilhelm, 2018) as well as packet arrivals in Ethernet-based data networks (Kessler et al, 2000) or when analyzing the internet traffic (Kresch and Kulkarni, 2011;Zuckermann, Neame and Addie, 2003), where data packets arrive in bursts as well.…”

Section: Introductionmentioning

confidence: 99%