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Analysis and Modeling of Traffic in Modern Data Communication Networks

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G. Babic, B. Vandalore, R. Jain

Networking and Internet Architecture · Submitted 23 Sep 1998

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We present results of data traffic analysis and simulated traffic, which demonstrates that the packet train model fits the traffic at source destination level and long-memory (self-similar) model fits the traffic at the aggregate level.

how it is stated · asserted 1.00 read the record →

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arXiv IDcs/9809056 (version 1)
Pith IDGKBO5NY6, Pith's permanent record of this paper and its review
CitationB. Vandalore, G. Babic, R. Jain. "Analysis and Modeling of Traffic in Modern Data Communication Networks." arXiv:cs/9809056 (1998). https://pith.science/paper/GKBO5NY6
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,493 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.10 · human participants 0.02 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.26 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In performance analysis and design of communication netword modeling data traffic is important. With introduction of new applications, the characteristics of the data traffic changes. We present a brief review the different models of data traffic and how they have evolved. We present results of data traffic analysis and simulated traffic, which demonstrates that the packet train model fits the traffic at source destination level and long-memory (self-similar) model fits the traffic at the aggregate level.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In performance analysis and design of communication netword modeling data traffic is important. background 1.00 0.05 0.02 asserted 1.00
s1 With introduction of new applications, the characteristics of the data traffic changes. background 1.00 0.06 0.02 asserted 1.00
s2 We present a brief review the different models of data traffic and how they have evolved. contribution 0.76 0.35 0.03 asserted 0.65
s3 We present results of data traffic analysis and simulated traffic, which demonstrates that the packet train model fits the traffic at source destination level and long-memory (self-similar) model fits the traffic at the aggregate level. result 0.99 0.92 0.87 asserted 1.00

What kind of paper the classifier reports this is: simulation study 0.75 · evidence from simulation or computation 0.98.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In performance analysis and design of communication netword modeling data traffic is important. With introduction of new applications, the characteristics of the data traffic changes. We present a brief review the different models of data traffic and how they have evolved. We present results of data traffic analysis and simulated traffic, which demonstrates that the packet train model fits the traffic at source destination level and long-memory (self-similar) model fits the traffic at the aggregate level.

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