DRL-TAL: Deep Reinforcement Learning-Based Traffic-Aware Load Balancing in Data Center Networks-Reference-Cited by-同舟云学术

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DRL-TAL: Deep Reinforcement Learning-Based Traffic-Aware Load Balancing in Data Center Networks

Published:2023-12-04 Issue: Volume:10 Page:928-933
ISSN:
Container-title:GLOBECOM 2023 - 2023 IEEE Global Communications Conference
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Author:

Jiang Guoyong¹,Wei Wenting¹,Wang Kun²,Pang Chengding¹,Liu Yong³

Affiliation:

1. Xidian University,State Key Laboratory of Integrated Service Networks,Xi'an,China

2. School of Computer Science and Technology, Xidian University,Xi'an,China

3. School of Information Science and Technology, Hangzhou Normal University,Hangzhou,China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10436708/10436716/10437481.pdf?arnumber=10437481

Reference14 articles.

1. Adjusting Switching Granularity of Load Balancing for Heterogeneous Datacenter Traffic

2. A Receiver-Driven Transport Protocol with High Link Utilization Using Anti-ECN Marking in Data Center Networks

3. Load Balancing in Data Center Networks: A Survey

4. CONGA

5. Let It Flow: Resilient Asymmetric Load Balancing with Flowlet Switching;Vanini,2017

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