Multi-Agent Graph Convolutional Reinforcement Learning for Intelligent Load Balancing

Author:

Houidi Omar1,Bakri Sihem1,Zeghlache Djamal1

Affiliation:

1. Institut Mines-Telecom, Institut Polytechnique de Paris,Telecom SudParis, Samovar-Lab,France

Publisher

IEEE

Reference13 articles.

1. A scalable video coding dataset and toolchain for dynamic adaptive streaming over HTTP

2. The ns-3 Network Simulator

3. Amust framework-adaptive multimedia streaming simulation framework for ns-3 and ndnsim;kreuzberger,2016

4. Quality of Experience-based Routing of Video Traffic for Overlay and ISP Networks

5. Learning to Communicate with Deep Multi-Agent Reinforcement Learning;foerster;Advances in neural information processing systems,2016

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Improving the Traffic Engineering of SDN networks by using Local Multi-Agent Deep Reinforcement Learning;NOMS 2024-2024 IEEE Network Operations and Management Symposium;2024-05-06

2. Cloud-Native Computing: A Survey From the Perspective of Services;Proceedings of the IEEE;2024-01

3. Graph Convolutional Reinforcement Learning for Load Balancing and Smart Queuing;2023 IFIP Networking Conference (IFIP Networking);2023-06-12

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