Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning

Published:2023-05-17 Issue: Volume: Page:
ISSN:
Container-title:IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
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Author:

Lu Lie¹,Li Qing²,Zhao Dan²,Yang Yuan³,Luan Zeyu¹,Zhou Jianer²,Jiang Yong¹,Xu Mingwei³

Affiliation:

1. Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China

2. Peng Cheng Laboratory (PCL),Department of Mathematics and Theories,Shenzhen,China

3. Tsinghua University,Department of Computer Science and Technology,Beijing,China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Ministry of Education

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10228851/10228852/10228869.pdf?arnumber=10228869

Reference41 articles.

1. Online Multicast Traffic Engineering for Software-Defined Networks

2. Optimization of Multicast Source-Routing Based on Bloom Filter

3. Multicast traffic engineering for software-defined networks

4. Explicit Multicast (Xcast) Concepts and Options

5. A Multi-agent Reinforcement Learning Perspective on Distributed Traffic Engineering

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1. Achieving High Efficiency for Datacenter Multicast using Skewed Bloom Filter;Proceedings of the 53rd International Conference on Parallel Processing;2024-08-12

2. Solving the Annealing of Mo Interconnects for Next‐Gen Integrated Circuits;Advanced Electronic Materials;2024-06-24

3. Opti-DeepRoute: A Topology-Adaptive Deep Reinforcement Learning Based Service Provisioning Framework for Elastic Optical Network;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS);2024-05-20

4. Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission;Frontiers of Information Technology & Electronic Engineering;2024-04-18

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