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Conference Papers Year : 2018

An efficient and lightweight load forecasting for proactive scaling in 5G mobile networks

Abstract

The number of connected devices is increasing with the emergence of new services and trends. This phenomenon is leading to a traffic growth over both the control and the data planes of the mobile core network. It is expected that the traffic will increase more and more with the installation of the new generation of mobile networking (5G) as it offers more services that are intended to be connected over the same network in addition to the legacy ones. Therefore the 3GPP group has rethought the architecture of the New Generation Core (NGC) by defining its components as Virtualized Network Functions (VNF). However, 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. Neural networks, and in particular deep learning, having shown their effectiveness in predicting time series, could be good candidates for predicting traffic evolution. In this paper, we proposed a novel solution to generalize neural networks while accelerating the learning process by using K-mean clustering, and a Monte-Carlo method. We benchmarked multiple types of deep neural networks using real operator's data in order to compare their efficiency in predicting the upcoming network load for dynamic and proactive resource provisioning. The proposed solution allow obtaining very good predictions of the traffic evolution while reducing by 50% the time needed for the learning phase.
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Dates and versions

hal-01933974 , version 1 (24-11-2018)

Identifiers

  • HAL Id : hal-01933974 , version 1

Cite

Imad Alawe, Yassine Hadjadj-Aoul, Adlen Ksentini, Philippe Bertin, César Viho, et al.. An efficient and lightweight load forecasting for proactive scaling in 5G mobile networks. CSCN 2018 - IEEE Conference on Standards for Communications and Networking, Oct 2018, Paris, France. pp.1-6. ⟨hal-01933974⟩
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