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On Using Deep Reinforcement Learning for VNF Forwarding Graphs Placement

Abstract : The placement of Virtual Network Function-Forwarding Graphs (VNF-FGs) is one of the fundamental operations in the next generation networks. However, embedding a VNF-FG is a complicated process which has been proved as an NP-hard problem. In this demo, we leverage one of the most advanced approaches in Deep Reinforcement Learning (DRL) in order to efficiently solve the VNF-FG embedding problem. In particular, we have proposed an extension of this approach in order to better explore the space of solutions while making it safer. The experimental platform we have developed, which is based on Mininet and containers, allows us to clearly show the advantage of our approach over existing approaches.
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Conference papers
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https://hal.inria.fr/hal-03130842
Contributor : Yassine Hadjadj Aoul Connect in order to contact the contributor
Submitted on : Wednesday, February 3, 2021 - 9:05:35 PM
Last modification on : Wednesday, November 3, 2021 - 8:14:25 AM

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Pham Tran Anh Quang, Yassine Hadjadj-Aoul, Abdelkader Outtagarts. On Using Deep Reinforcement Learning for VNF Forwarding Graphs Placement. NoF 2020 - 11th International Conference on Network of the Future, Oct 2020, Bordeaux, France. pp.126-128, ⟨10.1109/NoF50125.2020.9249090⟩. ⟨hal-03130842⟩

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