Smart scaling of the 5G core network: an RNN-based approach - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2018

Smart scaling of the 5G core network: an RNN-based approach


The upcoming mobile core network, which will be based on Virtual Network Functions (VNF), will face an increase of data traffic on both data and control planes. This is due to the increase of the number of connected devices and the newly 5G supported-services like IoT, Connected Health Care etc. Therefore dynamic and accurate scalability techniques should be envisioned in order to answer the needs, in term of resource provisioning, without degrading the Quality Of Service (QoS) already offered by hardware based core networks. Although provisioning new resources is easier as it is a matter of software deployment, the strategy to use (when to scale and how much to scale) remains complex. In this paper we propose scaling techniques based on neural networks to forecast the upcoming load. Hence scheduling the resource provisioning should be in a manner that all the needed resources will be deployed and active when the load increases. In the same way, it will scale-in the unneeded resources when the traffic load decreases. The proposal is tested via discrete event simulations using a traffic load dataset provided by a Network Operator. The results show clearly the robustness of our proposal compared to a threshold-based scaling technique.
Fichier principal
Vignette du fichier
main.pdf (11.22 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01934057 , version 1 (25-11-2018)


  • HAL Id : hal-01934057 , version 1


Imad Alawe, Yassine Hadjadj-Aoul, Adlen Ksentini, Philippe Bertin, César Viho, et al.. Smart scaling of the 5G core network: an RNN-based approach. Globecom 2018 - IEEE Global Communications Conference, Dec 2018, Abu Dhabi, United Arab Emirates. pp.1-6. ⟨hal-01934057⟩
225 View
324 Download


Gmail Facebook X LinkedIn More