Machine Learning for Interconnect Network Traffic Forecasting: Investigation and Exploitation-Reference-Cited by-同舟云学术

Machine Learning for Interconnect Network Traffic Forecasting: Investigation and Exploitation

Published:2023-06-21 Issue: Volume: Page:
ISSN:
Container-title:ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
language:
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Author:

Xu Xiongxiao¹^ORCID,Wang Xin¹^ORCID,Cruz-Camacho Elkin²^ORCID,D. Carothers Christopher²^ORCID,A. Brown Kevin³^ORCID,B. Ross Robert³^ORCID,Lan Zhiling¹^ORCID,Shu Kai¹^ORCID

Affiliation:

1. Illinois Institute of Technology, USA

2. Rensselaer Polytechnic Institute, USA

3. Argonne National Laboratory, USA

Publisher

ACM

Reference44 articles.

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2. NeuTM: A neural network-based framework for traffic matrix prediction in SDN

3. ROSS: A high-performance, low-memory, modular Time Warp system

4. Manoel Castro-Neto , Young-Seon Jeong , Myong-Kee Jeong , and Lee D Han . 2009. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert systems with applications 36, 3 ( 2009 ), 6164–6173. Manoel Castro-Neto, Young-Seon Jeong, Myong-Kee Jeong, and Lee D Han. 2009. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert systems with applications 36, 3 (2009), 6164–6173.

5. Bike flow prediction with multi-graph convolutional networks

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