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Reconfiguring Network Slices at the Best Time With Deep Reinforcement Learning

Adrien Gausseran 1 Redha A. Alliche 1, 2 Hicham Lesfari 1 Ramon Aparicio-Pardo 2, 1 Frédéric Giroire 1 Joanna Moulierac 1 
1 COATI - Combinatorics, Optimization and Algorithms for Telecommunications
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - COMRED - COMmunications, Réseaux, systèmes Embarqués et Distribués
Abstract : The emerging 5G induces a great diversity of use cases, a multiplication of the number of connections, an increase in throughput as well as stronger constraints in terms of quality of service such as low latency and isolation of requests. To support these new constraints, Network Function Virtualization (NFV) and Software Defined Network (SDN) technologies have been coupled to introduce the network slicing paradigm. Due to the high dynamicity of the demands, it is crucial to regularly reconfigure the network slices in order to maintain an efficient provisioning of the network. A major concern is to find the best frequency to carry out these reconfigurations, as there is a tradeoff between a reduced network congestion and the additional costs induced by the reconfiguration. In this paper, we tackle the problem of deciding the best moment to reconfigure by taking into account this trade-off. By coupling Deep Reinforcement Learning for decision and a Column Generation algorithm to compute the reconfiguration, we propose Deep-REC and show that choosing the best time during the day to reconfigure allows to maximize the profit of the network operator while minimizing the use of network resources and the congestion of the network. Moreover, by selecting the best moment to reconfigure, our approach allows to decrease the number of needed reconfigurations compared to an algorithm doing periodic reconfigurations during the day.
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Contributor : Frédéric Giroire Connect in order to contact the contributor
Submitted on : Friday, September 23, 2022 - 5:17:24 PM
Last modification on : Tuesday, October 25, 2022 - 4:25:11 PM


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  • HAL Id : hal-03786887, version 1


Adrien Gausseran, Redha A. Alliche, Hicham Lesfari, Ramon Aparicio-Pardo, Frédéric Giroire, et al.. Reconfiguring Network Slices at the Best Time With Deep Reinforcement Learning. CloudNet 2022 - IEEE International Conference on Cloud Networking, Nov 2022, Paris, France. ⟨hal-03786887⟩



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