Providing public intradomain traffic matrices to the research community

By Steve Uhlig, B. Quoitin, Jean Lepropre, S. Balon. CCRV, 2006

537 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2b6ba82d6d9907e36a799e57afbd186eaf74b877

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Yin Zhang
2. Mingui Zhang
3. Beichuan Zhang
4. Tamara G. Kolda
5. Walter Willinger
6. Matthew Roughan
7. Cheng Yi
8. Xianfu Chen

In Electrical and Electronic Engineering

1. Yin Zhang
2. Mingui Zhang
3. Beichuan Zhang
4. Matthew Roughan
5. Cheng Yi
6. Xianfu Chen
7. Honggang Zhang
8. B. Liu

Citations in the corpus

• Deep Learning with Long Short-Term Memory for Time Series Prediction (2018, methodology): “We evaluate the model’s performance on traffic prediction depending upon real traffic data from a link in the GÉANT backbone networks [9].”
• Scalable Tensor Factorizations for Incomplete Data (2010, methodology): “For instance, G´eant data [36] records the traffic exchanged between 23 routers over a period of 4 months collected using 15-minute intervals.”
• Spatio-temporal compressive sensing and internet traffic matrices (2009, methodology): “The first two are the Abilene (Internet2) [1] dataset used previously in various studies [13,14,30], and the GÉANT TM dataset provided in [26], and previously examined in [2].”
• GreenTE: Power-aware traffic engineering (2010, methodology): “The non-anonymized topology and traffic matrices of G´EANT are provided by the authors of [15].”