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Deep Reinforcement Learning based QoS-aware Routing in Knowledge-defined networking

Quang Tran Anh Pham 1 Yassine Hadjadj-Aoul 1 Abdelkader Outtagarts 2
1 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS
IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES, Inria Rennes – Bretagne Atlantique
Abstract : Knowledge-Defined networking (KDN) is a concept that relies on Software-Defined networking (SDN) and Machine Learning (ML) in order to operate and optimize data networks. Thanks to SDN, a centralized path calculation can be deployed, thus enhancing the network utilization as well as Quality of Services (QoS). QoS-aware routing problem is a high complexity problem, especially when there are multiple flows coexisting in the same network. Deep Reinforcement Learning (DRL) is an emerging technique that is able to cope with such complex problem. Recent studies confirm the ability of DRL in solving complex routing problems; however, its performance in the network with QoS-sensitive flows has not been addressed. In this paper, we exploit a DRL agent with convolutional neural networks in the context of KDN in order to enhance the performance of QoS-aware routing. The obtained results demonstrate that the proposed approach is able to improve the performance of routing configurations significantly even in complex networks.
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Quang Tran Anh Pham, Yassine Hadjadj-Aoul, Abdelkader Outtagarts. Deep Reinforcement Learning based QoS-aware Routing in Knowledge-defined networking. Qshine 2018 - 14th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, Dec 2018, Ho Chi Minh City, Vietnam. pp.1-13. ⟨hal-01933970⟩

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