MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined Networks-Reference-Cited by-同舟云学术

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MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined Networks

Published:2024-11 Issue: Volume:231 Page:103981
ISSN:1084-8045
Container-title:Journal of Network and Computer Applications
language:en
Short-container-title:Journal of Network and Computer Applications

Author:

Guo Yingya^ORCID,Ding Mingjie,Zhou Weihong,Lin Bin,Chen Cen,Luo Huan

Publisher

Elsevier BV

Reference48 articles.

1. Traffic engineering in software defined networks;Agarwal,2013

2. Intra-domain traffic engineering with shortest path routing protocols;Altın;Ann. Oper. Res.,2013

3. Multi-agent based autonomic network management architecture;Arzo;IEEE Trans. Netw. Serv. Manag.,2021

4. The traffic matrices and topology of the abilene network;Balon,2019

5. Divide and conquer: Partitioning OSPF networks with SDN;Caria,2015

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