article

Providing public intradomain traffic matrices to the research community

Authors:

Simon BalonAuthors Info & Claims

ACM SIGCOMM Computer Communication Review, Volume 36, Issue 1

Pages 83 - 86

https://doi.org/10.1145/1111322.1111341

Published: 10 January 2006 Publication History

Abstract

This paper presents a new publicly available dataset from GÉANT, the European Research and Educational Network. This dataset consists of traffic matrices built using full IGP routing information, sampled Netflow data and BGP routing information of the GÉANT network, one per 15 minutes interval for several months. Potential benefits of publicly available traffic matrices comprise improving our understanding of real traffic matrices, their dynamics, and to make possible the benchmarking of intradomain traffic engineering methods

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Index Terms

Providing public intradomain traffic matrices to the research community
1. Networks

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Published In

cover image ACM SIGCOMM Computer Communication Review

ACM SIGCOMM Computer Communication Review Volume 36, Issue 1

January 2006

90 pages

ISSN:0146-4833

DOI:10.1145/1111322

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Copyright © 2006 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 January 2006

Published in SIGCOMM-CCR Volume 36, Issue 1

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Cited By

Ma YGuo YYang RLuo H(2025)FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDNJournal of Network and Computer Applications10.1016/j.jnca.2024.104054234(104054)Online publication date: Mar-2025
https://doi.org/10.1016/j.jnca.2024.104054
Lu JTang CChen ZGuo JZou AYang WTang C(2025)Intraflow temporal correlation-based network traffic predictionComputer Networks10.1016/j.comnet.2024.110913256(110913)Online publication date: Jan-2025
https://doi.org/10.1016/j.comnet.2024.110913
Gong XRen SWang CWang J(2024)Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First NetworkSensors10.3390/s2404108624:4(1086)Online publication date: 7-Feb-2024
https://doi.org/10.3390/s24041086
WANG YNAKACHI T(2024)Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse RepresentationIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.2022EAP1161E107.A:1(125-133)Online publication date: 1-Jan-2024
https://doi.org/10.1587/transfun.2022EAP1161
Miyata T(2024)Traffic matrix completion by weighted tensor nuclear norm minimization and time slicingNonlinear Theory and Its Applications, IEICE10.1587/nolta.15.31115:2(311-323)Online publication date: 2024
https://doi.org/10.1587/nolta.15.311
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Liu XZhao SCui YWang XSekar VYu MSeneviratne AVeitch D(2024)FIGRET: Fine-Grained Robustness-Enhanced Traffic EngineeringProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672258(117-135)Online publication date: 4-Aug-2024
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Dou SQi LGuo ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)ARES: Predictable Traffic Engineering under Controller Failures in SD-WANsProceedings of the ACM Web Conference 202410.1145/3589334.3645321(2703-2712)Online publication date: 13-May-2024
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